Program description

Content

Core Qualification

Module M0718: Linear Algebra for Technomathematicians

Courses

Title	Typ	Hrs/wk	CP
Linear Algebra 1 for Technomathematicians (L0587)	Lecture	4	5
Linear Algebra 1 for Technomathematicians (L0588)	Recitation Section (small)	2	4
Linear Algebra 2 for Technomathematicians (L0589)	Lecture	4	5
Linear Algebra 2 for Technomathematicians (L0590)	Recitation Section (small)	2	4

Module Responsible	Prof. Sabine Le Borne
Admission Requirements	None
Recommended Previous Knowledge	High school mathematics
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students are able to define the basic terms of Linear Algebra, illustrate them with examples and detect interrelations, list techniques for proofs, sketch main steps in proofs of central theorems. Students can furthermore explain the basic steps that arise in modelling and relate them to application scenarios.
Skills	Students are capable to apply the tools of Linear Algebra, implement (MATLAB) and test algorithms (e.g. solution of linear systems of equations, computation of the determinant, computation of eigenvalues and eigenvectors), develop proofs for propositions in Linear Algebra and to document them in a comprehensible manner.
Personal Competence
Social Competence	Students are able to work together in heterogeneously composed teams (i.e., teams from different study programs and background knowledge), explain theoretical foundations and support each other with practical aspects regarding the implementation of algorithms, explain solutions/proofs of the excercises at the blackboard in a way suitable for the audience (in the excercise sessions).
Autonomy	Students are capable to assess whether the supporting theoretical and practical excercises are better solved individually or in a team, to work on complex problems over an extended period of time, to assess their individual progess and, if necessary, to ask questions and seek help.
Workload in Hours	Independent Study Time 372, Study Time in Lecture 168
Credit points	18
Course achievement	None
Examination	Written exam
Examination duration and scale	120 min
Assignment for the Following Curricula	Orientation Studies: Core Qualification: Elective Compulsory Technomathematics: Core Qualification: Compulsory

Course L0587: Linear Algebra 1 for Technomathematicians

Typ	Lecture
Hrs/wk	4
CP	5
Workload in Hours	Independent Study Time 94, Study Time in Lecture 56
Lecturer	Prof. Sabine Le Borne, Prof. Anusch Taraz
Language	DE
Cycle	WiSe
Content	Proof techniques, sets, relations, functions Groups and Fields Vector spaces Applications of vector spaces Linear mappings Polynomials Determinants
Literature	G. Fischer, Lineare Algebra: Eine Einführung für Studienanfänger A. Beutelspacher: Lineare Algebra: Eine Einführung in die Wissenschaft der Vektoren, Abbildungen und Matrizen J. Liesen, V. Mehrmann: Lineare Algebra: Ein Lehrbuch über die Theorie mit Blick auf die Praxis G. Strang: Introduction to Linear Algebra

Course L0588: Linear Algebra 1 for Technomathematicians

Typ	Recitation Section (small)
Hrs/wk	2
CP	4
Workload in Hours	Independent Study Time 92, Study Time in Lecture 28
Lecturer	Prof. Sabine Le Borne, Prof. Anusch Taraz
Language	DE
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Course L0589: Linear Algebra 2 for Technomathematicians

Typ	Lecture
Hrs/wk	4
CP	5
Workload in Hours	Independent Study Time 94, Study Time in Lecture 56
Lecturer	Prof. Sabine Le Borne, Prof. Anusch Taraz
Language	DE
Cycle	SoSe
Content	Eigenvalues Bilinear forms Singular value decomposition Tensor products Application: Linear ordinary differential equations
Literature	siehe Lineare Algebra 1 für Technomathematiker

Course L0590: Linear Algebra 2 for Technomathematicians

Typ	Recitation Section (small)
Hrs/wk	2
CP	4
Workload in Hours	Independent Study Time 92, Study Time in Lecture 28
Lecturer	Prof. Sabine Le Borne, Prof. Anusch Taraz
Language	DE
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Module M0690: Analysis for Technomathematicians

Courses

Title	Typ	Hrs/wk	CP
Analysis I for Technomathematicians (L0483)	Lecture	4	5
Analysis I for Technomathematicians (L0484)	Recitation Section (small)	2	4
Analysis II for Technomathematicians (L0485)	Lecture	4	5
Analysis II for Technomathematicians (L0486)	Recitation Section (small)	2	4

Module Responsible	Prof. Marko Lindner
Admission Requirements	None
Recommended Previous Knowledge	High school mathematics
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students are able to name, define and explain the basic properties of the field of real numbers, define and interrelate the basic topological terms in a metric space, in particular, describe their interrelation with the concepts of convergence and continuiuty, define, explain and use the basic terms of differential calculus in several veriables and integral calculus in one variable, In particular, they are able to correctly define, explain and interrelate all these concepts and to sketch the main ideas in proofs of central theorems. Students can furthermore explain the basic steps that arise in modelling and relate them to application scenarios.
Skills	Students are able to determine topological properties of concrete sets in metric space, determine and prove convergence and divergence of sequences and series - as well as continuity, uniform continuity and Lipschitz continuity of a given function between two metric spaces, differentiate a function in one or several variables, decide whether a given function is Riemann integrable and compute its integral, compute Taylor polynomial and Taylor series of a given, sufficiently smooth, function in one or more variables, find local and global extrema of a given function - possibly under constraints
Personal Competence
Social Competence	Students are able to solve specific problems in groups (e.g. in connection with their regular homework) and to present their results appropriately (e.g. during exercise class).
Autonomy	Students are able to gain further information from additional literature and put it in context with the contents of the lecture, put their knowledge in relation to the contents of other lectures, work on difficult problems over a long period.
Workload in Hours	Independent Study Time 372, Study Time in Lecture 168
Credit points	18
Course achievement	None
Examination	Oral exam
Examination duration and scale	30 min
Assignment for the Following Curricula	Orientation Studies: Core Qualification: Elective Compulsory Technomathematics: Core Qualification: Compulsory

Course L0483: Analysis I for Technomathematicians

Typ	Lecture
Hrs/wk	4
CP	5
Workload in Hours	Independent Study Time 94, Study Time in Lecture 56
Lecturer	Prof. Marko Lindner, Prof. Matthias Schulte, Prof. Sabine Le Borne
Language	DE
Cycle	WiSe
Content	logic, sets cardinalities numbers metric space and convergence continuity
Literature	K. Königsberger: Analysis I und II O. Forster: Analysis 1 und 2 H. Heuser: Lehrbuch der Analysis. Teile 1 und 2

Course L0484: Analysis I for Technomathematicians

Typ	Recitation Section (small)
Hrs/wk	2
CP	4
Workload in Hours	Independent Study Time 92, Study Time in Lecture 28
Lecturer	Prof. Marko Lindner, Prof. Matthias Schulte, Prof. Sabine Le Borne
Language	DE
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Course L0485: Analysis II for Technomathematicians

Typ	Lecture
Hrs/wk	4
CP	5
Workload in Hours	Independent Study Time 94, Study Time in Lecture 56
Lecturer	Prof. Marko Lindner, Prof. Sabine Le Borne
Language	DE
Cycle	SoSe
Content	differentiation in 1D integration in 1D sequences and series of functions differentiation in several variables
Literature	K. Königsberger: Analysis I und II O. Forster: Analysis 1 und 2 H. Heuser: Lehrbuch der Analysis. Teile 1 und 2

Course L0486: Analysis II for Technomathematicians

Typ	Recitation Section (small)
Hrs/wk	2
CP	4
Workload in Hours	Independent Study Time 92, Study Time in Lecture 28
Lecturer	Prof. Marko Lindner, Prof. Sabine Le Borne
Language	DE
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Module M1436: Procedural Programming for Computer Engineers

Courses

Title	Typ	Hrs/wk	CP
Procedural Programming for Computer Engineers (L2163)	Lecture	2	2
Procedural Programming for Computer Engineers (L2164)	Recitation Section (large)	1	1
Procedural Programming for Computer Engineers (L2165)	Practical Course	2	3

Module Responsible	Prof. Bernd-Christian Renner
Admission Requirements	None
Recommended Previous Knowledge	None
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students will know - the essential features of a procedural programming language - the steps during the compilation of procedural source code to machine code - all essential language constructs and data types of a procedural programming language - software design concepts for the implementation of procedural programs
Skills	- Mastery of typical development tools - Designing simple, structured programs based on a procedural programming language - Debugging by analyzing compiler warnings and error messages - Analysis and explanation of procedural programs
Personal Competence
Social Competence	- After completing the module, students are able to work on subject-specific tasks alone or in a group and to present the results appropriately.
Autonomy	- After completion of the module, students are able to work independently on parts of the subject area using reference books, to summarize the acquired knowledge, to present and to link it with the contents of other courses.
Workload in Hours	Independent Study Time 110, Study Time in Lecture 70
Credit points	6
Course achievement	None
Examination	Written exam
Examination duration and scale	120 min
Assignment for the Following Curricula	Computer Science: Core Qualification: Compulsory Data Science: Core Qualification: Compulsory Computer Science in Engineering: Core Qualification: Compulsory Orientation Studies: Core Qualification: Elective Compulsory Technomathematics: Core Qualification: Compulsory

Course L2163: Procedural Programming for Computer Engineers

Typ	Lecture
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Bernd-Christian Renner
Language	DE/EN
Cycle	WiSe
Content	- Development tools: preprocessor, compiler, linker, assembler, IDE, version management (Git) - Procedural programming: fundamental data types, operators, control structures, functions, pointers and arrays, scopes and lifetime of variables, structures / unions, function pointers, Command line arguments - Programming techniques: Modularization, separation of interface and implementation, callback functions, structured data types.
Literature	- Greg Perry and Dean Miller. C Programming Absolute Beginner's Guide: No experience necessary! Que Publishing; 3. Auflage (7. August 2013). ISBN 978-0789751980. - Helmut Erlenkötter. C: Programmieren von Anfang an. Rowohlt Taschenbuch; 25. Auflage (1. Dezember 1999). ISBN 978-3499600746. - Markus Neumann. C Programmieren: für Einsteiger: Der leichte Weg zum C-Experten (Einfach Programmieren lernen, Band 8). BMU Verlag (30. Januar 2020). ISBN ‎ 978-3966450607. - Brian W. Kernighan, Dennis M. Ritchie: The C Programming Language. Prentice Hall; 2. Auflage (1988), ISBN 0-13-110362-8.

Course L2164: Procedural Programming for Computer Engineers

Typ	Recitation Section (large)
Hrs/wk	1
CP	1
Workload in Hours	Independent Study Time 16, Study Time in Lecture 14
Lecturer	Prof. Bernd-Christian Renner
Language	DE/EN
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Course L2165: Procedural Programming for Computer Engineers

Typ	Practical Course
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Bernd-Christian Renner
Language	DE/EN
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Module M1847: Introduction to Mechanics (Technomathematics)

Courses

Title	Typ	Hrs/wk	CP
Introduction to Mechanics (Technomathematics) (L3058)	Lecture	3	4
Introduction to Mechanics (Technomathematics) (L3059)	Recitation Section (small)	2	2

Module Responsible	Prof. Christian Kautz
Admission Requirements	None
Recommended Previous Knowledge	Knowledge in Physics (upper-level secondary school)
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students know and understand the basic concepts and relationships that are used to describe and analyze mechanical Systems in static, elastically deformed, as well as simple dynamic situations. Students apply these concepts and relationships to simple example systems.
Skills	Students use different representations for the description of mechanical systems and explain their representation in mathematical form. They describe typical patterns and compare and contrast those. Students calculate physical quantities on the basis of given data. Students consider limiting cases of mechanical situations and analyze the relevant physical quantities and units in order to arrive at general conclusions.
Personal Competence
Social Competence	Students work in teams, describe technical arrangements and carry out professional discussions.
Autonomy	Students use recommended texts to study technical content on their own and critically examine their own understanding of the material. They pose questions with the aim of closing possible gaps in their understanding. Students search the literature concerning special topics and summarize their results in written form.
Workload in Hours	Independent Study Time 110, Study Time in Lecture 70
Credit points	6
Course achievement	None
Examination	Subject theoretical and practical work
Examination duration and scale	Online-Tests, Exercises, short oral exam, short project
Assignment for the Following Curricula	Technomathematics: Core Qualification: Compulsory

Course L3058: Introduction to Mechanics (Technomathematics)

Typ	Lecture
Hrs/wk	3
CP	4
Workload in Hours	Independent Study Time 78, Study Time in Lecture 42
Lecturer	Prof. Christian Kautz
Language	DE
Cycle	WiSe
Content	Core content of statics: Forces and moments, free-body diagrams, equilibrium (Newton’s 2^nd law), action and reaction (Newton’s 3^rd law) Equivalence of force/moment systems, supports, internal forces) Basic of elastostatics (mechanics of materials): Stresses, strains, stress-strain relationships in tension/compression or torsion or bending Brief glance at kinematics and dynamics (only translatory motion): Operational definitions of kinematic quantities, Newton’s laws, forces in accelerated situations, work and kinetic energy
Literature	Selected chapters from: Gross, Hauger, Schröder, Wall: Mechanik, Bd. 1 (Statik) Bd. 2 (Elastostatik) Bd. 3 (Kinetik) or corresponding sections from C. Hibbeler, Mechanics, or corresponding sections from C. Spura, Technische Mechanik

Course L3059: Introduction to Mechanics (Technomathematics)

Typ	Recitation Section (small)
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Christian Kautz
Language	DE
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Module M1755: Linking theory and practice (dual study program, Bachelor's degree)

Module Responsible	Dr. Henning Haschke
Admission Requirements	None
Recommended Previous Knowledge	none
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Dual students… … can describe and classify selected classic and modern theories, concepts and methods related to self-management, and organising work and learning self-competence and social skills ... and apply them to specific situations, projects and plans in a personal and professional context.
Skills	Dual students… ... anticipate typical difficulties, positive and negative effects, as well as success and failure factors in the engineering sector, evaluate them and consider promising strategies and courses of action.
Personal Competence
Social Competence	Dual students… … work together in a problem-oriented and interdisciplinary manner as part of expert and work teams. … are able to assemble and lead working groups. … present complex, subject-related solutions to problems to experts and stakeholders and can develop these further together.
Autonomy	Dual students… … define, reflect and evaluate goals for learning and work processes. … design their learning and work processes independently and sustainably at the university and company. … take responsibility for their learning and work processes. … are able to consciously think through their ideas or actions and relate them to their self-image to develop conclusions for future action based on this.
Workload in Hours	Independent Study Time 96, Study Time in Lecture 84
Credit points	6
Course achievement	None
Examination	Written elaboration
Examination duration and scale	Studienbegleitende und semesterübergreifende Dokumentation: Die Leistungspunkte für das Modul werden durch die Anfertigung eines digitalen Lern- und Entwicklungsberichtes (E-Portfolio) erworben. Dabei handelt es sich um eine fortlaufende Dokumentation und Reflexion der Lernerfahrungen und der Kompetenzentwicklung im Bereich der Personalen Kompetenz.

Course L2885: Self-Competence for Professional Success in Engineering (for Dual Study Program)

Typ	Seminar
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Dr. Henning Haschke, Heiko Sieben
Language	DE
Cycle	WiSe/SoSe
Content	Key qualifications for professional success Personality and self-image Personality profiles Emotional competence Needs structure models Motivation theories and models Communication basics, communication problems Conflict management Constructive communication and language cultures Resilience Transfer skills and (self-)reflection Intercultural competence and business etiquette Documenting and reflecting on learning experiences
Literature	Seminarapparat

Course L2884: Self-Management, Organising Work and Learning in Engineering (for Dual Study Program)

Typ	Seminar
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Dr. Henning Haschke, Heiko Sieben
Language	DE
Cycle	WiSe/SoSe
Content	Learning to learn Instruments and methods for time and self-management Personality and work style/behaviour (DISC model); inner drivers/motivation Goal setting and planning techniques (SMART, GROW); for short-, medium- and long-term planning Creativity techniques Stress management, resilience (Self-)reflection throughout the learning and work process Structuring/connecting learning and work processes within different learning environments Factors influencing learning transfer/transfer skills Documenting and reflecting on learning experiences
Literature	Seminarapparat

Course L2886: Social-Competence: Team Development and Communication in Engineering (for Dual Study Program)

Typ	Seminar
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Dr. Henning Haschke, Heiko Sieben
Language	DE
Cycle	WiSe/SoSe
Content	Forms, conditions and processes of working groups and leadership relationships Social skills: theories and models Communication and discussion techniques Empathy and motivation in teamwork, the way teams work Critical ability Team development: ways of developing working and project groups Insights into day-to-day leadership: theories and models, leadership tasks, leadership styles, situational leadership, basics of change management Documenting and reflecting on learning experiences
Literature	Seminarapparat

Module M1750: Practical module 1 (dual study program, Bachelor's degree)

Courses

Title	Typ	Hrs/wk	CP
Practical term 1 (dual study program, Bachelor's degree) (L2879)		0	6

Module Responsible	Dr. Henning Haschke
Admission Requirements	None
Recommended Previous Knowledge	A: Self-management, organising work and learning in engineering (for dual study program)
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Dual students… … describe their employer’s organisation (company) and the associated regulations that relate to how tasks and competences are distributed, as well as how work processes are handled. … understand the structure and objectives of the dual study programme and the increasing requirements throughout the course of study.
Skills	Dual students… … use equipment and resources professionally in accordance with the assigned work areas and tasks, and describe operational processes and procedures with regard to the intended work results/objectives. … implement the university’s application recommendations in relation to their current tasks.
Personal Competence
Social Competence	Dual students… … have familiarised themselves with their new working environment (learning environment) and the associated tasks/processes/working relationships. … know their central points of contact and company colleagues, and exchange ideas with them constructively. … coordinate work tasks with their professional supervisor and ask for support as needed. … help shape the work in the assigned work area and offer their colleagues support to complete their work. … work together with others in smaller work teams in a result-oriented manner.
Autonomy	Dual students… … structure their work and learning processes within the company independently in line with their responsibilities and authorisations, and coordinate them with their professional supervisor. … complete work tasks/assignments with the support of colleagues. … coordinate the practical phase with any individual preparation required for the examination phase at TUHH. … document and reflect on how their foundational subjects link with their work as an engineer.
Workload in Hours	Independent Study Time 180, Study Time in Lecture 0
Credit points	6
Course achievement	None
Examination	Written elaboration
Examination duration and scale	Documentation accompanying studies and across semesters: Module credit points are earned by completing a digital learning and development report (e-portfolio). This documents and reflects individual learning experiences and skills development relating to interlinking theory and practice, as well as professional practice. In addition, the partner company provides proof to the dual@TUHH Coordination Office that the dual student has completed the practical phase.
Assignment for the Following Curricula	General Engineering Science (German program, 7 semester): Core Qualification: Compulsory Civil- and Environmental Engineering: Core Qualification: Compulsory Chemical and Bioprocess Engineering: Core Qualification: Compulsory Computer Science: Core Qualification: Compulsory Data Science: Core Qualification: Compulsory Electrical Engineering: Core Qualification: Compulsory Engineering Science: Core Qualification: Compulsory Green Technologies: Energy, Water, Climate: Core Qualification: Compulsory Computer Science in Engineering: Core Qualification: Compulsory Mechanical Engineering: Core Qualification: Compulsory Mechatronics: Core Qualification: Compulsory Naval Architecture: Core Qualification: Compulsory Technomathematics: Core Qualification: Compulsory Engineering and Management - Major in Logistics and Mobility: Core Qualification: Compulsory

Course L2879: Practical term 1 (dual study program, Bachelor's degree)

Typ
Hrs/wk	0
CP	6
Workload in Hours	Independent Study Time 180, Study Time in Lecture 0
Lecturer	Dr. Henning Haschke
Language	DE
Cycle	WiSe
Content	Company onboarding process Assigning initial work areas (supervisor, colleagues) Assigning a contact person within the company (usually the HR department) Assigning a professional mentor in the work area (relating to practical application) Responsibilities and authorisations of the dual student within the company Supporting/working with colleagues Scheduling the relevant practical modules with initial work tasks Theory/practice transfer options Scheduling the examination phase/subsequent study semester Operational knowledge and skills Company-specific: organisational structure, corporate strategy, business and work areas, work procedures and processes, operational levels Process and procedure options within the labour-market-relevant field of engineering Operational equipment and resources Implementing the university’s application recommendations (theory-practice transfer) in corresponding work and task areas across the company Sharing/reflecting on learning Creating an e-portfolio Relevance of foundational subjects when working as an engineer Comparing the learning and working processes of different learning environments with regard to their results and effects
Literature	Studierendenhandbuch Betriebliche Dokumente Hochschulseitige Anwendungsempfehlungen zum Theorie-Praxis-Transfer

Module M1519: Introduction to Electrical Engineering (Technomathematics)

Courses

Title	Typ	Hrs/wk	CP
Introduction to Electrical Engineering (Technomathematics) (L2292)	Lecture	3	4
Introduction to Electrical Engineering (Technomathematics) (L2293)	Recitation Section (small)	2	2

Module Responsible	Prof. Christian Kautz
Admission Requirements	None
Recommended Previous Knowledge	Knowledge in Physics (upper-level secondary school)
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students know and understand the basic concepts and relationships for electric circuits (DC and AC) and apply these to simple example systems. Students know and understand the basic concepts and relationships for electric and magnetic interactions and apply these to simple example systems.
Skills	Students use different representations for the description of electrical systems (circuits and fields) and explain their representation in mathematical form. They describe typical patterns and compare and contrast those. Students calculate physical quantities on the basis of given data.
Personal Competence
Social Competence	Students work in teams, describe technical circumstances and carry out professional discussions.
Autonomy	Students use recommended texts to study technical content on their own and critically examine their own understanding of the material
Workload in Hours	Independent Study Time 110, Study Time in Lecture 70
Credit points	6
Course achievement	None
Examination	Subject theoretical and practical work
Examination duration and scale	online exercises, short presentation, presence exercise, short oral exam
Assignment for the Following Curricula	Data Science: Specialisation II. Application: Elective Compulsory Technomathematics: Core Qualification: Compulsory

Course L2292: Introduction to Electrical Engineering (Technomathematics)

Typ	Lecture
Hrs/wk	3
CP	4
Workload in Hours	Independent Study Time 78, Study Time in Lecture 42
Lecturer	Prof. Christian Kautz
Language	DE
Cycle	SoSe
Content	Electric charge, current, resistance, voltage, potential and power Kirchhoff's laws and Ohm's law Equivalent sources and load lines Circuit elements in AC systems complex-valued signals and phase relationships Gauss' law of electrostatics and capacitance Magnetic interactions and induction Energy transport and electromagnetic waves
Literature	W. Nerreter, Grundlagen der Elektrotechnik, 3. Auflage, 2020. (Online unter: https://www.hanser-elibrary.com/isbn/9783446465855 - aus dem Netz der TUHH oder über VPN) M. Albach, Elektrotechnik, 2. Auflage, 2020. (Online unter: https://elibrary.pearson.de/book/view/99.150005/9783863268947? - aus dem Netz der TUHH oder über VPN)

Course L2293: Introduction to Electrical Engineering (Technomathematics)

Typ	Recitation Section (small)
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Christian Kautz
Language	DE
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Module M1432: Programming Paradigms

Courses

Title	Typ	Hrs/wk	CP
Programming Paradigms (L2169)	Lecture	2	2
Programming Paradigms (L2170)	Recitation Section (large)	1	1
Programming Paradigms (L2171)	Practical Course	2	3

Module Responsible	NN
Admission Requirements	None
Recommended Previous Knowledge	Lecture on procedural programming or equivalent programming skills
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	The students have a fundamental understanding of object orientated and generic programming and can apply it in small programming projects. The can design own class hierarchies and differentiate between different ways of inheritance. They have a fundamental understanding of polymorphism and can differentiate between run-time and compile-time polymorphism. The students know the concept of information hiding and can design interfaces with public and private methods. They can use exceptions and apply generic programming in order to make existing data structures generic. The students know the pros and cons of both programming paradigms.
Skills	Students can break down a medium-sized problem into subproblems and create their own classes in an object-oriented programming language based on these subproblems. They can design a public and private interface and implement the implementation generically and extensible by abstraction. They can distinguish different language constructs of a modern programming language and use these suitably in the implementation. They can design and implement unit tests.
Personal Competence
Social Competence	Students can work in teams and communicate in forums.
Autonomy	In a programming internship, students learn object-oriented programming under supervision. In exercises they develop individual and independent solutions and receive feedback.
Workload in Hours	Independent Study Time 110, Study Time in Lecture 70
Credit points	6
Course achievement	None
Examination	Written exam
Examination duration and scale	90 min
Assignment for the Following Curricula	Computer Science: Core Qualification: Compulsory Data Science: Core Qualification: Compulsory Computer Science in Engineering: Core Qualification: Compulsory Orientation Studies: Core Qualification: Elective Compulsory Technomathematics: Core Qualification: Compulsory

Course L2169: Programming Paradigms

Typ	Lecture
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Dozenten des SD E
Language	DE/EN
Cycle	SoSe
Content	fundamentals behind object orientated programming classes and objects inheritance (single, multiple) interfaces information hiding exception handling generic programming and the implementation in the compiler excursus in programming with dynamically typed programming languages
Literature	Skript

Course L2170: Programming Paradigms

Typ	Recitation Section (large)
Hrs/wk	1
CP	1
Workload in Hours	Independent Study Time 16, Study Time in Lecture 14
Lecturer	Dozenten des SD E
Language	DE/EN
Cycle	SoSe
Content	fundamentals behind object orientated programming classes and objects inheritance (single, multiple) interfaces information hiding exception handling generic programming and the implementation in the compiler excursus in programming with dynamically typed programming languages
Literature	Skript

Course L2171: Programming Paradigms

Typ	Practical Course
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Dozenten des SD E
Language	DE/EN
Cycle	SoSe
Content	fundamentals behind object orientated programming classes and objects inheritance (single, multiple) interfaces information hiding exception handling generic programming and the implementation in the compiler excursus in programming with dynamically typed programming languages
Literature	Skript

Module M1751: Practical module 2 (dual study program, Bachelor's degree)

Courses

Title	Typ	Hrs/wk	CP
Practical term 2 (dual study program, Bachelor's degree) (L2880)		0	6

Module Responsible	Dr. Henning Haschke
Admission Requirements	None
Recommended Previous Knowledge	Successful completion of practical module 1 as part of the dual Bachelor’s course course A from the module on interlinking theory and practice as part of the dual Bachelor’s course
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Dual students … … describe their employer’s organisational structure (company) and differentiate between associated regulations that relate to how tasks and competences are distributed, as well as how work processes are handled. … understand the structure and objectives of the dual study programme and the increasing requirements throughout the course of study.
Skills	Dual students … … use equipment and resources professionally in accordance with the assigned work areas and tasks, and assess operational processes and procedures with regard to the intended work results/objectives. … implement the university’s application recommendations in relation to their current tasks.
Personal Competence
Social Competence	Dual students … … have familiarised themselves with their new working environment (learning environment) and the associated tasks/processes/working relationships. … know their central points of contact and colleagues, and are integrated into the designated tasks and work areas. … coordinate work tasks with their professional supervisor and justify procedures and intended results. … help shape the work in the assigned work area and offer their colleagues support to complete their work or ask for support based on their needs. … work together with others in interdisciplinary work teams in a result-oriented manner.
Autonomy	Dual students … … structure their work and learning processes within the company independently in line with their responsibilities and authorisations, and coordinate them with their professional supervisor. … complete work tasks/assignments independently and/or with the support of colleagues. … coordinate the practical phase with any individual preparation required for the examination phase at TUHH. … document and reflect on how their foundational subjects link with their work as an engineer.
Workload in Hours	Independent Study Time 180, Study Time in Lecture 0
Credit points	6
Course achievement	None
Examination	Written elaboration
Examination duration and scale	Documentation accompanying studies and across semesters: Module credit points are earned by completing a digital learning and development report (e-portfolio). This documents and reflects individual learning experiences and skills development relating to interlinking theory and practice, as well as professional practice. In addition, the partner company provides proof to the dual@TUHH Coordination Office that the dual student has completed the practical phase.
Assignment for the Following Curricula	General Engineering Science (German program, 7 semester): Core Qualification: Compulsory Civil- and Environmental Engineering: Core Qualification: Compulsory Chemical and Bioprocess Engineering: Core Qualification: Compulsory Computer Science: Core Qualification: Compulsory Data Science: Core Qualification: Compulsory Electrical Engineering: Core Qualification: Compulsory Engineering Science: Core Qualification: Compulsory Green Technologies: Energy, Water, Climate: Core Qualification: Compulsory Computer Science in Engineering: Core Qualification: Compulsory Mechanical Engineering: Core Qualification: Compulsory Mechatronics: Core Qualification: Compulsory Naval Architecture: Core Qualification: Compulsory Technomathematics: Core Qualification: Compulsory Engineering and Management - Major in Logistics and Mobility: Core Qualification: Compulsory

Course L2880: Practical term 2 (dual study program, Bachelor's degree)

Typ
Hrs/wk	0
CP	6
Workload in Hours	Independent Study Time 180, Study Time in Lecture 0
Lecturer	Dr. Henning Haschke
Language	DE
Cycle	SoSe
Content	Company onboarding process Assigning work areas (supervisor, colleagues) Assigning a contact person within the company (usually the HR department) Assigning a professional mentor in the work area (relating to practical application) Responsibilities and authorisations of the dual student within the company Supporting/working with colleagues Scheduling the relevant practical modules with work tasks Theory/practice transfer options Scheduling the examination phase/subsequent study semester Operational knowledge and skills Company-specific: organisational structure, corporate strategy, business and work areas, work procedures and processes, operational levels Process and procedure options within the labour-market-relevant field of engineering Operational equipment and resources Implementing the university’s application recommendations (theory-practice transfer) in corresponding work and task areas across the company Sharing/reflecting on learning Creating an e-portfolio Relevance of foundational subjects when working as an engineer Comparing the learning and working processes of different learning environments with regard to their results and effects
Literature	Studierendenhandbuch Betriebliche Dokumente Hochschulseitige Anwendungsempfehlungen zum Theorie-Praxis-Transfer

Module M1113: Proseminar Technomathematics

Courses

Title	Typ	Hrs/wk	CP
Proseminar Mathematics (L0919)	Seminar	2	2

Module Responsible	Prof. Anusch Taraz
Admission Requirements	None
Recommended Previous Knowledge	Analysis & Linear Algebra I + II for Technomathematicians or Mathematik I + II (for Engineering Students - German or English lecture series), and an advanced course by the lecturer who is responsible for the proseminar
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students acquire a deep understanding of the mathematical subject under consideration.
Skills	Students are able to understand, analyze, classify and work on an advanced mathematical topic, thoroughly study the recommended literature, present their results in a mathematically correct and comprehensible way.
Personal Competence
Social Competence	Students are able to present their results in an appropriate way to the group.
Autonomy	Students are able to prepare a written scientific presentation on their own; in particular to find and critically check relevant literature, make and incorporate their own thoughts, complete the presentation in time.
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Credit points	2
Course achievement	None
Examination	Presentation
Examination duration and scale	60 Minutes
Assignment for the Following Curricula	Technomathematics: Core Qualification: Compulsory

Course L0919: Proseminar Mathematics

Typ	Seminar
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Anusch Taraz, Dozenten des Fachbereiches Mathematik der UHH, Dr. Christian Seifert, Dr. Haibo Ruan, Dr. Julian Peter Großmann, Dr. Mijail Guillemard, Prof. Heinrich Voß, Prof. Marko Lindner, Prof. Sabine Le Borne
Language	DE
Cycle	WiSe/SoSe
Content	Selected topics from the fields Applied Analysis Numerical Linear Algebra Computational mathematics Discrete mathematics
Literature	wird in der Lehrveranstaltung bekannt gegeben

Module M1075: Numerical Mathematics

Courses

Title	Typ	Hrs/wk	CP
Numerical Mathematics (L1357)	Lecture	4	6
Numerical Mathematics (L1358)	Recitation Section (small)	2	3

Module Responsible	Prof. Jens Struckmeier
Admission Requirements	None
Recommended Previous Knowledge	Linear Algebra Analysis
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students can describe basic concepts in Numerical Mathematics such as moethods for linear systems of equations and their error analysis, interpolation by polynomials and splines, orthogonalization methods, linear regression, linear optimization, numerical integration, nonlinear equations and eigenvalue problems. They are able to explain them using appropriate examples. Students can discuss logical connections between these concepts. They are capable of illustrating these connections with the help of examples. They know proof strategies and can reproduce them.
Skills	Students can model problems in Numerical Mathematics ith the help of the concepts studied in this course. Moreover, they are capable of solving them by applying established methods. Students are able to discover and verify further logical connections between the concepts studied in the course. For a given problem, the students can develop and execute a suitable approach, and are able to critically evaluate the results.
Personal Competence
Social Competence	Students are able to work together in teams. They are capable to use mathematics as a common language. In doing so, they can communicate new concepts according to the needs of their cooperating partners. Moreover, they can design examples to check and deepen the understanding of their peers.
Autonomy	Students are capable of checking their understanding of complex concepts on their own. They can specify open questions precisely and know where to get help in solving them. Students have developed sufficient persistence to be able to work for longer periods in a goal-oriented manner on hard problems.
Workload in Hours	Independent Study Time 186, Study Time in Lecture 84
Credit points	9
Course achievement	None
Examination	Written exam
Examination duration and scale	120 minutes
Assignment for the Following Curricula	Technomathematics: Core Qualification: Compulsory

Course L1357: Numerical Mathematics

Typ	Lecture
Hrs/wk	4
CP	6
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Lecturer	Dozenten des Fachbereiches Mathematik der UHH
Language	DE/EN
Cycle	WiSe
Content	Linear systems of equations, error analysis Interpolation by polynomials and splines Orthogonalization methods, linear regression Linear optimization, in particular simplex method Numerical integration Nonlinear equations Eigenvalue problems
Literature	Numerische Mathematik, Jochen Werner, Vieweg, 1992 Numerische Mathematik, Robert Schaback, Holger Wendland, Auflage: 5., vollst. neu bearb. Aufl. 2005 (8. September 2004), Sprache: Deutsch, ISBN-10: 3540213945, ISBN-13: 978-3540213949 Numerische Mathematik, Hans-Rudolf Schwarz, Norbert Köckler, Vieweg+Teubner Verlag, 2011, ISBN: 3834815519 ISBN: 9783834815514 Stoer/Bulirsch: Numerische Mathematik 1, Roland Freund, Ronald Hoppe, Springer; Auflage: 10., neu bearb. Aufl. 2007 (18. April 2007), Sprache: Deutsch, ISBN-10: 354045389X, ISBN-13: 978-3540453895 Numerische Mathematik I, Peter Deuflhard, Andreas Hohmann, Gruyter; Auflage: 3., überarb. A. (18. April 2002), Deutsch, ISBN-10: 3110171821, ISBN-13: 978-3110171822

Course L1358: Numerical Mathematics

Typ	Recitation Section (small)
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Dozenten des Fachbereiches Mathematik der UHH
Language	DE/EN
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Module M1085: Mathematical Stochastics

Courses

Title	Typ	Hrs/wk	CP
Mathematical Stochastics (L1392)	Lecture	4	6
Mathematical Stochastics (L1393)	Recitation Section (small)	2	3

Module Responsible	Prof. Holger Drees
Admission Requirements	None
Recommended Previous Knowledge	Analysis Linear Algebra
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students can describe basic concepts in Mathematical Stochastics such as probability measures and random experiments, random variables and pushforward measures, classification numbers of random variables and distributions, transition probabilities and stochastic independence, law of large numbers and limit theorems, measurable functions and general measure integral. They are able to explain them using appropriate examples. Students can discuss logical connections between these concepts. They are capable of illustrating these connections with the help of examples. They know proof strategies and can reproduce them.
Skills	Students can model problems in Stochastics with the help of the concepts studied in this course. Moreover, they are capable of solving them by applying established methods. Students are able to discover and verify further logical connections between the concepts studied in the course. For a given problem, the students can develop and execute a suitable approach, and are able to critically evaluate the results.
Personal Competence
Social Competence	Students are able to work together in teams. They are capable to use mathematics as a common language. In doing so, they can communicate new concepts according to the needs of their cooperating partners. Moreover, they can design examples to check and deepen the understanding of their peers.
Autonomy	Students are capable of checking their understanding of complex concepts on their own. They can specify open questions precisely and know where to get help in solving them. Students have developed sufficient persistence to be able to work for longer periods in a goal-oriented manner on hard problems.
Workload in Hours	Independent Study Time 186, Study Time in Lecture 84
Credit points	9
Course achievement	None
Examination	Written exam
Examination duration and scale	120 minutes
Assignment for the Following Curricula	Technomathematics: Core Qualification: Compulsory

Course L1392: Mathematical Stochastics

Typ	Lecture
Hrs/wk	4
CP	6
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Lecturer	Dozenten des Fachbereiches Mathematik der UHH
Language	DE/EN
Cycle	WiSe
Content	Probability measures and random experiments Random variables and pushforward measures, classification numbers of random variables and distributions Multi-level models: Transition probabilities and stochastic independence Law of large numbers and central limit theorem, Poisson's limit theorem Measurable functions and general measure integral, application in stochastics Treatment of selected problems of statistics, stochastic processes, insurance mathematics Problems of stochastic modelling
Literature	K. Behnen und G. Neuhaus (2003). Grundkurs Stochastik (4. Aufl.). PD-Verlag P. Billingsley (1995). Probability and Measure (3. ed.). Wiley. H. Dehling und B. Haupt (2003). Einführung in die Wahrscheinlichkeitstheorie und Statistik. Springer. C. Hesse (2003). Angewandte Wahrscheinlichkeitstheorie. Vieweg Verlag. U. Krengel (2000). Einführung in die Wahrscheinlichkeitstheorie und Statistik. Vieweg.

Course L1393: Mathematical Stochastics

Typ	Recitation Section (small)
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Dozenten des Fachbereiches Mathematik der UHH
Language	DE/EN
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Module M1074: Higher Analysis

Courses

Title	Typ	Hrs/wk	CP
Higher Analysis (L1355)	Lecture	4	6
Higher Analysis (L1356)	Recitation Section (small)	2	3

Module Responsible	Prof. Vicente Cortés
Admission Requirements	None
Recommended Previous Knowledge	Analysis Linear Algebra
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students can describe basic concepts in Higher Analysis such as submanifolds, tangential bundles, Lebesgue integration theory, fundamentals of funktional analysis, the Hilbert space L², Fourier analysis, L^p spaces, classical inequalities and fundamentals of general measure and integration theory. They are able to explain them using appropriate examples. Students can discuss logical connections between these concepts. They are capable of illustrating these connections with the help of examples. They know proof strategies and can reproduce them.
Skills	Students can model problems in Higher Analysis with the help of the concepts studied in this course. Moreover, they are capable of solving them by applying established methods. Students are able to discover and verify further logical connections between the concepts studied in the course. For a given problem, the students can develop and execute a suitable approach, and are able to critically evaluate the results.
Personal Competence
Social Competence	Students are able to work together in teams. They are capable to use mathematics as a common language. In doing so, they can communicate new concepts according to the needs of their cooperating partners. Moreover, they can design examples to check and deepen the understanding of their peers.
Autonomy	Students are capable of checking their understanding of complex concepts on their own. They can specify open questions precisely and know where to get help in solving them. Students have developed sufficient persistence to be able to work for longer periods in a goal-oriented manner on hard problems.
Workload in Hours	Independent Study Time 186, Study Time in Lecture 84
Credit points	9
Course achievement	None
Examination	Written exam
Examination duration and scale	120 minutes
Assignment for the Following Curricula	Technomathematics: Core Qualification: Compulsory

Course L1355: Higher Analysis

Typ	Lecture
Hrs/wk	4
CP	6
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Lecturer	Dozenten des Fachbereiches Mathematik der UHH
Language	DE/EN
Cycle	WiSe
Content	Submanifolds of Rⁿ Tangential bundles Differential of differentiable mappings Integral theorems for submanifolds (in general form) Lebesgue integration theory Fundamentals of funktional analysis Hilbert space L² and Fourier analysis L^p spaces Classical inequalities Fundamentals of general measure and integration theory
Literature	a) Vektoranalysis - Differentialformen in Analysis, Geometrie und Physik Autoren: Ilka Agricola, Thomas Friedrich Vieweg + Teubner Verlag, 2. Auflage, 2010 Sprache: Deutsch ISBN-10: 3834810169 ISBN-13: 978-3834810168 b) Analysis 3: Maß- und Integrationstheorie, Integralsätze im IRn und Anwendungen (Aufbaukurs Mathematik) Autor: Otto Forster Vieweg+Teubner Verlag; Auflage: 7., überarb. Aufl. 2012 Sprache: Deutsch ISBN-10: 3834823732 ISBN-13: 978-3834823731 c) Höhere Analysis, Autor: R. Lauterbach (Skript, WS 09/10, verfügbar auf http://www.math.uni-hamburg.de/home/lauterbach/analysis3_WS0910.html#skript) d) Real and complex analysis Autor: Walter Rudin Verlag: Oldenbourg Wissenschaftsverlag (25. August 1999) Sprache: Deutsch ISBN-10: 3486247891 ISBN-13: 978-3486247893 oder Real and complex analysis Autor: Walter Rudin McGraw-Hill, 1987 , 3. illustrierte Neuauflage Sprache: Englisch Digitalisiert: 2. Febr. 2010 ISBN: 0070542341, 9780070542341 e) An Introduction to Measure Theory (Graduate Studies in Mathematics) Autor: Terence Tao Verlag: American Mathematical Society (15. September 2011) Sprache: Englisch ISBN-10: 0821869191 ISBN-13: 978-0821869192 f) Maß- und Integrationstheorie Autor: Heinz Bauer Verlag: de Gruyter; Auflage: 2., überarb. A. (1. Juli 1992) Sprache: Englisch ISBN-10: 3110136252 ISBN-13: 978-3110136258 g) Maß- und Integrationstheorie Autor: Jürgen Elstrodt Springer, 2004 ISBN-10: 3540213902 ISBN-13: 9783540213901

Course L1356: Higher Analysis

Typ	Recitation Section (small)
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Dozenten des Fachbereiches Mathematik der UHH
Language	DE/EN
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Module M1752: Practical module 3 (dual study program, Bachelor's degree)

Courses

Title	Typ	Hrs/wk	CP
Practical term 3 (dual study program, Bachelor's degree) (L2881)		0	6

Module Responsible	Dr. Henning Haschke
Admission Requirements	None
Recommended Previous Knowledge	Successful completion of practical module 2 as part of the dual Bachelor’s course course B from the module on interlinking theory and practice as part of the dual Bachelor’s course
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Dual students … … understand the company’s strategic orientation, as well as the functions and organisation of central departments with their decision-making structures, network relationships. … understand the requirements of the engineering profession and correctly estimate the resulting responsibility. … combine their knowledge of facts, principles, theories and methods gained from previous study content with acquired practical knowledge - in particular their knowledge of practical professional procedures and approaches, in the current field of activity.
Skills	Dual students … … apply technical theoretical knowledge to current problems in their own area of work, and evaluate work processes and results. … use technology, equipment and resources in accordance with the assigned work areas and tasks, and assess operational processes and procedures with regard to the intended work results/objectives. … implement the university’s application recommendations in relation to their current tasks.
Personal Competence
Social Competence	Dual students … … plan work processes cooperatively, including across work areas. … communicate professionally with operational stakeholders and present complex issues in a structured, targeted and convincing manner.
Autonomy	Dual students … … assume responsibility for work assignments and areas. … document and reflect on the relevance of subject modules and specialisations for work as an engineer, as well as the implementation of the university’s application recommendations and the associated challenges of a positive transfer of knowledge between theory and practice.
Workload in Hours	Independent Study Time 180, Study Time in Lecture 0
Credit points	6
Course achievement	None
Examination	Written elaboration
Examination duration and scale	Documentation accompanying studies and across semesters: Module credit points are earned by completing a digital learning and development report (e-portfolio). This documents and reflects individual learning experiences and skills development relating to interlinking theory and practice, as well as professional practice. In addition, the partner company provides proof to the dual@TUHH Coordination Office that the dual student has completed the practical phase.
Assignment for the Following Curricula	General Engineering Science (German program, 7 semester): Core Qualification: Compulsory Civil- and Environmental Engineering: Core Qualification: Compulsory Chemical and Bioprocess Engineering: Core Qualification: Compulsory Computer Science: Core Qualification: Compulsory Data Science: Core Qualification: Compulsory Electrical Engineering: Core Qualification: Compulsory Engineering Science: Core Qualification: Compulsory Green Technologies: Energy, Water, Climate: Core Qualification: Compulsory Computer Science in Engineering: Core Qualification: Compulsory Mechanical Engineering: Core Qualification: Compulsory Mechatronics: Core Qualification: Compulsory Naval Architecture: Core Qualification: Compulsory Technomathematics: Core Qualification: Compulsory Engineering and Management - Major in Logistics and Mobility: Core Qualification: Compulsory

Course L2881: Practical term 3 (dual study program, Bachelor's degree)

Typ
Hrs/wk	0
CP	6
Workload in Hours	Independent Study Time 180, Study Time in Lecture 0
Lecturer	Dr. Henning Haschke
Language	DE
Cycle	WiSe
Content	Company onboarding process Assigning work area(s) Extending responsibilities and authorisations of the dual student within the company Independent work tasks and areas Participating in project teams Scheduling the relevant practical modules with work tasks Theory/practice transfer options Scheduling the examination phase/subsequent study semester Operational knowledge and skills Company-specific: strategic direction, organisation of central business and work areas, departments, decision-making structures, network relationships and internal communication Linking facts, principles and theories with practical knowledge Process and procedure options within the labour-market-relevant field of engineering Operational technology, equipment and resources Implementing the university’s application recommendations (theory-practice transfer) in corresponding work and task areas across the company Sharing/reflecting on learning E-portfolio Relevance of subject modules and specialisations when working as an engineer University application recommendations for transferring knowledge between theory and practice
Literature	Studierendenhandbuch Betriebliche Dokumente Hochschulseitige Anwendungsempfehlungen zum Theorie-Praxis-Transfer

Module M0829: Foundations of Management

Courses

Title	Typ	Hrs/wk	CP
Management Tutorial (L0882)	Recitation Section (small)	2	3
Introduction to Management (L0880)	Lecture	3	3

Module Responsible	Prof. Christian Lüthje
Admission Requirements	None
Recommended Previous Knowledge	Basic Knowledge of Mathematics and Business
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	After taking this module, students know the important basics of many different areas in Business and Management, from Planning and Organisation to Marketing and Innovation, and also to Investment and Controlling. In particular they are able to explain the differences between Economics and Management and the sub-disciplines in Management and to name important definitions from the field of Management explain the most important aspects of and goals in Management and name the most important aspects of entreprneurial projects describe and explain basic business functions as production, procurement and sourcing, supply chain management, organization and human ressource management, information management, innovation management and marketing explain the relevance of planning and decision making in Business, esp. in situations under multiple objectives and uncertainty, and explain some basic methods from mathematical Finance state basics from accounting and costing and selected controlling methods.
Skills	Students are able to analyse business units with respect to different criteria (organization, objectives, strategies etc.) and to carry out an Entrepreneurship project in a team. In particular, they are able to analyse Management goals and structure them appropriately analyse organisational and staff structures of companies apply methods for decision making under multiple objectives, under uncertainty and under risk analyse production and procurement systems and Business information systems analyse and apply basic methods of marketing select and apply basic methods from mathematical finance to predefined problems apply basic methods from accounting, costing and controlling to predefined problems
Personal Competence
Social Competence	Students are able to work successfully in a team of students to apply their knowledge from the lecture to an entrepreneurship project and write a coherent report on the project to communicate appropriately and to cooperate respectfully with their fellow students.
Autonomy	Students are able to work in a team and to organize the team themselves to write a report on their project.
Workload in Hours	Independent Study Time 110, Study Time in Lecture 70
Credit points	6
Course achievement	None
Examination	Subject theoretical and practical work
Examination duration and scale	several written exams during the semester
Assignment for the Following Curricula	General Engineering Science (German program, 7 semester): Core Qualification: Compulsory Civil- and Environmental Engineering: Specialisation Civil Engineering: Elective Compulsory Civil- and Environmental Engineering: Specialisation Water and Environment: Elective Compulsory Civil- and Environmental Engineering: Specialisation Traffic and Mobility: Elective Compulsory Bioprocess Engineering: Core Qualification: Compulsory Chemical and Bioprocess Engineering: Specialisation Bio Engineering: Elective Compulsory Chemical and Bioprocess Engineering: Specialisation Chemical Engineering: Elective Compulsory Computer Science: Core Qualification: Compulsory Data Science: Core Qualification: Compulsory Electrical Engineering: Core Qualification: Compulsory Green Technologies: Energy, Water, Climate: Specialisation Biotechnologies: Elective Compulsory Green Technologies: Energy, Water, Climate: Specialisation Energy Systems / Renewable Energies: Elective Compulsory Green Technologies: Energy, Water, Climate: Specialisation Energy Technology: Elective Compulsory Green Technologies: Energy, Water, Climate: Specialisation Maritime Technologies: Elective Compulsory Green Technologies: Energy, Water, Climate: Specialisation Water Technologies: Elective Compulsory Computer Science in Engineering: Core Qualification: Compulsory Integrated Building Technology: Core Qualification: Compulsory Logistics and Mobility: Core Qualification: Compulsory Mechanical Engineering: Core Qualification: Compulsory Mechatronics: Specialisation Naval Engineering: Compulsory Mechatronics: Specialisation Electrical Systems: Compulsory Mechatronics: Specialisation Dynamic Systems and AI: Compulsory Mechatronics: Core Qualification: Compulsory Mechatronics: Specialisation Robot- and Machine-Systems: Compulsory Mechatronics: Specialisation Medical Engineering: Compulsory Orientation Studies: Core Qualification: Elective Compulsory Orientation Studies: Core Qualification: Elective Compulsory Naval Architecture: Core Qualification: Compulsory Technomathematics: Core Qualification: Compulsory Process Engineering: Core Qualification: Compulsory Engineering and Management - Major in Logistics and Mobility: Core Qualification: Compulsory

Course L0882: Management Tutorial

Typ	Recitation Section (small)
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Christian Lüthje, Katharina Roedelius
Language	DE
Cycle	WiSe/SoSe
Content	In the management tutorial, the contents of the lecture will be deepened by practical examples and the application of the discussed tools. If there is adequate demand, a problem-oriented tutorial will be offered in parallel, which students can choose alternatively. Here, students work in groups on self-selected projects that focus on the elaboration of an innovative business idea from the point of view of an established company or a startup. Again, the business knowledge from the lecture should come to practical use. The group projects are guided by a mentor.
Literature	Relevante Literatur aus der korrespondierenden Vorlesung.

Course L0880: Introduction to Management

Typ	Lecture
Hrs/wk	3
CP	3
Workload in Hours	Independent Study Time 48, Study Time in Lecture 42
Lecturer	Prof. Christian Lüthje, Prof. Christian Ringle, Prof. Christoph Ihl, Prof. Cornelius Herstatt, Prof. Kathrin Fischer, Prof. Matthias Meyer, Prof. Thomas Wrona, Prof. Thorsten Blecker, Prof. Wolfgang Kersten
Language	DE
Cycle	WiSe/SoSe
Content	Introduction to Business and Management, Business versus Economics, relevant areas in Business and Management Important definitions from Management, Developing Objectives for Business, and their relation to important Business functions Business Functions: Functions of the Value Chain, e.g. Production and Procurement, Supply Chain Management, Innovation Management, Marketing and Sales Cross-sectional Functions, e.g. Organisation, Human Ressource Management, Supply Chain Management, Information Management Definitions as information, information systems, aspects of data security and strategic information systems Definition and Relevance of innovations, e.g. innovation opporunities, risks etc. Relevance of marketing, B2B vs. B2C-Marketing different techniques from the field of marketing (e.g. scenario technique), pricing strategies important organizational structures basics of human ressource management Introduction to Business Planning and the steps of a planning process Decision Analysis: Elements of decision problems and methods for solving decision problems Selected Planning Tasks, e.g. Investment and Financial Decisions Introduction to Accounting: Accounting, Balance-Sheets, Costing Relevance of Controlling and selected Controlling methods Important aspects of Entrepreneurship projects
Literature	Bamberg, G., Coenenberg, A.: Betriebswirtschaftliche Entscheidungslehre, 14. Aufl., München 2008 Eisenführ, F., Weber, M.: Rationales Entscheiden, 4. Aufl., Berlin et al. 2003 Heinhold, M.: Buchführung in Fallbeispielen, 10. Aufl., Stuttgart 2006. Kruschwitz, L.: Finanzmathematik. 3. Auflage, München 2001. Pellens, B., Fülbier, R. U., Gassen, J., Sellhorn, T.: Internationale Rechnungslegung, 7. Aufl., Stuttgart 2008. Schweitzer, M.: Planung und Steuerung, in: Bea/Friedl/Schweitzer: Allgemeine Betriebswirtschaftslehre, Bd. 2: Führung, 9. Aufl., Stuttgart 2005. Weber, J., Schäffer, U. : Einführung in das Controlling, 12. Auflage, Stuttgart 2008. Weber, J./Weißenberger, B.: Einführung in das Rechnungswesen, 7. Auflage, Stuttgart 2006.

Module M1753: Practical module 4 (dual study program, Bachelor's degree)

Courses

Title	Typ	Hrs/wk	CP
Practical term 4 (dual study program, Bachelor's degree) (L2882)		0	6

Module Responsible	Dr. Henning Haschke
Admission Requirements	None
Recommended Previous Knowledge	Successful completion of practical module 3 as part of the dual Bachelor’s course course B from the module on interlinking theory and practice as part of the dual Bachelor’s course
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Dual students … … understand the company’s strategic orientation, as well as the functions and organisation of central departments with their decision-making structures, network relationships, and relevant company communication. … have developed an understanding of the requirements and responsibilities of the engineering profession, know the scope and limits of the professional field of activity. … can combine their knowledge of facts, principles, theories and methods gained from previous study content with acquired practical knowledge - in particular their knowledge of practical professional procedures and approaches, in the current field of activity.
Skills	Dual students … … apply technical theoretical knowledge to current problems in their own field of work, and evaluate work processes and results, taking into account different possible courses of action. … use technology, equipment and resources in accordance with the assigned work areas and tasks, and can assess operational processes and procedures with regard to the intended work results/objectives. … implement the university’s application recommendations in relation to their current tasks.
Personal Competence
Social Competence	Dual students … … are able to plan work processes cooperatively, across work areas and in heterogeneous groups. … communicate professionally with operational stakeholders and present complex issues in a structured, targeted and convincing manner.
Autonomy	Dual students … … assume responsibility for work assignments and areas, and coordinate the associated work processes. … document and reflect on the relevance of subject modules and specialisations for work as an engineer, as well as the implementation of the university’s application recommendations and the associated challenges of a positive transfer of knowledge between theory and practice.
Workload in Hours	Independent Study Time 180, Study Time in Lecture 0
Credit points	6
Course achievement	None
Examination	Written elaboration
Examination duration and scale	Documentation accompanying studies and across semesters: Module credit points are earned by completing a digital learning and development report (e-portfolio). This documents and reflects individual learning experiences and skills development relating to interlinking theory and practice, as well as professional practice. In addition, the partner company provides proof to the dual@TUHH Coordination Office that the dual student has completed the practical phase.
Assignment for the Following Curricula	General Engineering Science (German program, 7 semester): Core Qualification: Compulsory Civil- and Environmental Engineering: Core Qualification: Compulsory Chemical and Bioprocess Engineering: Core Qualification: Compulsory Computer Science: Core Qualification: Compulsory Data Science: Core Qualification: Compulsory Electrical Engineering: Core Qualification: Compulsory Engineering Science: Core Qualification: Compulsory Green Technologies: Energy, Water, Climate: Core Qualification: Compulsory Computer Science in Engineering: Core Qualification: Compulsory Mechanical Engineering: Core Qualification: Compulsory Mechatronics: Core Qualification: Compulsory Naval Architecture: Core Qualification: Compulsory Technomathematics: Core Qualification: Compulsory Engineering and Management - Major in Logistics and Mobility: Core Qualification: Compulsory

Course L2882: Practical term 4 (dual study program, Bachelor's degree)

Typ
Hrs/wk	0
CP	6
Workload in Hours	Independent Study Time 180, Study Time in Lecture 0
Lecturer	Dr. Henning Haschke
Language	DE
Cycle	SoSe
Content	Company onboarding process Assigning work area(s) Extending responsibilities and authorisations of the dual student within the company Independent work tasks and areas Participating in project teams Scheduling the relevant practical module Theory/practice transfer options Scheduling the examination phase/subsequent study semester Operational knowledge and skills Company-specific: strategic direction, organisation of central business and work areas, departments, decision-making structures, network relationships and internal communication Linking facts, principles and theories with practical knowledge Process and procedure options within the labour-market-relevant field of engineering Operational technology, equipment and resources Implementing the university’s application recommendations (theory-practice transfer) in corresponding work and task areas across the company Sharing/reflecting on learning E-portfolio Relevance of subject modules and specialisations when working as an engineer University application recommendations for transferring knowledge between theory and practice
Literature	Studierendenhandbuch Betriebliche Dokumente Hochschulseitige Anwendungsempfehlungen zum Theorie-Praxis-Transfer

Module M1959: Seminar Technomathematics

Courses

Title	Typ	Hrs/wk	CP
Seminar: Technomathematics (L0920)	Seminar	2	4

Module Responsible	Prof. Anusch Taraz
Admission Requirements	None
Recommended Previous Knowledge	Analysis & Linear Algebra I + II for Technomathematicians or Mathematik I + II (for Engineering Students - German or English lecture series), and an advanced course by the lecturer who is responsible for the seminar
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students acquire a deep understanding of the mathematical subject under consideration.
Skills	Students are able to understand, analyze, classify and work on an advanced mathematical topic, thoroughly study the recommended (and further) literature, write down and present their results in a mathematically correct and comprehensible way.
Personal Competence
Social Competence	Students are able to present their results in an appropriate way to the group.
Autonomy	Students are able to prepare a written scientific report on their own; in particular to find and critically check relevant literature, make and incorporate their own thoughts, finish in time.
Workload in Hours	Independent Study Time 92, Study Time in Lecture 28
Credit points	4
Course achievement	None
Examination	Presentation
Examination duration and scale	60 Minutes
Assignment for the Following Curricula	Technomathematics: Core Qualification: Compulsory

Course L0920: Seminar: Technomathematics

Typ	Seminar
Hrs/wk	2
CP	4
Workload in Hours	Independent Study Time 92, Study Time in Lecture 28
Lecturer	Dr. Christian Seifert, Dozenten der Mathematik, Dozenten des Fachbereiches Mathematik der UHH, Dr. Jens-Peter Zemke, Dr. Thibaut Lunet, Prof. Sabine Le Borne
Language	DE/EN
Cycle	WiSe/SoSe
Content	Selected topics from the fields Applied Analysis Computational mathematics Discrete mathematics Mathematical Optimization
Literature	wird in der Lehrveranstaltung bekannt gegeben

Module M1754: Practical module 5 (dual study program, Bachelor's degree)

Courses

Title	Typ	Hrs/wk	CP
Practical term 5 (dual study program, Bachelor's degree) (L2883)		0	6

Module Responsible	Dr. Henning Haschke
Admission Requirements	None
Recommended Previous Knowledge	Successful completion of practical module 4 as part of the dual Bachelor’s course course C from the module on interlinking theory and practice as part of the dual Bachelor’s course
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Dual students … … combine their knowledge of facts, principles, theories and methods gained from previous study content with acquired practical knowledge - in particular their knowledge of practical professional procedures and approaches, in the current field of activity. … have a critical understanding of the practical applications of their engineering subject.
Skills	Dual students … … apply technical theoretical knowledge to complex, interdisciplinary problems within the company, and evaluate the associated work processes and results, taking into account different possible courses of action. … implement the university’s application recommendations with regard to their current tasks. … develop new solutions as well as procedures and approaches in their field of activity and area of responsibility - including in the case of frequently changing requirements (systemic skills). … are able to analyse and evaluate operational issues using academic methods.
Personal Competence
Social Competence	Dual students … … work responsibly in operational project teams and proactively deal with problems within their team. … represent complex engineering viewpoints, facts, problems and solution approaches in discussions with internal and external stakeholders and develop these further together.
Autonomy	Dual students … … define goals for their own learning and working processes as engineers. … document and reflect on learning and work processes in their area of responsibility. … document and reflect on the relevance of subject modules, specialisations and research for work as an engineer, as well as the implementation of the university’s application recommendations and the associated challenges of a positive transfer of knowledge between theory and practice.
Workload in Hours	Independent Study Time 180, Study Time in Lecture 0
Credit points	6
Course achievement	None
Examination	Written elaboration
Examination duration and scale	Documentation accompanying studies and across semesters: Module credit points are earned by completing a digital learning and development report (e-portfolio). This documents and reflects individual learning experiences and skills development relating to interlinking theory and practice, as well as professional practice. In addition, the partner company provides proof to the dual@TUHH Coordination Office that the dual student has completed the practical phase.
Assignment for the Following Curricula	General Engineering Science (German program, 7 semester): Core Qualification: Compulsory Civil- and Environmental Engineering: Core Qualification: Compulsory Chemical and Bioprocess Engineering: Core Qualification: Compulsory Computer Science: Core Qualification: Compulsory Data Science: Core Qualification: Compulsory Electrical Engineering: Core Qualification: Compulsory Engineering Science: Core Qualification: Compulsory Green Technologies: Energy, Water, Climate: Core Qualification: Compulsory Computer Science in Engineering: Core Qualification: Compulsory Mechanical Engineering: Core Qualification: Compulsory Mechatronics: Core Qualification: Compulsory Naval Architecture: Core Qualification: Compulsory Technomathematics: Core Qualification: Compulsory Engineering and Management - Major in Logistics and Mobility: Core Qualification: Compulsory

Course L2883: Practical term 5 (dual study program, Bachelor's degree)

Typ
Hrs/wk	0
CP	6
Workload in Hours	Independent Study Time 180, Study Time in Lecture 0
Lecturer	Dr. Henning Haschke
Language	DE
Cycle	WiSe
Content	Company onboarding process Assigning a future professional field of activity as an engineer (B.Sc.) and associated areas of work Extending responsibilities and authorisations of the dual student within the company up to the intended first assignment after completing their studies or to the assignment completed during the subsequent dual Master’s course Taking personal responsibility within a team - in their own area of responsibility and across departments Scheduling the final practical module with a clear correlation to work structures Internal agreement on a potential topic for the Bachelor’s dissertation Planning the Bachelor’s dissertation within the company in cooperation with TU Hamburg Scheduling the examination phase/sixth study semester Operational knowledge and skills Company-specific: dealing with change, team development, responsibility as an engineer in their own future field of work (B.Sc.), dealing with complex contexts and unresolved problems, developing and implementing innovative solutions Specialising in one field of work (final dissertation) Systemic skills Implementing the university’s application recommendations (theory-practice transfer) in corresponding work and task areas across the company Sharing/reflecting on learning E-portfolio Relevance of subject modules and specialisations when working as an engineer Importance of research and innovation when working as an engineer University application recommendations for transferring knowledge between theory and practice
Literature	Studierendenhandbuch Betriebliche Dokumente Hochschulseitige Anwendungsempfehlungen zum Theorie-Praxis-Transfer

Specialization I. Mathematics

Module M1052: Algebra

Courses

Title	Typ	Hrs/wk	CP
Algebra (L1317)	Lecture	4	6
Algebra (L1318)	Recitation Section (small)	2	3

Module Responsible	Prof. Christoph Schweigert
Admission Requirements	None
Recommended Previous Knowledge	Linear Algebra
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students can name the basic concepts in Algebra such as groups, rings and modules. They are able to explain them using appropriate examples. Students can discuss logical connections between these concepts. They are capable of illustrating these connections with the help of examples. They know proof strategies and can reproduce them.
Skills	Students can model problems in Algebra with the help of the concepts studied in this course. Moreover, they are capable of solving them by applying established methods. Students are able to discover and verify further logical connections between the concepts studied in the course. For a given problem, the students can develop and execute a suitable approach, and are able to critically evaluate the results.
Personal Competence
Social Competence	Students are able to work together in teams. They are capable to use mathematics as a common language. In doing so, they can communicate new concepts according to the needs of their cooperating partners. Moreover, they can design examples to check and deepen the understanding of their peers.
Autonomy	Students are capable of checking their understanding of complex concepts on their own. They can specify open questions precisely and know where to get help in solving them. Students have developed sufficient persistence to be able to work for longer periods in a goal-oriented manner on hard problems.
Workload in Hours	Independent Study Time 186, Study Time in Lecture 84
Credit points	9
Course achievement	None
Examination	Oral exam
Examination duration and scale	30 min
Assignment for the Following Curricula	Technomathematics: Specialisation I. Mathematics: Elective Compulsory

Course L1317: Algebra

Typ	Lecture
Hrs/wk	4
CP	6
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Lecturer	Dozenten des Fachbereiches Mathematik der UHH
Language	DE/EN
Cycle	SoSe
Content
Literature	Jantzen, Schwermer, "Algebra" (Springer) Artin, "Algebra" (Birkhäuser) Bosch, "Algebra" (Springer) Lang, "Algebra" (Springer)

Course L1318: Algebra

Typ	Recitation Section (small)
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Dozenten des Fachbereiches Mathematik der UHH
Language	DE/EN
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Module M0715: Solvers for Sparse Linear Systems

Courses

Title	Typ	Hrs/wk	CP
Solvers for Sparse Linear Systems (L0583)	Lecture	2	3
Solvers for Sparse Linear Systems (L0584)	Recitation Section (small)	2	3

Module Responsible	Prof. Sabine Le Borne
Admission Requirements	None
Recommended Previous Knowledge	Mathematics I + II for Engineering students or Analysis & Lineare Algebra I + II for Technomathematicians Programming experience in C
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students can list classical and modern iteration methods and their interrelationships, repeat convergence statements for iterative methods, explain aspects regarding the efficient implementation of iteration methods.
Skills	Students are able to analyse, implement, test, and compare iterative methods, analyse the convergence behaviour of iterative methods and, if applicable, compute congergence rates.
Personal Competence
Social Competence	Students are able to work together in heterogeneously composed teams (i.e., teams from different study programs and background knowledge), explain theoretical foundations and support each other with practical aspects regarding the implementation of algorithms.
Autonomy	Students are capable to assess whether the supporting theoretical and practical excercises are better solved individually or in a team, to work on complex problems over an extended period of time, to assess their individual progess and, if necessary, to ask questions and seek help.
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Credit points	6
Course achievement	None
Examination	Oral exam
Examination duration and scale	20 min
Assignment for the Following Curricula	Computer Science: Specialisation II. Mathematics and Engineering Science: Elective Compulsory Data Science: Core Qualification: Elective Compulsory Data Science: Specialisation I. Mathematics/Computer Science: Elective Compulsory Computer Science in Engineering: Specialisation II. Mathematics & Engineering Science: Elective Compulsory Technomathematics: Specialisation I. Mathematics: Elective Compulsory

Course L0583: Solvers for Sparse Linear Systems

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Sabine Le Borne
Language	EN
Cycle	SoSe
Content	Sparse systems: Orderings and storage formats, direct solvers Classical methods: basic notions, convergence Projection methods Krylov space methods Preconditioning (e.g. ILU) Multigrid methods Domain Decomposition Methods
Literature	Y. Saad. Iterative methods for sparse linear systems M. Olshanskii, E. Tyrtyshnikov. Iterative methods for linear systems: theory and applications

Course L0584: Solvers for Sparse Linear Systems

Typ	Recitation Section (small)
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Sabine Le Borne
Language	EN
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Module M1056: Functional Analysis

Courses

Title	Typ	Hrs/wk	CP
Functional Analysis (L1327)	Lecture	4	6
Functional Analysis (L1328)	Recitation Section (small)	2	3

Module Responsible	Prof. Armin Iske
Admission Requirements	None
Recommended Previous Knowledge	Linear Algebra Analysis
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students can name basic concepts in Functional Analysis such as Banach and Hilbert spaces, Baire's category theorem, Linear operators, dual spaces, classical function spaces, the Hahn-Banach theorem, (non-)compactness, the Spectrum and compact operators. They are able to explain them using appropriate examples. Students can discuss logical connections between these concepts. They are capable of illustrating these connections with the help of examples. They know proof strategies and can reproduce them.
Skills	Students can model problems in Functional Analysis with the help of the concepts studied in this course. Moreover, they are capable of solving them by applying established methods. Students are able to discover and verify further logical connections between the concepts studied in the course. For a given problem, the students can develop and execute a suitable approach, and are able to critically evaluate the results.
Personal Competence
Social Competence	Students are able to work together in teams. They are capable to use mathematics as a common language. In doing so, they can communicate new concepts according to the needs of their cooperating partners. Moreover, they can design examples to check and deepen the understanding of their peers.
Autonomy	Students are capable of checking their understanding of complex concepts on their own. They can specify open questions precisely and know where to get help in solving them. Students have developed sufficient persistence to be able to work for longer periods in a goal-oriented manner on hard problems.
Workload in Hours	Independent Study Time 186, Study Time in Lecture 84
Credit points	9
Course achievement	None
Examination	Oral exam
Examination duration and scale	30 min
Assignment for the Following Curricula	Technomathematics: Specialisation I. Mathematics: Elective Compulsory

Course L1327: Functional Analysis

Typ	Lecture
Hrs/wk	4
CP	6
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Lecturer	Dozenten des Fachbereiches Mathematik der UHH
Language	DE/EN
Cycle	SoSe
Content	Normed, Banach and Hilbert spaces Baire's category theorem and implications (fundamental principles) Linear operators, dual spaces classical function spaces Hahn-Banach theorem, (non-)compactness Spectrum, compact operators
Literature	Alt, Lineare Funktionalanalysis -Eine anwendungsorientierte Einführung, Springer, 2012 Werner, Funktionalanalysis, Springer, 2011 Rudin, Functional analysis, McGraw-Hill, 1973 Adams, Sobolev spaces, Academic press, 1975

Course L1328: Functional Analysis

Typ	Recitation Section (small)
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Dozenten des Fachbereiches Mathematik der UHH
Language	DE/EN
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Module M0692: Approximation and Stability

Courses

Title	Typ	Hrs/wk	CP
Approximation and Stability (L0487)	Lecture	3	4
Approximation and Stability (L0488)	Recitation Section (small)	1	2

Module Responsible

Prof. Marko Lindner

Admission Requirements

None

Recommended Previous Knowledge

Linear Algebra: systems of linear equations, least squares problems, eigenvalues, singular values
Analysis: sequences, series, differentiation, integration

Educational Objectives

After taking part successfully, students have reached the following learning results

Professional Competence

Knowledge

Students are able to

sketch and interrelate basic concepts of functional analysis (Hilbert space, operators),
name and understand concrete approximation methods,
name and explain basic stability theorems,
discuss spectral quantities, conditions numbers and methods of regularisation

Skills

Students are able to

apply basic results from functional analysis,
apply approximation methods,
apply stability theorems,
compute spectral quantities,
apply regularisation methods.

Personal Competence

Social Competence

Students are able to solve specific problems in groups and to present their results appropriately (e.g. as a seminar presentation).

Autonomy

Students are capable of checking their understanding of complex concepts on their own. They can specify open questions precisely and know where to get help in solving them.
Students have developed sufficient persistence to be able to work for longer periods in a goal-oriented manner on hard problems.

Workload in Hours

Independent Study Time 124, Study Time in Lecture 56

Credit points

Course achievement

Compulsory	Bonus	Form	Description
Yes	None	Presentation

Examination

Oral exam

Examination duration and scale

20 min

Assignment for the Following Curricula

Electrical Engineering: Specialisation Control and Power Systems Engineering: Elective Compulsory
Mechatronics: Core Qualification: Elective Compulsory
Technomathematics: Specialisation I. Mathematics: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: Elective Compulsory

Course L0487: Approximation and Stability

Typ	Lecture
Hrs/wk	3
CP	4
Workload in Hours	Independent Study Time 78, Study Time in Lecture 42
Lecturer	Dr. Christian Seifert, Prof. Marko Lindner
Language	DE/EN
Cycle	SoSe
Content	This course is about solving the following basic problems of Linear Algebra, systems of linear equations, least squares problems, eigenvalue problems but now in function spaces (i.e. vector spaces of infinite dimension) by a stable approximation of the problem in a space of finite dimension. Contents: crash course on Hilbert spaces: metric, norm, scalar product, completeness crash course on operators: boundedness, norm, compactness, projections uniform vs. strong convergence, approximation methods applicability and stability of approximation methods, Polski's theorem Galerkin methods, collocation, spline interpolation, truncation convolution and Toeplitz operators crash course on C*-algebras convergence of condition numbers convergence of spectral quantities: spectrum, eigen values, singular values, pseudospectra regularisation methods (truncated SVD, Tichonov)
Literature	R. Hagen, S. Roch, B. Silbermann: C*-Algebras in Numerical Analysis H. W. Alt: Lineare Funktionalanalysis M. Lindner: Infinite matrices and their finite sections

Course L0488: Approximation and Stability

Typ	Recitation Section (small)
Hrs/wk	1
CP	2
Workload in Hours	Independent Study Time 46, Study Time in Lecture 14
Lecturer	Dr. Christian Seifert, Prof. Marko Lindner
Language	DE/EN
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Module M1062: Mathematical Statistics

Courses

Title	Typ	Hrs/wk	CP
Mathematical Statistics (L1339)	Lecture	3	4
Mathematical Statistics (L1340)	Recitation Section (small)	1	2

Module Responsible	Prof. Natalie Neumeyer
Admission Requirements	None
Recommended Previous Knowledge	Mathematical Stochastics Measure Theory and Stochastics
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students can describe basic concepts in Mathematical Statistics such as the substitution and Maximum-Likelihood methods for construction of estimators, optimal unfalsified estimators, optimal tests for parametric probability distributions, sufficiency and completeness and their application to estimation and test problems, tests in normal distribution and confidence domains and test families. They are able to explain them using appropriate examples. Students can discuss logical connections between these concepts. They are capable of illustrating these connections with the help of examples. They know proof strategies and can reproduce them.
Skills	Students can model problems in Mathematical Statistics with the help of the concepts studied in this course. Moreover, they are capable of solving them by applying established methods. Students are able to discover and verify further logical connections between the concepts studied in the course. For a given problem, the students can develop and execute a suitable approach, and are able to critically evaluate the results.
Personal Competence
Social Competence	Students are able to work together in teams. They are capable to use mathematics as a common language. In doing so, they can communicate new concepts according to the needs of their cooperating partners. Moreover, they can design examples to check and deepen the understanding of their peers.
Autonomy	Students are capable of checking their understanding of complex concepts on their own. They can specify open questions precisely and know where to get help in solving them. Students have developed sufficient persistence to be able to work for longer periods in a goal-oriented manner on hard problems.
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Credit points	6
Course achievement	None
Examination	Written exam
Examination duration and scale	120 minutes
Assignment for the Following Curricula	Technomathematics: Specialisation I. Mathematics: Elective Compulsory

Course L1339: Mathematical Statistics

Typ	Lecture
Hrs/wk	3
CP	4
Workload in Hours	Independent Study Time 78, Study Time in Lecture 42
Lecturer	Dozenten des Fachbereiches Mathematik der UHH
Language	DE/EN
Cycle	SoSe
Content	Substitution and Maximum-Likelihood methods for construction of estimators Optimal unfalsified estimators Optimal tests for parametric probability distributions (Neymann-Pearson theory) Sufficiency and completeness and their application to estimation and test problems Tests in normal distribution (e.g. Student's test) Confidence domains and test families
Literature	V. K. Rohatgi and A. K. Ehsanes Saleh (2001). An introduction to probability and statistics. Wiley. L. Wasserman (2010). All of statistics : A concise course in statistical inference. Springer. H. Witting (1985). Mathematische Statistik: Parametrische Verfahren bei festem Stichprobenumfang. Teubner.

Course L1340: Mathematical Statistics

Typ	Recitation Section (small)
Hrs/wk	1
CP	2
Workload in Hours	Independent Study Time 46, Study Time in Lecture 14
Lecturer	Dozenten des Fachbereiches Mathematik der UHH
Language	DE/EN
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Module M1429: Complex Functions

Courses

Title	Typ	Hrs/wk	CP
Complex Functions (L1038)	Lecture	2	1
Complex Functions (L1042)	Recitation Section (large)	1	1
Complex Functions (L1041)	Recitation Section (small)	1	1

Module Responsible	Dozenten des Fachbereiches Mathematik der UHH
Admission Requirements	None
Recommended Previous Knowledge	Analysis, Higher Analysis, Linear Algebra
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students deepen their mathematics education through the comprehensive acquisition of knowledge in complex calculus.
Skills	Students possess the ability to use concepts and methods from this field, to classify and compare them, and to independently acquire further concepts from this field.
Personal Competence
Social Competence
Autonomy
Workload in Hours	Independent Study Time 34, Study Time in Lecture 56
Credit points	3
Course achievement	None
Examination	Oral exam
Examination duration and scale	30 min
Assignment for the Following Curricula	Technomathematics: Specialisation I. Mathematics: Elective Compulsory

Course L1038: Complex Functions

Typ	Lecture
Hrs/wk	2
CP	1
Workload in Hours	Independent Study Time 2, Study Time in Lecture 28
Lecturer	Dozenten des Fachbereiches Mathematik der UHH
Language	DE
Cycle	SoSe
Content	Main features of complex analysis Functions of one complex variable Complex differentiation Conformal mappings Complex integration Cauchy's integral theorem Cauchy's integral formula Taylor and Laurent series expansion Singularities and residuals Integral transformations: Fourier and Laplace transformation
Literature	http://www.math.uni-hamburg.de/teaching/export/tuhh/index.html

Course L1042: Complex Functions

Typ	Recitation Section (large)
Hrs/wk	1
CP	1
Workload in Hours	Independent Study Time 16, Study Time in Lecture 14
Lecturer	Dozenten des Fachbereiches Mathematik der UHH
Language	DE
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Course L1041: Complex Functions

Typ	Recitation Section (small)
Hrs/wk	1
CP	1
Workload in Hours	Independent Study Time 16, Study Time in Lecture 14
Lecturer	Dozenten des Fachbereiches Mathematik der UHH
Language	DE
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Module M1079: Differential Geometry

Courses

Title	Typ	Hrs/wk	CP
Differential Geometry (L1365)	Lecture	4	6
Differential Geometry (L1366)	Recitation Section (small)	2	3

Module Responsible	Prof. Vicente Cortés
Admission Requirements	None
Recommended Previous Knowledge	Analysis Higher Analysis
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students can describe basic concepts in Differential Geometry such as curves in Euclidean space, differentiable manifolds, hyperplanes in Euclidean space, surfaces, geodesy in Riemannian manifolds and Riemannian manifolds with constant curvature. They are able to explain them using appropriate examples. Students can discuss logical connections between these concepts. They are capable of illustrating these connections with the help of examples. They know proof strategies and can reproduce them.
Skills	Students can model problems in Differential Geometry with the help of the concepts studied in this course. Moreover, they are capable of solving them by applying established methods. Students are able to discover and verify further logical connections between the concepts studied in the course. For a given problem, the students can develop and execute a suitable approach, and are able to critically evaluate the results.
Personal Competence
Social Competence	Students are able to work together in teams. They are capable to use mathematics as a common language. In doing so, they can communicate new concepts according to the needs of their cooperating partners. Moreover, they can design examples to check and deepen the understanding of their peers.
Autonomy	Students are capable of checking their understanding of complex concepts on their own. They can specify open questions precisely and know where to get help in solving them. Students have developed sufficient persistence to be able to work for longer periods in a goal-oriented manner on hard problems.
Workload in Hours	Independent Study Time 186, Study Time in Lecture 84
Credit points	9
Course achievement	None
Examination	Oral exam
Examination duration and scale	30 min
Assignment for the Following Curricula	Technomathematics: Specialisation I. Mathematics: Elective Compulsory

Course L1365: Differential Geometry

Typ	Lecture
Hrs/wk	4
CP	6
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Lecturer	Dozenten des Fachbereiches Mathematik der UHH
Language	DE/EN
Cycle	SoSe
Content	Curves in the Euclidean space Introduction to differentiable manifolds Hyperplanes in the Euclidean space Surfaces Geodesy in Riemannian manifolds Riemannian manifolds with constant curvature
Literature	Manfredo Perdigão do Carmo: Riemannian geometry, Birkhäuser, 1992. Takashi Sakai, Riemannian geometry, AMS, 1996. Frank Warner, Foundations of differentiable manifolds and Lie groups, Springer, 1983.

Course L1366: Differential Geometry

Typ	Recitation Section (small)
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Dozenten des Fachbereiches Mathematik der UHH
Language	DE/EN
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Module M1080: Ordinary Differential Equations and Dynamical Systems

Courses

Title	Typ	Hrs/wk	CP
Ordinary Differential Equations and Dynamical Systems (L1367)	Lecture	4	6
Ordinary Differential Equations and Dynamical Systems (L1368)	Recitation Section (small)	2	3

Module Responsible	Prof. Jens Rademacher
Admission Requirements	None
Recommended Previous Knowledge	Analysis Higher Analysis
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students can describe basic concepts such as modelling with dynamical system, ordinary differential equations as dynamical systems, long time behavior of orbits, hyperbolic systems, linear differential equations and linearisations, structural stability and bifurcations, symbolic dynamic, Hamilton systems and ergodic systems. They are able to explain them using appropriate examples. Students can discuss logical connections between these concepts. They are capable of illustrating these connections with the help of examples. They know proof strategies and can reproduce them.
Skills	Students can model problems in Ordinary differential aquations and dynamical systems with the help of the concepts studied in this course. Moreover, they are capable of solving them by applying established methods. Students are able to discover and verify further logical connections between the concepts studied in the course. For a given problem, the students can develop and execute a suitable approach, and are able to critically evaluate the results.
Personal Competence
Social Competence	Students are able to work together in teams. They are capable to use mathematics as a common language. In doing so, they can communicate new concepts according to the needs of their cooperating partners. Moreover, they can design examples to check and deepen the understanding of their peers.
Autonomy	Students are capable of checking their understanding of complex concepts on their own. They can specify open questions precisely and know where to get help in solving them. Students have developed sufficient persistence to be able to work for longer periods in a goal-oriented manner on hard problems.
Workload in Hours	Independent Study Time 186, Study Time in Lecture 84
Credit points	9
Course achievement	None
Examination	Oral exam
Examination duration and scale	30 min
Assignment for the Following Curricula	Technomathematics: Specialisation I. Mathematics: Elective Compulsory

Course L1367: Ordinary Differential Equations and Dynamical Systems

Typ	Lecture
Hrs/wk	4
CP	6
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Lecturer	Dozenten des Fachbereiches Mathematik der UHH
Language	DE/EN
Cycle	SoSe
Content	Modelling with dynamical systems Ordinary differential equations as dynamical systems (existence, uniqueness) Long time behavior of orbits (predictibility, periodicity, stability, limit sets, attractors) Hyperbolic systems, linear differential equations and linearisations Structural stability and bifurcations Symbolic dynamics Hamilton systems, ergodic systems
Literature	H. Amann, Gewöhnliche Differentialgleichungen, de Gruyter 1995 C. Chicone, Ordinary Differential Equations with Applications, Springer 2006. H. Heuser, Gewöhnliche Differentialgleichungen, Teubner 2009. M. Hirsch, S. Smale, R. Devaney, Differential equations, dynamical systems, and an introduction to chaos, Elsevier 2004. W. Walter, Gewöhnliche Differentialgleichungen, Springer 2000.

Course L1368: Ordinary Differential Equations and Dynamical Systems

Typ	Recitation Section (small)
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Dozenten des Fachbereiches Mathematik der UHH
Language	DE/EN
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Module M1060: Optimization

Courses

Title	Typ	Hrs/wk	CP
Optimization (L1333)	Lecture	4	6
Optimization (L1334)	Recitation Section (small)	2	3

Module Responsible	Prof. Winnifried Wollner
Admission Requirements	None
Recommended Previous Knowledge	Linear Algebra Analysis
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students can describe basic concepts in Optimization such as conditions for optimality, globally convergent descent methods, locally fast convergent methods, locally and globally fast convergent methods, numerical methods and duality. They are able to explain them using appropriate examples. Students can discuss logical connections between these concepts. They are capable of illustrating these connections with the help of examples. They know proof strategies and can reproduce them.
Skills	Students can model problems in Optimization with the help of the concepts studied in this course. Moreover, they are capable of solving them by applying established methods. Students are able to discover and verify further logical connections between the concepts studied in the course. For a given problem, the students can develop and execute a suitable approach, and are able to critically evaluate the results.
Personal Competence
Social Competence	Students are able to work together in teams. They are capable to use mathematics as a common language. In doing so, they can communicate new concepts according to the needs of their cooperating partners. Moreover, they can design examples to check and deepen the understanding of their peers.
Autonomy	Students are capable of checking their understanding of complex concepts on their own. They can specify open questions precisely and know where to get help in solving them. Students have developed sufficient persistence to be able to work for longer periods in a goal-oriented manner on hard problems.
Workload in Hours	Independent Study Time 186, Study Time in Lecture 84
Credit points	9
Course achievement	None
Examination	Oral exam
Examination duration and scale	30 min
Assignment for the Following Curricula	Technomathematics: Specialisation I. Mathematics: Elective Compulsory

Course L1333: Optimization

Typ	Lecture
Hrs/wk	4
CP	6
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Lecturer	Dozenten des Fachbereiches Mathematik der UHH
Language	DE/EN
Cycle	SoSe
Content	real world Examples non-restricted optimization necessary and sufficient conditions for optimality globally convergent descent methods, (e.g gradient methods, Trust-Region-methods) locally fast convergentmethods (e.g. Newton and quasi-Newton-methods) locally and globally fast convergent methods (e.g. globalised Newton-method) restricted optimization necessary and sufficient conditions for optimality numerical methods (e.g. Penalty-method, SQP-method) Selected topics (e.g. convex optimization, duality, parametric optimization)
Literature	Ulbrich, M. and Ulbrich, S., Nichtlineare Optimierung, Verlag Birkhäuser Basel 2012 C. Geiger and C. Kanzow, Numerische Verfahren zur Lösung unrestringierter Optimierungsaufgaben, Verlag Springer Berlin Heidelberg, 1999 C. Geiger and C. Kanzow, Theorie und Numerik restringierter Optimierungsaufgaben, Verlag Springer Berlin Heidelberg, 2002 J. Nocedal and S. J. Wright, Numerical Optimization, Verlag: Springer, 1999 D. P. Bertsekas, Nonlinear Programming, Publisher: Athena Scientific,1999, 2nd Edition

Course L1334: Optimization

Typ	Recitation Section (small)
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Dozenten des Fachbereiches Mathematik der UHH
Language	DE/EN
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Module M0852: Graph Theory and Optimization

Courses

Title	Typ	Hrs/wk	CP
Graph Theory and Optimization (L1046)	Lecture	2	3
Graph Theory and Optimization (L1047)	Recitation Section (small)	2	3

Module Responsible	Prof. Anusch Taraz
Admission Requirements	None
Recommended Previous Knowledge	Discrete Algebraic Structures Mathematics I
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students can name the basic concepts in Graph Theory and Optimization. They are able to explain them using appropriate examples. Students can discuss logical connections between these concepts. They are capable of illustrating these connections with the help of examples. They know proof strategies and can reproduce them.
Skills	Students can model problems in Graph Theory and Optimization with the help of the concepts studied in this course. Moreover, they are capable of solving them by applying established methods. Students are able to discover and verify further logical connections between the concepts studied in the course. For a given problem, the students can develop and execute a suitable approach, and are able to critically evaluate the results.
Personal Competence
Social Competence	Students are able to work together in teams. They are capable to use mathematics as a common language. In doing so, they can communicate new concepts according to the needs of their cooperating partners. Moreover, they can design examples to check and deepen the understanding of their peers.
Autonomy	Students are capable of checking their understanding of complex concepts on their own. They can specify open questions precisely and know where to get help in solving them. Students have developed sufficient persistence to be able to work for longer periods in a goal-oriented manner on hard problems.
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Credit points	6
Course achievement	None
Examination	Written exam
Examination duration and scale	120 min
Assignment for the Following Curricula	General Engineering Science (German program, 7 semester): Specialisation Computer Science: Compulsory General Engineering Science (German program, 7 semester): Specialisation Data Science: Elective Compulsory Computer Science: Core Qualification: Compulsory Data Science: Core Qualification: Compulsory Engineering Science: Specialisation Data Science: Elective Compulsory Computer Science in Engineering: Specialisation II. Mathematics & Engineering Science: Elective Compulsory Logistics and Mobility: Specialisation Traffic Planning and Systems: Elective Compulsory Logistics and Mobility: Specialisation Information Technology: Elective Compulsory Technomathematics: Specialisation I. Mathematics: Elective Compulsory Engineering and Management - Major in Logistics and Mobility: Specialisation Traffic Planning and Systems: Elective Compulsory Engineering and Management - Major in Logistics and Mobility: Specialisation Information Technology: Elective Compulsory

Course L1046: Graph Theory and Optimization

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Anusch Taraz
Language	DE/EN
Cycle	SoSe
Content	Graphs, search algorithms for graphs, trees planar graphs shortest paths minimum spanning trees maximum flow and minimum cut theorems of Menger, König-Egervary, Hall NP-complete problems backtracking and heuristics linear programming duality integer linear programming
Literature	M. Aigner: Diskrete Mathematik, Vieweg, 2004 T. Cormen, Ch. Leiserson, R. Rivest, C. Stein: Algorithmen - Eine Einführung, Oldenbourg, 2013 J. Matousek und J. Nesetril: Diskrete Mathematik, Springer, 2007 A. Steger: Diskrete Strukturen (Band 1), Springer, 2001 A. Taraz: Diskrete Mathematik, Birkhäuser, 2012 V. Turau: Algorithmische Graphentheorie, Oldenbourg, 2009 K.-H. Zimmermann: Diskrete Mathematik, BoD, 2006

Course L1047: Graph Theory and Optimization

Typ	Recitation Section (small)
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Anusch Taraz
Language	DE/EN
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Module M1061: Measure Theory and Stochastics

Courses

Title	Typ	Hrs/wk	CP
Measure Theory and Stochastics (L1335)	Lecture	3	4
Measure Theory and Stochastics (L1338)	Recitation Section (small)	1	2

Module Responsible	Prof. Holger Drees
Admission Requirements	None
Recommended Previous Knowledge	Mathematical Stochastics
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students can describe basic concepts in Stochastics auch as general densities, conditional expectation, martingals in discrete time, convergence of probability measures and integral transformations. They are able to explain them using appropriate examples. Students can discuss logical connections between these concepts. They are capable of illustrating these connections with the help of examples. They know proof strategies and can reproduce them.
Skills	Students can model problems in Stochastics with the help of the concepts studied in this course. Moreover, they are capable of solving them by applying established methods. Students are able to discover and verify further logical connections between the concepts studied in the course. For a given problem, the students can develop and execute a suitable approach, and are able to critically evaluate the results.
Personal Competence
Social Competence	Students are able to work together in teams. They are capable to use mathematics as a common language. In doing so, they can communicate new concepts according to the needs of their cooperating partners. Moreover, they can design examples to check and deepen the understanding of their peers.
Autonomy	Students are capable of checking their understanding of complex concepts on their own. They can specify open questions precisely and know where to get help in solving them. Students have developed sufficient persistence to be able to work for longer periods in a goal-oriented manner on hard problems
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Credit points	6
Course achievement	None
Examination	Oral exam
Examination duration and scale	30 min
Assignment for the Following Curricula	Technomathematics: Specialisation I. Mathematics: Elective Compulsory

Course L1335: Measure Theory and Stochastics

Typ	Lecture
Hrs/wk	3
CP	4
Workload in Hours	Independent Study Time 78, Study Time in Lecture 42
Lecturer	Dozenten des Fachbereiches Mathematik der UHH
Language	DE/EN
Cycle	SoSe
Content	General densities, Radon-Nikodym theorem Conditional expectation, Markov kernels Martingals in discrete time Convergence of probability measures Integral transformations (e.g. generating functions, Fourier transformation, Laplace transformation)
Literature	H. Bauer, Maß- und Integrationstheorie, de Gruyter Lehrbuch, Auflage: 2., überarb. A. (1. Juli 1992) H. Bauer, Wahrscheinlichkeitstheorie, de Gruyter Lehrbuch, Verlag: de Gruyter; Auflage: 5. durchges. und verb. (2002) J. Estrodt, Maß- und Integrationstheorie, Springer, 7., korrigierte und aktualisierte Auflage 2011

Course L1338: Measure Theory and Stochastics

Typ	Recitation Section (small)
Hrs/wk	1
CP	2
Workload in Hours	Independent Study Time 46, Study Time in Lecture 14
Lecturer	Dozenten des Fachbereiches Mathematik der UHH
Language	DE/EN
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Module M0714: Numerical Methods for Ordinary Differential Equations

Courses

Title	Typ	Hrs/wk	CP
Numerical Treatment of Ordinary Differential Equations (L0576)	Lecture	2	3
Numerical Treatment of Ordinary Differential Equations (L0582)	Recitation Section (small)	2	3

Module Responsible	Prof. Daniel Ruprecht
Admission Requirements	None
Recommended Previous Knowledge	Mathematik I, II, III for Engineers (German or English) or Analysis & Linear Algebra I + II plus Analysis III for Technomathematiker. Basic knowledge of MATLAB, Python or a similar programming language.
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students are able to name numerical methods for the solution of ordinary differential equations and explain their core ideas, formulate convergence statements for the taught numerical methods (including the necessary assumptions about the solved problem), explain aspects regarding the practical realisation of a method, select the appropriate numerical method for specific problems, implement the numerical algorithms efficiently and interpret the numerical results.
Skills	Students are able to implement, apply and compare numerical methods for the solution of ordinary differential equations, explain the convergence behaviour of numerical methods, taking into consideration the solved problem and selected algorithm, develop a suitable solution approach for a given problem, if necessary by combining multiple algorithms, realise this approach and critically evaluate results.
Personal Competence
Social Competence	Students are able to work together in heterogeneous teams (i.e., teams from different study programs and with different background knowledge), explain theoretical foundations and support each other with practical aspects regarding the implementation of algorithms.
Autonomy	Students are capable to assess whether the provided theoretical and practical excercises are better solved individually or in a team and to assess their individual progress and, if necessary, to ask questions and seek help.
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Credit points	6
Course achievement	None
Examination	Written exam
Examination duration and scale	90 min
Assignment for the Following Curricula	Bioprocess Engineering: Specialisation A - General Bioprocess Engineering: Elective Compulsory Chemical and Bioprocess Engineering: Specialisation Chemical Process Engineering: Elective Compulsory Chemical and Bioprocess Engineering: Specialisation General Process Engineering: Elective Compulsory Computer Science: Specialisation III. Mathematics: Elective Compulsory Data Science: Specialisation I. Mathematics: Elective Compulsory Data Science: Specialisation IV. Special Focus Area: Elective Compulsory Electrical Engineering: Specialisation Control and Power Systems Engineering: Elective Compulsory Energy Systems: Core Qualification: Elective Compulsory Aircraft Systems Engineering: Core Qualification: Elective Compulsory Interdisciplinary Mathematics: Specialisation II. Numerical - Modelling Training: Compulsory Aeronautics: Core Qualification: Elective Compulsory Mechatronics: Core Qualification: Elective Compulsory Technomathematics: Specialisation I. Mathematics: Elective Compulsory Theoretical Mechanical Engineering: Core Qualification: Compulsory Process Engineering: Specialisation Chemical Process Engineering: Elective Compulsory Process Engineering: Specialisation Process Engineering: Elective Compulsory

Course L0576: Numerical Treatment of Ordinary Differential Equations

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Daniel Ruprecht
Language	DE/EN
Cycle	SoSe
Content	Numerical methods for Initial Value Problems single step methods multistep methods stiff problems differential algebraic equations (DAE) of index 1 Numerical methods for Boundary Value Problems multiple shooting method difference methods
Literature	E. Hairer, S. Noersett, G. Wanner: Solving Ordinary Differential Equations I: Nonstiff Problems. E. Hairer, G. Wanner: Solving Ordinary Differential Equations II: Stiff and Differential-Algebraic Problems. D. Griffiths, D. Higham: Numerical Methods for Ordinary Differential Equations.

Course L0582: Numerical Treatment of Ordinary Differential Equations

Typ	Recitation Section (small)
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Daniel Ruprecht
Language	DE/EN
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Module M1083: Discrete Mathematics

Courses

Title	Typ	Hrs/wk	CP
Discrete Mathematics (L1379)	Lecture	4	6
Discrete Mathematics (L1380)	Recitation Section (small)	2	3

Module Responsible	Prof. Matthias Schacht
Admission Requirements	None
Recommended Previous Knowledge	Linear Algebra Geometry Analysis
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students can describe basic concepts in Discrete Mathematics such as elementary combinatorics and counting coefficients, sorting algorithms, graphs and network algorithms, complexity, asymptotic analysis, discrete probability distributions, generating functions, the principle of inclusion and exclusion, ordered sets, counting of trees and patterns and fundamentals in coding theory or cryptography. They are able to explain them using appropriate examples. Students can discuss logical connections between these concepts. They are capable of illustrating these connections with the help of examples. They know proof strategies and can reproduce them.
Skills	Students can model problems in Combinatorics with the help of the concepts studied in this course. Moreover, they are capable of solving them by applying established methods. Students are able to discover and verify further logical connections between the concepts studied in the course. For a given problem, the students can develop and execute a suitable approach, and are able to critically evaluate the results.
Personal Competence
Social Competence	Students are able to work together in teams. They are capable to use mathematics as a common language. In doing so, they can communicate new concepts according to the needs of their cooperating partners. Moreover, they can design examples to check and deepen the understanding of their peers.
Autonomy	Students are capable of checking their understanding of complex concepts on their own. They can specify open questions precisely and know where to get help in solving them. Students have developed sufficient persistence to be able to work for longer periods in a goal-oriented manner on hard problems.
Workload in Hours	Independent Study Time 186, Study Time in Lecture 84
Credit points	9
Course achievement	None
Examination	Oral exam
Examination duration and scale	30 min
Assignment for the Following Curricula	Technomathematics: Specialisation I. Mathematics: Elective Compulsory

Course L1379: Discrete Mathematics

Typ	Lecture
Hrs/wk	4
CP	6
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Lecturer	Dozenten des Fachbereiches Mathematik der UHH
Language	DE/EN
Cycle	SoSe
Content	Introduction to discrete mathematics Topics: Combinatorial problems and counting coefficients Sorting algorithms Fundamentals of graph theory Graph and Network algorithms Complexity Asymptotic analysiy Diskrete probability distributions Generating functions (ring of formal power series) Inclusion and exklusion principle oredered sets (Möbius inversion) Counting of trees and patterns Fundamentals in coding theory or cryptography
Literature	M. Aigner: Diskrete Mathematik, Vieweg, 6., korr. Aufl. 2006 L. Lovász, J. Pelikan & K. Vesztergombi Diskrete Mathematik, Springer, 2005 J. Matoušek & J. Nešetřil: Diskrete Mathematik - Eine Entdeckungsreise, Springer, 2007 A. Steger: Diskrete Strukturen - Band 1: Kombinatorik, Graphentheorie, Algebra, Springer, 2. Aufl. 2007 A. Taraz: Diskrete Mathematik - Grundlagen und Methoden, Birkhäuser, 2012

Course L1380: Discrete Mathematics

Typ	Recitation Section (small)
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Dozenten des Fachbereiches Mathematik der UHH
Language	DE/EN
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Module M1055: Complex Analysis

Courses

Title	Typ	Hrs/wk	CP
Complex Analysis (L1325)	Lecture	4	6
Complex Analysis (L1326)	Recitation Section (small)	2	3

Module Responsible	Prof. Vicente Cortés
Admission Requirements	None
Recommended Previous Knowledge	Analysis Higher Analysis
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students can describe basic concepts in Complex Analysis such as holomorphic functions, Cauchy's integral theorem and formula, the residue theorem, conformal maps, homology and homotopy versions of the residue theorem, analytic functions, Fourier series, harmonic functions, elliptic functions and integrals and the Gamma function. They are able to explain them using appropriate examples. Students can discuss logical connections between these concepts. They are capable of illustrating these connections with the help of examples. They know proof strategies and can reproduce them.
Skills	Students can model problems in Complex Analysis with the help of the concepts studied in this course. Moreover, they are capable of solving them by applying established methods. Students are able to discover and verify further logical connections between the concepts studied in the course. For a given problem, the students can develop and execute a suitable approach, and are able to critically evaluate the results.
Personal Competence
Social Competence	Students are able to work together in teams. They are capable to use mathematics as a common language. In doing so, they can communicate new concepts according to the needs of their cooperating partners. Moreover, they can design examples to check and deepen the understanding of their peers.
Autonomy	Students are capable of checking their understanding of complex concepts on their own. They can specify open questions precisely and know where to get help in solving them. Students have developed sufficient persistence to be able to work for longer periods in a goal-oriented manner on hard problems.
Workload in Hours	Independent Study Time 186, Study Time in Lecture 84
Credit points	9
Course achievement	None
Examination	Oral exam
Examination duration and scale	30 min
Assignment for the Following Curricula	Technomathematics: Specialisation I. Mathematics: Elective Compulsory Technomathematics: Specialisation I. Mathematics: Elective Compulsory

Course L1325: Complex Analysis

Typ	Lecture
Hrs/wk	4
CP	6
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Lecturer	Dozenten des Fachbereiches Mathematik der UHH
Language	DE/EN
Cycle	SoSe
Content	complex numbers, sequences and series of complex numbers (recapitulation) real and complex differentiation of complex-valued functions, Wirtinger calculus holomorphic functions Cauchy's integral theorem, Cauchy's integral formula, residue theorem determination of improper (real) integrals via complex methods conformal maps homology and homotopy versions of the residue theorem Maximum principle Counting of zeros and poles Proofs of the fundamental theorem of algebra analytic functions Fourier series harmonic functions The Mittag-Leffler theorem and the Weierstraß factorization theorem Elliptic funktions and integrals Gamma function
Literature	W. Fischer, I. Lieb, Einführung in die komplexe Analysis, Verlag: Vieweg+Teubner Verlag; Auflage: 2010 Dietmar A. Salamon, Funktionentheorie, Verlag: Springer Basel; Auflage: 2012 K. Fritzsche, Grundkurs Funktionentheorie, Verlag: Spektrum Akademischer Verlag; Auflage: 2009 E. Freitag, R. Busam, Funktionentheorie 1, Verlag: Springer Berlin Heidelberg, 2002 R. Remmert, G. Schumacher, Funktionentheorie 1, Verlag: Springer Berlin Heidelberg, 2002 L.V. Ahlfors, Complex Analysis, Publisher: McGraw-Hill Science/Engineering/Math; 3 edition (January 1, 1979) J.B. Conway, Functions of one complex variable, Springer, 1978

Course L1326: Complex Analysis

Typ	Recitation Section (small)
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Dozenten des Fachbereiches Mathematik der UHH
Language	DE/EN
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Module M1958: Risk Theory

Courses

Title	Typ	Hrs/wk	CP
Risk Theory (L3191)	Lecture	2	4
Risk Theory (L3192)	Recitation Section (small)	1	2

Module Responsible	Prof. Holger Drees
Admission Requirements	None
Recommended Previous Knowledge
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge
Skills
Personal Competence
Social Competence
Autonomy
Workload in Hours	Independent Study Time 138, Study Time in Lecture 42
Credit points	6
Course achievement	None
Examination	Oral exam
Examination duration and scale	30 min
Assignment for the Following Curricula	Technomathematics: Specialisation I. Mathematics: Elective Compulsory

Course L3191: Risk Theory

Typ	Lecture
Hrs/wk	2
CP	4
Workload in Hours	Independent Study Time 92, Study Time in Lecture 28
Lecturer	Prof. Holger Drees
Language	DE
Cycle	WiSe
Content
Literature	Literatur: Es wird ein ausführliches Skript zur Vorlesung jeweils kapitelweise im Vorhinein zur Verfügung gestellt werden. Ergänzende und weiterführende Literatur: H. Föllmer und A. Schied (2011). Stochastic Finance (3rd ed.). De Gruyter. R. Kaas, M. Goovaerts, J. Dhaene und M. Denuit (2008). Modern Actuarial Risk Theory (2nd ed.), Springer. T. Mikosch (2003). Non-Life Insurance Mathematics: an Introduction with Stochastic Processes. Springer. K.D. Schmidt (2002). Versicherungsmathematik. Springer. B. Sundt (1994). An Introduction to Non-Life Insurance. Verlag Versicherungswirtschaft.

Course L3192: Risk Theory

Typ	Recitation Section (small)
Hrs/wk	1
CP	2
Workload in Hours	Independent Study Time 46, Study Time in Lecture 14
Lecturer	Prof. Holger Drees
Language	DE
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Module M1552: Advanced Machine Learning

Courses

Title	Typ	Hrs/wk	CP
Advanced Machine Learning (L2322)	Lecture	2	3
Advanced Machine Learning (L2323)	Recitation Section (small)	2	3

Module Responsible	Dr. Jens-Peter Zemke
Admission Requirements	None
Recommended Previous Knowledge	Mathematics I-III Numerical Mathematics 1/ Numerics Programming skills, preferably in Python
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students are able to name, state and classify state-of-the-art neural networks and their corresponding mathematical basics. They can assess the difficulties of different neural networks.
Skills	Students are able to implement, understand, and, tailored to the field of application, apply neural networks.
Personal Competence
Social Competence	Students can develop and document joint solutions in small teams; form groups to further develop the ideas and transfer them to other areas of applicability; form a team to develop, build, and advance a software library.
Autonomy	Students are able to correctly assess the time and effort of self-defined work; assess whether the supporting theoretical and practical excercises are better solved individually or in a team; define test problems for testing and expanding the methods; assess their individual progess and, if necessary, to ask questions and seek help.
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Credit points	6
Course achievement	None
Examination	Written exam
Examination duration and scale	90 min
Assignment for the Following Curricula	Computer Science: Specialisation III. Mathematics: Elective Compulsory Data Science: Core Qualification: Compulsory Computer Science in Engineering: Specialisation III. Mathematics: Elective Compulsory Interdisciplinary Mathematics: Specialisation Computational Methods in Biomedical Imaging: Compulsory Mechatronics: Core Qualification: Elective Compulsory Technomathematics: Specialisation I. Mathematics: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: Elective Compulsory

Course L2322: Advanced Machine Learning

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Dr. Jens-Peter Zemke
Language	EN
Cycle	WiSe
Content	Basics: analogy; layout of neural nets, universal approximation, NP-completeness Feedforward Neural Networks: backpropagation, variants of Stochastistic Gradients Convolutional Neural Networks: idea, layout, FFT and Winograds algorithms, implementation details Adversarial Attacks Recurrent Neural Networks: idea, dynamical systems, training, LSTM Residual Neural Networks Neural ODEs Autoencoder and Generative Adversarial Networks Attention and Transformers
Literature	Skript Online-Werke: http://neuralnetworksanddeeplearning.com/ https://www.deeplearningbook.org/

Course L2323: Advanced Machine Learning

Typ	Recitation Section (small)
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Dr. Jens-Peter Zemke
Language	EN
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Module M1966: Mathematical Image Processing

Courses

Title	Typ	Hrs/wk	CP
Mathematical Image Processing (L0991)	Lecture	3	4
Mathematical Image Processing (L0992)	Recitation Section (small)	1	2

Module Responsible	Prof. Marko Lindner
Admission Requirements	None
Recommended Previous Knowledge	Analysis: partial derivatives, gradient, directional derivative Linear Algebra: eigenvalues, least squares solution of a linear system
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students are able to characterize and compare diffusion equations explain elementary methods of image processing explain methods of image segmentation and registration sketch and interrelate basic concepts of functional analysis
Skills	Students are able to implement and apply elementary methods of image processing explain and apply modern methods of image processing
Personal Competence
Social Competence	Students are able to work together in heterogeneously composed teams (i.e., teams from different study programs and background knowledge) and to explain theoretical foundations.
Autonomy	Students are capable of checking their understanding of complex concepts on their own. They can specify open questions precisely and know where to get help in solving them. Students have developed sufficient persistence to be able to work for longer periods in a goal-oriented manner on hard problems.
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Credit points	6
Course achievement	None
Examination	Written exam
Examination duration and scale	20 min
Assignment for the Following Curricula	Bioprocess Engineering: Specialisation A - General Bioprocess Engineering: Elective Compulsory Computer Science: Specialisation III. Mathematics: Elective Compulsory Computer Science in Engineering: Specialisation III. Mathematics: Elective Compulsory Interdisciplinary Mathematics: Specialisation Computational Methods in Biomedical Imaging: Compulsory Mechatronics: Core Qualification: Elective Compulsory Technomathematics: Specialisation I. Mathematics: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: Elective Compulsory Process Engineering: Specialisation Process Engineering: Elective Compulsory

Course L0991: Mathematical Image Processing

Typ	Lecture
Hrs/wk	3
CP	4
Workload in Hours	Independent Study Time 78, Study Time in Lecture 42
Lecturer	Prof. Marko Lindner
Language	DE/EN
Cycle	WiSe
Content	basic methods of image processing smoothing filters the diffusion / heat equation variational formulations in image processing edge detection de-convolution inpainting image segmentation image registration
Literature	Bredies/Lorenz: Mathematische Bildverarbeitung

Course L0992: Mathematical Image Processing

Typ	Recitation Section (small)
Hrs/wk	1
CP	2
Workload in Hours	Independent Study Time 46, Study Time in Lecture 14
Lecturer	Prof. Marko Lindner
Language	DE/EN
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Module M1973: Numerical Methods for Partial Differential Equations

Courses

Title	Typ	Hrs/wk	CP
Numerics of Partial Differential Equations (L1247)	Lecture	2	3
Numerics of Partial Differential Equations (L1248)	Recitation Section (small)	2	3

Module Responsible

Prof. Daniel Ruprecht

Admission Requirements

None

Recommended Previous Knowledge

Mathematik I - IV (for Engineering Students) or Analysis & Linear Algebra I + II for Technomathematicians
Numerical mathematics 1
Numerical methods for ordinary differential equations
Basic knowledge of MATLAB, Python or a similar programming language.

Educational Objectives

After taking part successfully, students have reached the following learning results

Professional Competence

Knowledge

Students can classify partial differential equations according to the three basic types.
They know typical numerical methods like finite differences or finite volumes.
Students know the theoretical convergence results and other important properties of these methods.

Skills

Students are capable of formulating solution strategies for given partial differential equations, can comment on theoretical properties regarding convergence and are able to implement and test these methods.

Personal Competence

Social Competence

Students are able of working together in heterogeneous teams (i.e., teams from different study programs and background knowledge) and to explain theoretical foundations.

Autonomy

Students are capable of checking their understanding of complex concepts on their own. They can specify open questions precisely and know where to get help in solving them.
Students have developed sufficient mental stamina to work on hard problems for an extended period of time

Workload in Hours

Independent Study Time 124, Study Time in Lecture 56

Credit points

Course achievement

Compulsory	Bonus	Form	Description
Yes	None	Excercises	Regelmässige Bearbeitung von Übungsaufgaben und aktive Teilnahme an den Übungsgruppen

Examination

Oral exam

Examination duration and scale

30 min

Assignment for the Following Curricula

Computer Science: Specialisation III. Mathematics: Elective Compulsory
Technomathematics: Specialisation I. Mathematics: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Simulation Technology: Elective Compulsory

Course L1247: Numerics of Partial Differential Equations

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Daniel Ruprecht
Language	DE/EN
Cycle	WiSe
Content	Elementary Theory and Numerics of PDEs types of PDEs well posed problems finite differences finite volumes applications
Literature	Dale R. Durran: Numerical Methods for Fluid Dynamics. Randall J. LeVeque: Numerical Methods for Conservation Laws.

Course L1248: Numerics of Partial Differential Equations

Typ	Recitation Section (small)
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Daniel Ruprecht
Language	DE/EN
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Module M0716: Hierarchical Algorithms

Courses

Title	Typ	Hrs/wk	CP
Hierarchical Algorithms (L0585)	Lecture	2	3
Hierarchical Algorithms (L0586)	Recitation Section (small)	2	3

Module Responsible	Prof. Sabine Le Borne
Admission Requirements	None
Recommended Previous Knowledge	Mathematics I, II, III for Engineering students (german or english) or Analysis & Linear Algebra I + II as well as Analysis III for Technomathematicians Programming experience in C
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students are able to name representatives of hierarchical algorithms and list their characteristics, explain construction techniques for hierarchical algorithms, discuss aspects regarding the efficient implementation of hierarchical algorithms.
Skills	Students are able to implement the hierarchical algorithms discussed in the lecture, analyse the storage and computational complexities of the algorithms, adapt algorithms to problem settings of various applications and thus develop problem adapted variants.
Personal Competence
Social Competence	Students are able to work together in heterogeneously composed teams (i.e., teams from different study programs and background knowledge), explain theoretical foundations and support each other with practical aspects regarding the implementation of algorithms.
Autonomy	Students are capable to assess whether the supporting theoretical and practical excercises are better solved individually or in a team, to work on complex problems over an extended period of time, to assess their individual progess and, if necessary, to ask questions and seek help.
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Credit points	6
Course achievement	None
Examination	Oral exam
Examination duration and scale	20 min
Assignment for the Following Curricula	Computer Science: Specialisation III. Mathematics: Elective Compulsory Data Science: Specialisation I. Mathematics: Elective Compulsory Data Science: Specialisation IV. Special Focus Area: Elective Compulsory Technomathematics: Specialisation I. Mathematics: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Simulation Technology: Elective Compulsory

Course L0585: Hierarchical Algorithms

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Sabine Le Borne
Language	EN
Cycle	WiSe
Content	Low rank matrices Separable expansions Hierarchical matrix partitions Hierarchical matrices Formatted matrix operations H2 matrices Applications Additional topics (e.g. matrix functions, tensor products)
Literature	W. Hackbusch: Hierarchical matrices: Algorithms and Analysis, Springer (2015)

Course L0586: Hierarchical Algorithms

Typ	Recitation Section (small)
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Sabine Le Borne
Language	EN
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Module M1063: Stochastic Processes

Courses

Title	Typ	Hrs/wk	CP
Stochastic Processes (L1343)	Lecture	3	4
Stochastic Processes (L1344)	Recitation Section (small)	1	2

Module Responsible	Prof. Holger Drees
Admission Requirements	None
Recommended Previous Knowledge	Mathematical Stochastics Measure Theory and Stochastics
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students can describe basic concepts such as the classification and construction of stochastic processes, Markov processes with discrete state space in discrete and continuous time, renewal theory, general Markov processes and Markov semigroups, Poisson processes and Brownian motion. They are able to explain them using appropriate examples. Students can discuss logical connections between these concepts. They are capable of illustrating these connections with the help of examples. They know proof strategies and can reproduce them.
Skills	Students can model problems in Stochastic Processes with the help of the concepts studied in this course. Moreover, they are capable of solving them by applying established methods. Students are able to discover and verify further logical connections between the concepts studied in the course. For a given problem, the students can develop and execute a suitable approach, and are able to critically evaluate the results.
Personal Competence
Social Competence	Students are able to work together in teams. They are capable to use mathematics as a common language. In doing so, they can communicate new concepts according to the needs of their cooperating partners. Moreover, they can design examples to check and deepen the understanding of their peers.
Autonomy	Students are capable of checking their understanding of complex concepts on their own. They can specify open questions precisely and know where to get help in solving them. Students have developed sufficient persistence to be able to work for longer periods in a goal-oriented manner on hard problems.
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Credit points	6
Course achievement	None
Examination	Oral exam
Examination duration and scale	30 min
Assignment for the Following Curricula	Technomathematics: Specialisation I. Mathematics: Elective Compulsory

Course L1343: Stochastic Processes

Typ	Lecture
Hrs/wk	3
CP	4
Workload in Hours	Independent Study Time 78, Study Time in Lecture 42
Lecturer	Dozenten des Fachbereiches Mathematik der UHH
Language	DE
Cycle	WiSe
Content	Classification and construction of stochastic processes, existence theorems Markov processes with discrete state space in discrete and continuous time Renewal theory General Markov processes and Markov semigroups Poisson processes, Brownian motion
Literature	Asmussen, S.: Applied Probability and Queues, 2.ed., Springer, New York 2003 Chung, K.L.: Markov Chains, 2.ed., Springer Berlin 1967 Grimmett, G.; Stirzaker, D.R.: Probability and Random Processes, 3.ed., Oxford University Press, Oxford 2009 Karlin, S.; Taylor, H.M.: A First Course in Stochastic Processes, 2.ed., Academic Press, New York 1975 Resnick, S.I.: Adventures in Stochastic Processes, 2.pr., Birkhäuser, Boston 1994 Stroock, D.W.: An Introduction to Markov Processes, Springer, New York 2005

Course L1344: Stochastic Processes

Typ	Recitation Section (small)
Hrs/wk	1
CP	2
Workload in Hours	Independent Study Time 46, Study Time in Lecture 14
Lecturer	Dozenten des Fachbereiches Mathematik der UHH
Language	DE
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Module M1059: Approximation

Courses

Title	Typ	Hrs/wk	CP
Approximation (L1331)	Lecture	4	6
Approximation (L1332)	Recitation Section (small)	2	3

Module Responsible	Prof. Armin Iske
Admission Requirements	None
Recommended Previous Knowledge	Linear Algebra Analysis Introduction to Numerical Analysis
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students can describe basic concepts in Approximation such as L² approximation, Tschebychev approximation and Remez methods, approximation of periodic functions, Fourier series, splines, representation of curves and surfaces, and wavelets and radial basis function. They are able to explain them using appropriate examples. Students can discuss logical connections between these concepts. They are capable of illustrating these connections with the help of examples. They know proof strategies and can reproduce them.
Skills	Students can model problems in Approximation with the help of the concepts studied in this course. Moreover, they are capable of solving them by applying established methods. Students are able to discover and verify further logical connections between the concepts studied in the course. For a given problem, the students can develop and execute a suitable approach, and are able to critically evaluate the results.
Personal Competence
Social Competence	Students are able to work together in teams. They are capable to use mathematics as a common language. In doing so, they can communicate new concepts according to the needs of their cooperating partners. Moreover, they can design examples to check and deepen the understanding of their peers.
Autonomy	Students are capable of checking their understanding of complex concepts on their own. They can specify open questions precisely and know where to get help in solving them. Students have developed sufficient persistence to be able to work for longer periods in a goal-oriented manner on hard problems.
Workload in Hours	Independent Study Time 186, Study Time in Lecture 84
Credit points	9
Course achievement	None
Examination	Oral exam
Examination duration and scale	30 min
Assignment for the Following Curricula	Technomathematics: Specialisation I. Mathematics: Elective Compulsory

Course L1331: Approximation

Typ	Lecture
Hrs/wk	4
CP	6
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Lecturer	Dozenten des Fachbereiches Mathematik der UHH
Language	DE
Cycle	WiSe
Content	L² approximation Tschebychev approximation and Remez methods Approximation of periodic functions, Fourier series Interpolation and approximation by splines Representation of curves and surfaces Wavelets and radial basis functions
Literature	DeVore, Ronald A. und Lorentz, George G.: Constructive Approximation, Springer, 1993. Powell, Michael J. D.: Approximation theory and methods, Cambridge University Press, 1981. Cheney, Elliot W. und Light, William A.: A course in approximation theory, Brooks/Cole Publishing, 2000.

Course L1332: Approximation

Typ	Recitation Section (small)
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Dozenten des Fachbereiches Mathematik der UHH
Language	DE
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Module M1058: Introduction to Mathematical Modeling

Courses

Title	Typ	Hrs/wk	CP
Introduction in Mathematical Modeling (L1329)	Lecture	4	6
Introduction in Mathematical Modeling (L1330)	Recitation Section (small)	2	3

Module Responsible	Prof. Ingenuin Gasser
Admission Requirements	None
Recommended Previous Knowledge	Analysis Linear Algebra
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students can describe basic concepts in Mathematical Modeling such as he modelling process, deterministic and stochastic models, modelling of dynamic processes, and discrete and continuous models. They are able to explain them using appropriate examples. Students can discuss logical connections between these concepts. They are capable of illustrating these connections with the help of examples. They know proof strategies and can reproduce them.
Skills	Students can model problems in Mathematical Modeling with the help of the concepts studied in this course. Moreover, they are capable of solving them by applying established methods. Students are able to discover and verify further logical connections between the concepts studied in the course. For a given problem, the students can develop and execute a suitable approach, and are able to critically evaluate the results.
Personal Competence
Social Competence	Students are able to work together in teams. They are capable to use mathematics as a common language. In doing so, they can communicate new concepts according to the needs of their cooperating partners. Moreover, they can design examples to check and deepen the understanding of their peers.
Autonomy	Students are capable of checking their understanding of complex concepts on their own. They can specify open questions precisely and know where to get help in solving them. Students have developed sufficient persistence to be able to work for longer periods in a goal-oriented manner on hard problems.
Workload in Hours	Independent Study Time 186, Study Time in Lecture 84
Credit points	9
Course achievement	None
Examination	Oral exam
Examination duration and scale	30 min
Assignment for the Following Curricula	Technomathematics: Specialisation I. Mathematics: Elective Compulsory

Course L1329: Introduction in Mathematical Modeling

Typ	Lecture
Hrs/wk	4
CP	6
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Lecturer	Dozenten des Fachbereiches Mathematik der UHH
Language	DE
Cycle	WiSe
Content	The modelling process deterministic and stochastic models modelling of dynamic processes discrete and continuous models
Literature	C.P. Ortlieb, C. v. Dresky, I. Gasser, S. Günzel : Mathematische Modellierung - Eine Einführung in zwölf Fallstudien, 2. Auflage, Vieweg+Teubner (2012) Richard Haberman : Mathematical Models: Mechanical Vibrations, Population Dynamics, and Traffic Flow. Classics in Mathematics 21, SIAM (1998). C. C. Lin und L. A. Segal: Mathematics Applied to Deterministic Problems in the natural Sciences, SIAM (1988) C. Eck, H. Garcke, P. Knabner: Mathematische Modellierung. Springer (2008)

Course L1330: Introduction in Mathematical Modeling

Typ	Recitation Section (small)
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Dozenten des Fachbereiches Mathematik der UHH
Language	DE
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Module M1078: Geometry

Courses

Title	Typ	Hrs/wk	CP
Geometry (L1363)	Lecture	4	6
Geometry (L1364)	Recitation Section (small)	2	3

Module Responsible	Prof. Alexander Kreuzer
Admission Requirements	None
Recommended Previous Knowledge	Linear Algebra
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students can describe basic concepts in Geometry such as affine and projective planes and spaces, coordinatisation, collineations, fundamental theorems and applications of geometry. They are able to explain them using appropriate examples. Students can discuss logical connections between these concepts. They are capable of illustrating these connections with the help of examples. They know proof strategies and can reproduce them.
Skills	Students can model problems in Geometry with the help of the concepts studied in this course. Moreover, they are capable of solving them by applying established methods. Students are able to discover and verify further logical connections between the concepts studied in the course. For a given problem, the students can develop and execute a suitable approach, and are able to critically evaluate the results.
Personal Competence
Social Competence	Students are able to work together in teams. They are capable to use mathematics as a common language. In doing so, they can communicate new concepts according to the needs of their cooperating partners. Moreover, they can design examples to check and deepen the understanding of their peers.
Autonomy	Students are capable of checking their understanding of complex concepts on their own. They can specify open questions precisely and know where to get help in solving them. Students have developed sufficient persistence to be able to work for longer periods in a goal-oriented manner on hard problems.
Workload in Hours	Independent Study Time 186, Study Time in Lecture 84
Credit points	9
Course achievement	None
Examination	Oral exam
Examination duration and scale	30 min
Assignment for the Following Curricula	Technomathematics: Specialisation I. Mathematics: Elective Compulsory

Course L1363: Geometry

Typ	Lecture
Hrs/wk	4
CP	6
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Lecturer	Dozenten des Fachbereiches Mathematik der UHH
Language	DE
Cycle	WiSe
Content	Affine and projective planes and spaces Coordinatisation Collineations Fundamental theorems Applications of geometry
Literature	M. Berger, Geometry I, Verlag: Springer, 1987 A. Beutelspacher und U. Rosenbaum, Projektive Geometrie, Verlag Vieweg, 1992 H. Brauner, Geometrie projektiver Räume I, II, BI, 1976 F. Buckenhout (Hrsg.), Handbook of Incidence Geometry, Verlag: Elsevier, 1995 R. Casse, Projective Geometry: An Introduction, Verlag: Oxford University Press, 2009 A. Herzer, Geometrie I,II, Skript, Universität Mainz, 1991/92 A. Holme, Geometry: Our Cultural Heritage, Verlag: Springer, 2002 D.R. Hughes und F.C. Piper, Projective Planes, Verlag: Springer, 1973 G.A. Jennings, Modern Geometry with Applications, Verlag: Springer, 1994 L. Kadison und M.T. Kromann, Projective Geometry and Modern Algebra, Verlag: Birkhäuser , 1996 H. Karzel und H.-J. Kroll, Geschichte der Geometrie seit Hilbert, Verlag: Wiss. Buchgesellschaft, 1988 H. Karzel, K. Sörensen und D. Windelberg, Einführung in die Geometrie, Verlag: Vandenhoeck und Rupprecht, 1973 H. Lenz, Vorlesungen über projektive Geometrie, Akad. Verl.-Ges., 1965 R. Lingenberg, Grundlagen der Geometrie, BI, 1978 E.M. Schröder, Vorlesungen über Geometrie, II, BI., 1991 C.J. Scriba und P. Schreiber, 5000 Jahre Geometrie, Verlag: Springer, 2001 J. Ueberberg, Foundations of Incidence Geometry: Projective and Polar Spaches, Verlag: Springer, 2011

Course L1364: Geometry

Typ	Recitation Section (small)
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Dozenten des Fachbereiches Mathematik der UHH
Language	DE
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Module M0941: Combinatorial Structures and Algorithms

Courses

Title	Typ	Hrs/wk	CP
Combinatorial Structures and Algorithms (L1100)	Lecture	3	4
Combinatorial Structures and Algorithms (L1101)	Recitation Section (small)	1	2

Module Responsible	Prof. Anusch Taraz
Admission Requirements	None
Recommended Previous Knowledge	Mathematics I + II Discrete Algebraic Structures Graph Theory and Optimization
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students can name the basic concepts in Combinatorics and Algorithms. They are able to explain them using appropriate examples. Students can discuss logical connections between these concepts. They are capable of illustrating these connections with the help of examples. They know proof strategies and can reproduce them.
Skills	Students can model problems in Combinatorics and Algorithms with the help of the concepts studied in this course. Moreover, they are capable of solving them by applying established methods. Students are able to discover and verify further logical connections between the concepts studied in the course. For a given problem, the students can develop and execute a suitable approach, and are able to critically evaluate the results.
Personal Competence
Social Competence	Students are able to work together in teams. They are capable to use mathematics as a common language. In doing so, they can communicate new concepts according to the needs of their cooperating partners. Moreover, they can design examples to check and deepen the understanding of their peers.
Autonomy	Students are capable of checking their understanding of complex concepts on their own. They can specify open questions precisely and know where to get help in solving them. Students have developed sufficient persistence to be able to work for longer periods in a goal-oriented manner on hard problems.
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Credit points	6
Course achievement	None
Examination	Oral exam
Examination duration and scale	30 min
Assignment for the Following Curricula	Computer Science: Specialisation II. Mathematics and Engineering Science: Elective Compulsory Data Science: Specialisation I. Mathematics/Computer Science: Elective Compulsory Computer Science in Engineering: Specialisation II. Mathematics & Engineering Science: Elective Compulsory Technomathematics: Specialisation I. Mathematics: Elective Compulsory

Course L1100: Combinatorial Structures and Algorithms

Typ	Lecture
Hrs/wk	3
CP	4
Workload in Hours	Independent Study Time 78, Study Time in Lecture 42
Lecturer	Prof. Anusch Taraz, Dr. Dennis Clemens
Language	DE/EN
Cycle	WiSe
Content	Counting Structural Graph Theory Analysis of Algorithms Extremal Combinatorics Random discrete structures
Literature	M. Aigner: Diskrete Mathematik, Vieweg, 6. Aufl., 2006 J. Matoušek & J. Nešetřil: Diskrete Mathematik - Eine Entdeckungsreise, Springer, 2007 A. Steger: Diskrete Strukturen - Band 1: Kombinatorik, Graphentheorie, Algebra, Springer, 2. Aufl. 2007 A. Taraz: Diskrete Mathematik, Birkhäuser, 2012.

Course L1101: Combinatorial Structures and Algorithms

Typ	Recitation Section (small)
Hrs/wk	1
CP	2
Workload in Hours	Independent Study Time 46, Study Time in Lecture 14
Lecturer	Prof. Anusch Taraz
Language	DE/EN
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Module M1050: Graph Theory

Courses

Title	Typ	Hrs/wk	CP
Graph Theory (L1311)	Lecture	4	6
Graph Theory (L1314)	Recitation Section (small)	2	3

Module Responsible	Prof. Reinhard Diestel
Admission Requirements	None
Recommended Previous Knowledge	Linear Algebra
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students can describe basic concepts in Graph Theory such as connectivity, matchings, planarity, colourings, infinite graphs, spanning structures and Ramsey theory. They are able to explain them using appropriate examples. Students can discuss logical connections between these concepts. They are capable of illustrating these connections with the help of examples. They know proof strategies and can reproduce them.
Skills	Students can model problems in Graph Theory with the help of the concepts studied in this course. Moreover, they are capable of solving them by applying established methods. Students are able to discover and verify further logical connections between the concepts studied in the course. For a given problem, the students can develop and execute a suitable approach, and are able to critically evaluate the results.
Personal Competence
Social Competence	Students are able to work together in teams. They are capable to use mathematics as a common language. In doing so, they can communicate new concepts according to the needs of their cooperating partners. Moreover, they can design examples to check and deepen the understanding of their peers.
Autonomy	Students are capable of checking their understanding of complex concepts on their own. They can specify open questions precisely and know where to get help in solving them. Students have developed sufficient persistence to be able to work for longer periods in a goal-oriented manner on hard problems.
Workload in Hours	Independent Study Time 186, Study Time in Lecture 84
Credit points	9
Course achievement	None
Examination	Oral exam
Examination duration and scale	30 min
Assignment for the Following Curricula	Technomathematics: Specialisation I. Mathematics: Elective Compulsory

Course L1311: Graph Theory

Typ	Lecture
Hrs/wk	4
CP	6
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Lecturer	Dozenten des Fachbereiches Mathematik der UHH
Language	DE
Cycle	WiSe
Content	Fundamentals of Graph Theory, important invariants and their relations Topics: Matchings Connectivity Planar graphs Graph coloring Subgraphs and infinite Graphs Ramsey theory Hamilton cycles Random graphs
Literature	R.Diestel, Graphentheorie (4. Auflage), Springer 2010 R.Diestel, Graph Theory (4th ed'n), GTM 173, Springer 2010/12

Course L1314: Graph Theory

Typ	Recitation Section (small)
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Dozenten des Fachbereiches Mathematik der UHH
Language	DE
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Module M1051: Combinatorial Optimization

Courses

Title	Typ	Hrs/wk	CP
Combinatorial Optimization (L1315)	Lecture	4	6
Combinatorial Optimization (L1316)	Recitation Section (small)	2	3

Module Responsible	Prof. Matthias Schacht
Admission Requirements	None
Recommended Previous Knowledge	Linear Algebra, Discrete Mathematics
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students can describe basic concepts in Combinatorial Optimization such as network algorithms, linear programming and duality, polyhedral combinatorics and NP-complexity theory They are able to explain them using appropriate examples. Students can discuss logical connections between these concepts. They are capable of illustrating these connections with the help of examples. They know proof strategies and can reproduce them.
Skills	Students can model problems in Combinatorial Optimization with the help of the concepts studied in this course. Moreover, they are capable of solving them by applying established methods. Students are able to discover and verify further logical connections between the concepts studied in the course. For a given problem, the students can develop and execute a suitable approach, and are able to critically evaluate the results.
Personal Competence
Social Competence	Students are able to work together in teams. They are capable to use mathematics as a common language. In doing so, they can communicate new concepts according to the needs of their cooperating partners. Moreover, they can design examples to check and deepen the understanding of their peers.
Autonomy	Students are capable of checking their understanding of complex concepts on their own. They can specify open questions precisely and know where to get help in solving them. Students have developed sufficient persistence to be able to work for longer periods in a goal-oriented manner on hard problems.
Workload in Hours	Independent Study Time 186, Study Time in Lecture 84
Credit points	9
Course achievement	None
Examination	Oral exam
Examination duration and scale	30 min
Assignment for the Following Curricula	Technomathematics: Specialisation I. Mathematics: Elective Compulsory

Course L1315: Combinatorial Optimization

Typ	Lecture
Hrs/wk	4
CP	6
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Lecturer	Dozenten des Fachbereiches Mathematik der UHH
Language	DE
Cycle	WiSe/SoSe
Content	Introduction to combinatorial optimization Topics: Linear optimization: Polyhedra and LP Duality Complexity of algorithms polynomial algorithms for minimal spanning trees shortest paths maximum flows and minimum cost flows maximum matching and linear programs polyhedral combinatorics for NP-hard problems (Knapsack, TSP, Clique Partioning)
Literature	William J. Cook, William H. Cunningham, William R. Pulleyblank, Alexander Schrijver: Combinatorial Optimization. John Wiley & Sons, 1997 Christos H. Papadimitriou, Kenneth Steiglitz: Combinatorial Optimization: Algorithms and Complexity. Dover Publications, 1998 Eugene Lawler: Combinatorial Optimization: Networks and Matroids, Oxford University Press 1995

Course L1316: Combinatorial Optimization

Typ	Recitation Section (small)
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Dozenten des Fachbereiches Mathematik der UHH
Language	DE
Cycle	WiSe/SoSe
Content	See interlocking course
Literature	See interlocking course

Module M0720: Matrix Algorithms

Courses

Title	Typ	Hrs/wk	CP
Matrix Algorithms (L0984)	Lecture	2	3
Matrix Algorithms (L0985)	Recitation Section (small)	2	3

Module Responsible	Dr. Jens-Peter Zemke
Admission Requirements	None
Recommended Previous Knowledge	Mathematics I - III Numerical Mathematics 1/ Numerics Basic knowledge of the programming language Python
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students are able to name, state and classify state-of-the-art Krylov subspace methods for the solution of the core problems of the engineering sciences, namely, eigenvalue problems, solution of linear systems, and model reduction; state approaches for the solution of matrix equations (Sylvester, Lyapunov, Riccati).
Skills	Students are capable to implement and assess basic Krylov subspace methods for the solution of eigenvalue problems, linear systems, and model reduction; assess methods used in modern software with respect to computing time, stability, and domain of applicability; adapt the approaches learned to new, unknown types of problem.
Personal Competence
Social Competence	Students can develop and document joint solutions in small teams; form groups to further develop the ideas and transfer them to other areas of applicability; form a team to develop, build, and advance a software library.
Autonomy	Students are able to correctly assess the time and effort of self-defined work; assess whether the supporting theoretical and practical excercises are better solved individually or in a team; define test problems for testing and expanding the methods; assess their individual progess and, if necessary, to ask questions and seek help.
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Credit points	6
Course achievement	None
Examination	Oral exam
Examination duration and scale	25 min
Assignment for the Following Curricula	Data Science: Specialisation IV. Special Focus Area: Elective Compulsory Data Science: Specialisation I. Mathematics: Elective Compulsory Mechatronics: Core Qualification: Elective Compulsory Mechatronics: Technical Complementary Course: Elective Compulsory Technomathematics: Specialisation I. Mathematics: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Simulation Technology: Elective Compulsory

Course L0984: Matrix Algorithms

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Dr. Jens-Peter Zemke
Language	EN
Cycle	WiSe
Content	Part A: Krylov Subspace Methods: Basics (derivation, basis, Ritz, OR, MR) Arnoldi-based methods (Arnoldi, GMRes) Lanczos-based methods (Lanczos, CG, BiCG, QMR, SymmLQ, PvL) Sonneveld-based methods (IDR, BiCGStab, TFQMR, IDR(s)) Part B: Matrix Equations: Sylvester Equation Lyapunov Equation Algebraic Riccati Equation
Literature	Skript (224 Seiten) Ergänzend können die folgenden Lehrbücher herangezogen werden: Saad, Yousef. Numerical methods for large eigenvalue problems: revised edition. Society for Industrial and Applied Mathematics, 2011. Saad, Yousef. Iterative methods for sparse linear systems. Society for Industrial and Applied Mathematics, 2003. Van der Vorst, Henk A. Iterative Krylov methods for large linear systems. No. 13. Cambridge University Press, 2003. Liesen, Jörg, and Zdenek Strakos. Krylov subspace methods: principles and analysis. Oxford University Press, 2013.

Course L0985: Matrix Algorithms

Typ	Recitation Section (small)
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Dr. Jens-Peter Zemke
Language	EN
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Module M1592: Statistics

Courses

Title	Typ	Hrs/wk	CP
Statistics (L2430)	Lecture	3	4
Statistics (L3229)	Project-/problem-based Learning	1	1
Statistics (L2431)	Recitation Section (small)	1	1

Module Responsible

Prof. Matthias Schulte

Admission Requirements

None

Recommended Previous Knowledge

Stochastics (or a comparable class)

Educational Objectives

After taking part successfully, students have reached the following learning results

Professional Competence

Knowledge

Students can name the basic concepts in Statistics. They are able to explain them using appropriate examples.
Students can discuss logical connections between these concepts. They are capable of illustrating these connections with the help of examples.

Skills

Students can model statistical problems with the help of the concepts studied in this course. Moreover, they are capable of solving them by applying established methods. They are able to use the statistical software R.
Students are able to discover and verify further logical connections between the concepts studied in the course.
For a given problem, the students can develop and execute a suitable approach, and are able to critically evaluate the results.

Personal Competence

Social Competence

Students are able to work together (e.g. on their regular home work) in heterogeneously composed teams and to present their results appropriately (e.g. during exercise class).
In doing so, they can communicate new concepts according to the needs of their cooperating partners. Moreover, they can design examples to check and deepen the understanding of their peers.

Autonomy

Students are capable of checking their understanding of complex concepts on their own. They can specify open questions precisely and know where to get help in solving them.
Students can put their knowledge in relation to the contents of other lectures.
Students have developed sufficient persistence to be able to work for longer periods in a goal-oriented manner on hard problems.

Workload in Hours

Independent Study Time 110, Study Time in Lecture 70

Credit points

Course achievement

Compulsory	Bonus	Form	Description
No	10 %	Excercises

Examination

Written exam

Examination duration and scale

90 min

Assignment for the Following Curricula

General Engineering Science (German program, 7 semester): Specialisation Advanced Materials: Elective Compulsory
General Engineering Science (German program, 7 semester): Specialisation Computer Science: Elective Compulsory
General Engineering Science (German program, 7 semester): Specialisation Data Science: Compulsory
Computer Science: Specialisation II. Mathematics and Engineering Science: Elective Compulsory
Data Science: Core Qualification: Compulsory
Engineering Science: Specialisation Advanced Materials: Elective Compulsory
Engineering Science: Specialisation Data Science: Compulsory
Engineering Science: Specialisation Information and Communication Systems: Compulsory
Logistics and Mobility: Specialisation Information Technology: Elective Compulsory
Technomathematics: Specialisation I. Mathematics: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: Elective Compulsory
Engineering and Management - Major in Logistics and Mobility: Specialisation II. Information Technology: Elective Compulsory

Course L2430: Statistics

Typ	Lecture
Hrs/wk	3
CP	4
Workload in Hours	Independent Study Time 78, Study Time in Lecture 42
Lecturer	Prof. Matthias Schulte
Language	DE/EN
Cycle	WiSe
Content	Multivariate distributions and stochastic convergence Point estimators Confidence intervals Hypothesis testing Nonparametric statistics Linear Regression Statistical software (R)
Literature	L. Dümbgen (2016): Einführung in die Statistik, Birkhäuser. L. Dümbgen (2003): Stochastik für Informatiker, Springer. H.-O. Georgii (2012): Stochastics: Introduction to Probability and Statistics, 2nd edition, De Gruyter. N. Henze (2018): Stochastik für Einsteiger, 12th edition, Springer. A. Klenke (2014): Probability Theory: A Comprehensive Course, 2nd edition, Springer. U. Krengel (2005): Einführung in die Wahrscheinlichkeitstheorie und Statistik, 8th edition, Vieweg.

Course L3229: Statistics

Typ	Project-/problem-based Learning
Hrs/wk	1
CP	1
Workload in Hours	Independent Study Time 16, Study Time in Lecture 14
Lecturer	Prof. Matthias Schulte
Language	DE/EN
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Course L2431: Statistics

Typ	Recitation Section (small)
Hrs/wk	1
CP	1
Workload in Hours	Independent Study Time 16, Study Time in Lecture 14
Lecturer	Prof. Matthias Schulte
Language	DE/EN
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Module M0711: Numerical Mathematics II

Courses

Title	Typ	Hrs/wk	CP
Numerical Mathematics II (L0568)	Lecture	2	3
Numerical Mathematics II (L0569)	Recitation Section (small)	2	3

Module Responsible	Dr. Jens-Peter Zemke
Admission Requirements	None
Recommended Previous Knowledge	Numerical Mathematics I Python knowledge
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students are able to name advanced numerical methods for interpolation, approximation, integration, eigenvalue problems, eigenvalue problems, nonlinear root finding problems and explain their core ideas, repeat convergence statements for the numerical methods, sketch convergence proofs, explain practical aspects of numerical methods concerning runtime and storage needs explain aspects regarding the practical implementation of numerical methods with respect to computational and storage complexity.
Skills	Students are able to implement, apply and compare advanced numerical methods in Python, justify the convergence behaviour of numerical methods with respect to the problem and solution algorithm and to transfer it to related problems, for a given problem, develop a suitable solution approach, if necessary through composition of several algorithms, to execute this approach and to critically evaluate the results
Personal Competence
Social Competence	Students are able to work together in heterogeneously composed teams (i.e., teams from different study programs and background knowledge), explain theoretical foundations and support each other with practical aspects regarding the implementation of algorithms.
Autonomy	Students are capable to assess whether the supporting theoretical and practical excercises are better solved individually or in a team, to assess their individual progess and, if necessary, to ask questions and seek help.
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Credit points	6
Course achievement	None
Examination	Oral exam
Examination duration and scale	25 min
Assignment for the Following Curricula	Computer Science: Specialisation III. Mathematics: Elective Compulsory Data Science: Specialisation I. Mathematics: Elective Compulsory Data Science: Specialisation IV. Special Focus Area: Elective Compulsory Computer Science in Engineering: Specialisation III. Mathematics: Elective Compulsory Technomathematics: Specialisation I. Mathematics: Elective Compulsory Theoretical Mechanical Engineering: Core Qualification: Elective Compulsory

Course L0568: Numerical Mathematics II

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Dr. Jens-Peter Zemke
Language	DE/EN
Cycle	SoSe
Content	Error and stability: Notions and estimates Rational interpolation and approximation Multidimensional interpolation (RBF) and approximation (neural nets) Quadrature: Gaussian quadrature, orthogonal polynomials Linear systems: Perturbation theory of decompositions, structured matrices Eigenvalue problems: LR-, QD-, QR-Algorithmus Nonlinear systems of equations: Newton and Quasi-Newton methods, line search (optional) Krylov space methods: Arnoldi-, Lanczos methods (optional)
Literature	Skript Stoer/Bulirsch: Numerische Mathematik 1, Springer Dahmen, Reusken: Numerik für Ingenieure und Naturwissenschaftler, Springer

Course L0569: Numerical Mathematics II

Typ	Recitation Section (small)
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Dr. Jens-Peter Zemke
Language	DE/EN
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Module M1053: Introductory Number Theory

Courses

Title	Typ	Hrs/wk	CP
Number Theory (L1319)	Lecture	4	6
Number Theory (L1320)	Recitation Section (small)	2	3

Module Responsible	Prof. Ulf Kühn
Admission Requirements	None
Recommended Previous Knowledge	Linear Algebra
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students can describe basic concepts in Number Theory such as congruences, quadratic remainders, ring of integers and diophantic problems. They are able to explain them using appropriate examples. Students can discuss logical connections between these concepts. They are capable of illustrating these connections with the help of examples. They know proof strategies and can reproduce them.
Skills	Students can model problems in Number Theory with the help of the concepts studied in this course. Moreover, they are capable of solving them by applying established methods. Students are able to discover and verify further logical connections between the concepts studied in the course. For a given problem, the students can develop and execute a suitable approach, and are able to critically evaluate the results.
Personal Competence
Social Competence	Students are able to work together in teams. They are capable to use mathematics as a common language. In doing so, they can communicate new concepts according to the needs of their cooperating partners. Moreover, they can design examples to check and deepen the understanding of their peers.
Autonomy	Students are capable of checking their understanding of complex concepts on their own. They can specify open questions precisely and know where to get help in solving them. Students have developed sufficient persistence to be able to work for longer periods in a goal-oriented manner on hard problems.
Workload in Hours	Independent Study Time 186, Study Time in Lecture 84
Credit points	9
Course achievement	None
Examination	Oral exam
Examination duration and scale	30 min
Assignment for the Following Curricula	Technomathematics: Specialisation I. Mathematics: Elective Compulsory

Course L1319: Number Theory

Typ	Lecture
Hrs/wk	4
CP	6
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Lecturer	Dozenten des Fachbereiches Mathematik der UHH
Language	DE
Cycle	WiSe/SoSe
Content	Congruences (chinese remainder theorem, Fermat's little problem, application to asymmetric cryptography) Quadratic Remainders (Legendre symbol, quadratic reciprocity) Properties of the ring of integers (units, ideals, classes of ideals) Application to diophantic problems
Literature	A. Beutelspacher, M.-A. Zschiegner: Diskrete Mathematik für Einsteiger. Vieweg F. Ischebeck: Einladung zur Zahlentheorie. BI J. Kramer: Zahlen für Einsteiger. Vieweg K. Reiss, G. Schmieder: Basiswissen Zahlentheorie. Springer

Course L1320: Number Theory

Typ	Recitation Section (small)
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Dozenten des Fachbereiches Mathematik der UHH
Language	DE
Cycle	WiSe/SoSe
Content	See interlocking course
Literature	See interlocking course

Module M1086: Practical Statistics

Courses

Title	Typ	Hrs/wk	CP
Practical Statistics (L1394)	Lecture	2	3
Practical Statistics (L1395)	Recitation Section (small)	1	2

Module Responsible	Prof. Natalie Neumeyer
Admission Requirements	None
Recommended Previous Knowledge	Mathematical Stochastics Mathematical Statistics
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students can describe basic concepts in Practical Statistics such as nonparametric methods, linear models and multivariate methods. They are able to explain them using appropriate examples. Students can discuss logical connections between these concepts. They are capable of illustrating these connections with the help of examples. They know proof strategies and can reproduce them.
Skills	Students can model problems in Practical Statistics with the help of the concepts studied in this course. Moreover, they are capable of solving them by applying established methods. Students are able to discover and verify further logical connections between the concepts studied in the course. For a given problem, the students can develop and execute a suitable approach, and are able to critically evaluate the results.
Personal Competence
Social Competence	Students are able to work together in teams. They are capable to use mathematics as a common language. In doing so, they can communicate new concepts according to the needs of their cooperating partners. Moreover, they can design examples to check and deepen the understanding of their peers.
Autonomy	Students are capable of checking their understanding of complex concepts on their own. They can specify open questions precisely and know where to get help in solving them. Students have developed sufficient persistence to be able to work for longer periods in a goal-oriented manner on hard problems.
Workload in Hours	Independent Study Time 108, Study Time in Lecture 42
Credit points	5
Course achievement	None
Examination	Oral exam
Examination duration and scale	30 min
Assignment for the Following Curricula	Technomathematics: Specialisation I. Mathematics: Elective Compulsory

Course L1394: Practical Statistics

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Dozenten des Fachbereiches Mathematik der UHH
Language	DE
Cycle	WiSe/SoSe
Content	Nonparametric methods Linear models Multivariate methods
Literature	P. Dalgaard, Introductory Statistics with R, Springer J. Verzani, Using R for introductory statistics, Chapman & Hall U. Ligges, Programmieren mit R, Springer

Course L1395: Practical Statistics

Typ	Recitation Section (small)
Hrs/wk	1
CP	2
Workload in Hours	Independent Study Time 46, Study Time in Lecture 14
Lecturer	Dozenten des Fachbereiches Mathematik der UHH
Language	DE
Cycle	WiSe/SoSe
Content	See interlocking course
Literature	See interlocking course

Module M1054: Topology

Courses

Title	Typ	Hrs/wk	CP
Topology (L1322)	Lecture	4	6
Topology (L1323)	Recitation Section (small)	2	3

Module Responsible	Prof. Birgit Richter
Admission Requirements	None
Recommended Previous Knowledge	Linear Algebra Analysis Higher Analysis
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students can name basic concepts in Topology auch as metric and topological spaces, separation axioms, subspace, quotient and product topologies, connecticity and compactnes, homotopy, fundamental groups and covering spaces. They are able to explain them using appropriate examples. Students can discuss logical connections between these concepts. They are capable of illustrating these connections with the help of examples. They know proof strategies and can reproduce them.
Skills	Students can model problems in Topology with the help of the concepts studied in this course. Moreover, they are capable of solving them by applying established methods. Students are able to discover and verify further logical connections between the concepts studied in the course. For a given problem, the students can develop and execute a suitable approach, and are able to critically evaluate the results.
Personal Competence
Social Competence	Students are able to work together in teams. They are capable to use mathematics as a common language. In doing so, they can communicate new concepts according to the needs of their cooperating partners. Moreover, they can design examples to check and deepen the understanding of their peers.
Autonomy	Students are capable of checking their understanding of complex concepts on their own. They can specify open questions precisely and know where to get help in solving them. Students have developed sufficient persistence to be able to work for longer periods in a goal-oriented manner on hard problems.
Workload in Hours	Independent Study Time 186, Study Time in Lecture 84
Credit points	9
Course achievement	None
Examination	Oral exam
Examination duration and scale	30 min
Assignment for the Following Curricula	Technomathematics: Specialisation I. Mathematics: Elective Compulsory

Course L1322: Topology

Typ	Lecture
Hrs/wk	4
CP	6
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Lecturer	Dozenten des Fachbereiches Mathematik der UHH
Language	DE
Cycle	SoSe
Content	set theoretic topology metric and topological spaces separation axiom subspace, quotient and product topologies connecticity compactness algebraic topology homotopy fundamental groups covering spaces
Literature	J. Munkres, Topology - a first course, Publisher: Prentice Hall College Div (June 1974) B. v. Querenburg, Mengentheoretische Topologie, Verlag: Springer; Auflage: 3 (4. Oktober 2013) G. Laures, M. Szymik, Grundkurs Topologie, Verlag: Spektrum Akademischer Verlag; Auflage: 2009 K. Jänich, Topologie,Verlag: Springer; Auflage: 8. Aufl. 2005. 4., korr. Nachdruck 2008 L.A. Steen, J.A. Seebach, Jr., Counterexamples in Topology, Publisher: Dover Publications (September 22, 1995) O. Viro, O. Ivanov, N. Netsvetaev, V. Kharlamov, Elementary Topology - Problem Textbook, Publisher: American Mathematical Society (September 17, 2008) A. Hatcher, Algebraic Topology, Verlag: Cambridge University Press (2002)

Course L1323: Topology

Typ	Recitation Section (small)
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Dozenten des Fachbereiches Mathematik der UHH
Language	DE
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Module M1556: Set Theory and Mathematical Logic

Courses

Title	Typ	Hrs/wk	CP
Set Theory and Mathematical Logic (L2332)	Lecture	4	6
Set Theory and Mathematical Logic (L2333)	Recitation Section (small)	2	3

Module Responsible	Prof. Benedikt Loewe
Admission Requirements	None
Recommended Previous Knowledge	Linear Algebra
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students can describe basic concepts in Mathematical Logic and in Set Theory such as formal languages, predicate logic, the completeness theorem, the compactness theorem and the Löwenheim-Skolem theorems, Zermelo-Fraenkel axioms, ordinal- and cardinal numbers and the axiom of choice. They are able to explain them using appropriate examples. Students can discuss logical connections between these concepts. They are capable of illustrating these connections with the help of examples. They know proof strategies and can reproduce them.
Skills	Students can model problems in Mathematical Logic and in Set Theory with the help of the concepts studied in this course. Moreover, they are capable of solving them by applying established methods. Students are able to discover and verify further logical connections between the concepts studied in the course. For a given problem, the students can develop and execute a suitable approach, and are able to critically evaluate the results.
Personal Competence
Social Competence	Students are able to work together in teams. They are capable to use mathematics as a common language. In doing so, they can communicate new concepts according to the needs of their cooperating partners. Moreover, they can design examples to check and deepen the understanding of their peers.
Autonomy	Students are capable of checking their understanding of complex concepts on their own. They can specify open questions precisely and know where to get help in solving them. Students have developed sufficient persistence to be able to work for longer periods in a goal-oriented manner on hard problems.
Workload in Hours	Independent Study Time 186, Study Time in Lecture 84
Credit points	9
Course achievement	None
Examination	Written exam
Examination duration and scale	120 min
Assignment for the Following Curricula	Technomathematics: Specialisation I. Mathematics: Elective Compulsory

Course L2332: Set Theory and Mathematical Logic

Typ	Lecture
Hrs/wk	4
CP	6
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Lecturer	Dozenten des Fachbereiches Mathematik der UHH
Language	DE
Cycle	SoSe
Content	Foundations of mathematical logic and model theory first order predicate logic Gödel's completeness theorem and compactness theorem Löwenheim-Skolem theorems Foundations of set theory & Zermelo-Fraenkel axioms Ordinal numbers and Cardinal numbers Axiom of choice & equivalent formulations
Literature	Heinz-Dieter Ebbinghaus, Einführung in die Mengenlehre.

Course L2333: Set Theory and Mathematical Logic

Typ	Recitation Section (small)
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Dozenten des Fachbereiches Mathematik der UHH
Language	DE
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Module M1668: Probability Theory

Courses

Title	Typ	Hrs/wk	CP
Probability Theory (L2643)	Lecture	3	4
Probability Theory (L2644)	Recitation Section (small)	1	2

Module Responsible

Prof. Matthias Schulte

Admission Requirements

None

Recommended Previous Knowledge

Familiarity with the basic concepts of probability

Educational Objectives

After taking part successfully, students have reached the following learning results

Professional Competence

Knowledge

Students can name the basic concepts in probability theory. They are able to explain them using appropriate examples.
Students can discuss logical connections between these concepts. They are capable of illustrating these connections with the help of examples.
They know proof strategies and can reproduce them.

Skills

Students can model problems from probability theory with the help of the concepts studied in this course. Moreover, they are capable of solving them by applying established methods.
Students are able to explore and verify further logical connections between the concepts studied in the course.
For a given problem, the students can develop and execute a suitable technique, and are able to critically evaluate the results.

Personal Competence

Social Competence

Students are able to work together (e.g. on their regular home work) and to present their results appropriately (e.g. during exercise class).
In doing so, they can communicate new concepts according to the needs of their cooperating partners. Moreover, they can design examples to check and deepen the understanding of their peers.

Autonomy

Students are capable of checking their understanding of complex concepts on their own. They can specify open questions precisely and know where to get help in solving them.
Students can put their knowledge in relation to the contents of other lectures.
Students have developed sufficient persistence to be able to work for longer periods in a goal-oriented manner on hard problems.

Workload in Hours

Independent Study Time 124, Study Time in Lecture 56

Credit points

Course achievement

Compulsory	Bonus	Form	Description
No	5 %	Excercises

Examination

Oral exam

Examination duration and scale

30 min

Assignment for the Following Curricula

Computer Science: Specialisation III. Mathematics: Elective Compulsory
Data Science: Specialisation IV. Special Focus Area: Elective Compulsory
Data Science: Specialisation I. Mathematics: Elective Compulsory
Interdisciplinary Mathematics: Specialisation II. Numerical - Modelling Training: Compulsory
Technomathematics: Specialisation I. Mathematics: Elective Compulsory

Course L2643: Probability Theory

Typ	Lecture
Hrs/wk	3
CP	4
Workload in Hours	Independent Study Time 78, Study Time in Lecture 42
Lecturer	Prof. Matthias Schulte
Language	EN
Cycle	SoSe
Content	Measure and probability spaces Integration and expectation Types of stochastic convergence Law of large numbers Central limit theorem Radon-Nikodym theorem Conditional expectation Martingales Markov chains Poisson processes
Literature	H. Bauer, Probability theory and elements of measure theory, second edition, Academic Press, 1981. A. Klenke, Probability Theory: A Comprehensive Course, second edition, Springer, 2014. G. F. Lawler, Introduction to Stochastic Processes, second edition, Chapman & Hall/CRC, 2006. A. N. Shiryaev, Probability, second edition, Springer, 1996.

Course L2644: Probability Theory

Typ	Recitation Section (small)
Hrs/wk	1
CP	2
Workload in Hours	Independent Study Time 46, Study Time in Lecture 14
Lecturer	Prof. Matthias Schulte
Language	EN
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Specialization II. Informatics

Module M0732: Software Engineering

Courses

Title	Typ	Hrs/wk	CP
Software Engineering (L0627)	Lecture	2	3
Software Engineering (L0628)	Recitation Section (small)	2	3

Module Responsible

Prof. Sibylle Schupp

Admission Requirements

None

Recommended Previous Knowledge

Automata theory and formal languages
Procedural programming or Functional programming
Object-oriented programming, algorithms, and data structures

Educational Objectives

After taking part successfully, students have reached the following learning results

Professional Competence

Knowledge

Students explain the phases of the software life cycle, describe the fundamental terminology and concepts of software engineering, and paraphrase the principles of structured software development. They give examples of software-engineering tasks of existing large-scale systems. They write test cases for different test strategies and devise specifications or models using different notations, and critique both. They explain simple design patterns and the major activities in requirements analysis, maintenance, and project planning.

Skills

For a given task in the software life cycle, students identify the corresponding phase and select an appropriate method. They choose the proper approach for quality assurance. They design tests for realistic systems, assess the quality of the tests, and find errors at different levels. They apply and modify non-executable artifacts. They integrate components based on interface specifications.

Personal Competence

Social Competence

Students practice peer programming. They explain problems and solutions to their peer. They communicate in English.

Autonomy

Using on-line quizzes and accompanying material for self study, students can assess their level of knowledge continuously and adjust it appropriately. Working on exercise problems, they receive additional feedback.

Workload in Hours

Independent Study Time 124, Study Time in Lecture 56

Credit points

Course achievement

Compulsory	Bonus	Form	Description
Yes	15 %	Excercises

Examination

Written exam

Examination duration and scale

90 min

Assignment for the Following Curricula

Course L0627: Software Engineering

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Sibylle Schupp
Language	EN
Cycle	SoSe
Content	Model-based software engineering Information modeling (use case diagrams) Behavioral modeling (finite state machines, Petri Nets, behavioral UML diagrams) Structural modeling (OOA, UML class diagrams, OCL) Model-based testing Engineering software products Agile processes Architecture Code-based testing System-level testing Software management Maintenance Project management Software processes
Literature	Ian Sommerville, Engineering Software Products: An Introduction to Modern Software Engineering, Pearson 2020. Kassem A. Saleh, Software Engineering, J. Ross Publishing 2009.

Course L0628: Software Engineering

Typ	Recitation Section (small)
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Sibylle Schupp
Language	EN
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Module M1595: Machine Learning I

Courses

Title	Typ	Hrs/wk	CP
Machine Learning I (L2432)	Lecture	2	3
Machine Learning I (L2433)	Recitation Section (small)	3	3

Module Responsible

Prof. Nihat Ay

Admission Requirements

None

Recommended Previous Knowledge

Linear Algebra, Analysis, Basic Programming Course

Educational Objectives

After taking part successfully, students have reached the following learning results

Professional Competence

Knowledge

The students know

general principles of machine learning learning: supervised/unsupervised learning, generative/descriptive learning, parametric/non-parametric learning
different learning methods: neural networks, support vector machines, clustering, dimensionality reduction, kernel methods
fundamentals of statistical learning theory
advanced techniques such as transfer learning, reinforcement learning, generative adversarial networks and adaptive control

Skills

The students can

apply machine learning methods to concrete problems
select and evaluate suitable methods for specific problems
evaluate the quality of a trained data-driven model
work with known software frameworks for machine learning
adapt the architecture and cost function of neural networks to specific problems
show the limits of machine learning methods

Personal Competence

Social Competence

Students can work on complex problems both independently and in teams. They can exchange ideas with each other and use their individual strengths to solve the problem.

Autonomy

Students are able to independently investigate a complex problem and assess which competencies are required to solve it.

Workload in Hours

Independent Study Time 110, Study Time in Lecture 70

Credit points

Course achievement

Compulsory	Bonus	Form	Description
No	20 %	Excercises

Examination

Written exam

Examination duration and scale

90 min

Assignment for the Following Curricula

General Engineering Science (German program, 7 semester): Specialisation Mechanical Engineering, Focus Theoretical Mechanical Engineering: Elective Compulsory
General Engineering Science (German program, 7 semester): Specialisation Data Science: Compulsory
Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory
Data Science: Core Qualification: Compulsory
Engineering Science: Specialisation Advanced Materials: Elective Compulsory
Engineering Science: Specialisation Mechatronics: Elective Compulsory
Engineering Science: Specialisation Data Science: Compulsory
Engineering Science: Specialisation Mechanical Engineering: Elective Compulsory
Computer Science in Engineering: Specialisation I. Computer Science: Elective Compulsory
Logistics and Mobility: Specialisation Information Technology: Elective Compulsory
Mechanical Engineering: Specialisation Theoretical Mechanical Engineering: Elective Compulsory
Mechatronics: Specialisation Dynamic Systems and AI: Compulsory
Technomathematics: Specialisation II. Informatics: Elective Compulsory
Engineering and Management - Major in Logistics and Mobility: Specialisation Information Technology: Elective Compulsory

Course L2432: Machine Learning I

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Nihat Ay
Language	DE/EN
Cycle	SoSe
Content	History of neuroscience and machine learning (in particular, the age of deep learning) McCulloch-Pitts neurons and binary Artificial Neural Networks Boolean and threshold functions Universality of McCulloch-Pitts neural networks Learning and the perceptron convergence theorem Support vector machines Harmonic analysis of Boolean functions Continuous Artificial Neural Networks Kolmogorov’s superposition theorem Universal approximation with continuous neural networks Approximation error and the gradient decent method: the general idea The stochastic gradient decent method (Robbins-Monro and Kiefer-Wolfowitz cases) Multilayer networks and the backpropagation algorithm Statistical Learning Theory
Literature	Martin Anthony and Peter L. Bartlett. Neural Network Learning: Theoretical Foundations. Cambridge University Press, 1999. Martin Anthony. Discrete Mathematics of Neural Networks: Selected Topics. SIAM Monographs on Discrete Mathematics & Applications, 1987. Mehryar Mohri, Afshin Rostamizadeh and Ameet Talwalkar. Foundations of Machine Learning, Second Edition. MIT Press, 2018. Christopher M. Bishop. Pattern Recognition and Machine Learning. Information Science and Statistics. Springer-Verlag, 2008. Bernhard Schölkopf, Alexander Smola. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Adaptive Computation and Machine Learning series. MIT Press, Cambridge, MA, 2002. Luc Devroye, László Györfi, Gábor Lugosi. A Probabilistic Theory of Pattern Recognition. Springer, 1996. Vladimir Vapnik. The Nature of Statistical Learning Theory. Springer-Verlag: New York, Berlin, Heidelberg, 1995.

Course L2433: Machine Learning I

Typ	Recitation Section (small)
Hrs/wk	3
CP	3
Workload in Hours	Independent Study Time 48, Study Time in Lecture 42
Lecturer	Prof. Nihat Ay
Language	DE/EN
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Module M0624: Automata Theory and Formal Languages

Courses

Title	Typ	Hrs/wk	CP
Automata Theory and Formal Languages (L0332)	Lecture	2	4
Automata Theory and Formal Languages (L0507)	Recitation Section (small)	2	2

Module Responsible

Prof. Matthias Mnich

Admission Requirements

None

Recommended Previous Knowledge

Participating students should be able to

- specify algorithms for simple data structures (such as, e.g., arrays) to solve computational problems

- apply propositional logic and predicate logic for specifying and understanding mathematical proofs

- apply the knowledge and skills taught in the module Discrete Algebraic Structures

Educational Objectives

After taking part successfully, students have reached the following learning results

Professional Competence

Knowledge

Students can explain syntax, semantics, and decision problems of propositional logic, and they are able to give algorithms for solving decision problems. Students can show correspondences to Boolean algebra. Students can describe which application problems are hard to represent with propositional logic, and therefore, the students can motivate predicate logic, and define syntax, semantics, and decision problems for this representation formalism. Students can explain unification and resolution for solving the predicate logic SAT decision problem. Students can also describe syntax, semantics, and decision problems for various kinds of temporal logic, and identify their application areas. The participants of the course can define various kinds of finite automata and can identify relationships to logic and formal grammars. The spectrum that students can explain ranges from deterministic and nondeterministic finite automata and pushdown automata to Turing machines. Students can name those formalism for which nondeterminism is more expressive than determinism. They are also able to demonstrate which decision problems require which expressivity, and, in addition, students can transform decision problems w.r.t. one formalism into decision problems w.r.t. other formalisms. They understand that some formalisms easily induce algorithms whereas others are best suited for specifying systems and their properties. Students can describe the relationships between formalisms such as logic, automata, or grammars.

Skills

Students can apply propositional logic as well as predicate logic resolution to a given set of formulas. Students analyze application problems in order to derive propositional logic, predicate logic, or temporal logic formulas to represent them. They can evaluate which formalism is best suited for a particular application problem, and they can demonstrate the application of algorithms for decision problems to specific formulas. Students can also transform nondeterministic automata into deterministic ones, or derive grammars from automata and vice versa. They can show how parsers work, and they can apply algorithms for the language emptiness problem in case of infinite words.

Personal Competence

Social Competence

Students are able to work together in teams. They are capable to use mathematics as a common language.
In doing so, they can communicate new concepts according to the needs of their cooperating partners. Moreover, they can design examples to check and deepen the understanding of their peers.

Autonomy

Students are capable of checking their understanding of complex concepts on their own. They can specify open questions precisely and know where to get help in solving them.
Students have developed sufficient persistence to be able to work for longer periods in a goal-oriented manner on hard problems.

Workload in Hours

Independent Study Time 124, Study Time in Lecture 56

Credit points

Course achievement

Compulsory	Bonus	Form	Description
No	20 %	Excercises

Examination

Written exam

Examination duration and scale

90 min

Assignment for the Following Curricula

General Engineering Science (German program, 7 semester): Specialisation Computer Science: Compulsory
General Engineering Science (German program, 7 semester): Specialisation Data Science: Compulsory
Computer Science: Core Qualification: Compulsory
Data Science: Core Qualification: Compulsory
Engineering Science: Specialisation Mechatronics: Elective Compulsory
Engineering Science: Specialisation Mechatronics: Elective Compulsory
Engineering Science: Specialisation Data Science: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Mechatronics: Elective Compulsory
Computer Science in Engineering: Core Qualification: Compulsory
Orientation Studies: Core Qualification: Elective Compulsory
Technomathematics: Specialisation II. Informatics: Elective Compulsory

Course L0332: Automata Theory and Formal Languages

Typ	Lecture
Hrs/wk	2
CP	4
Workload in Hours	Independent Study Time 92, Study Time in Lecture 28
Lecturer	Prof. Matthias Mnich
Language	EN
Cycle	SoSe
Content	Propositional logic, Boolean algebra, propositional resolution, SAT-2KNF Predicate logic, unification, predicate logic resolution Temporal Logics (LTL, CTL) Deterministic finite automata, definition and construction Regular languages, closure properties, word problem, string matching Nondeterministic automata: Rabin-Scott transformation of nondeterministic into deterministic automata Epsilon automata, minimization of automata, elimination of e-edges, uniqueness of the minimal automaton (modulo renaming of states) Myhill-Nerode Theorem: Correctness of the minimization procedure, equivalence classes of strings induced by automata Pumping Lemma for regular languages: provision of a tool which, in some cases, can be used to show that a finite automaton principally cannot be expressive enough to solve a word problem for some given language Regular expressions vs. finite automata: Equivalence of formalisms, systematic transformation of representations, reductions Pushdown automata and context-free grammars: Definition of pushdown automata, definition of context-free grammars, derivations, parse trees, ambiguities, pumping lemma for context-free grammars, transformation of formalisms (from pushdown automata to context-free grammars and back) Chomsky normal form CYK algorithm for deciding the word problem for context-free grammrs Deterministic pushdown automata Deterministic vs. nondeterministic pushdown automata: Application for parsing, LL(k) or LR(k) grammars and parsers vs. deterministic pushdown automata, compiler compiler Regular grammars Outlook: Turing machines and linear bounded automata vs general and context-sensitive grammars Chomsky hierarchy Mealy- and Moore automata: Automata with output (w/o accepting states), infinite state sequences, automata networks Omega automata: Automata for infinite input words, Büchi automata, representation of state transition systems, verification w.r.t. temporal logic specifications (in particular LTL) LTL safety conditions and model checking with Büchi automata, relationships between automata and logic Fixed points, propositional mu-calculus Characterization of regular languages by monadic second-order logic (MSO)
Literature	Logik für Informatiker Uwe Schöning, Spektrum, 5. Aufl. Logik für Informatiker Martin Kreuzer, Stefan Kühling, Pearson Studium, 2006 Grundkurs Theoretische Informatik, Gottfried Vossen, Kurt-Ulrich Witt, Vieweg-Verlag, 2010. Principles of Model Checking, Christel Baier, Joost-Pieter Katoen, The MIT Press, 2007

Course L0507: Automata Theory and Formal Languages

Typ	Recitation Section (small)
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Matthias Mnich
Language	EN
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Module M1586: Scientific Programming

Courses

Title	Typ	Hrs/wk	CP
Scientific Programming (L2405)	Lecture	3	4
Scientific Programming (L2406)	Recitation Section (small)	2	2

Module Responsible	Prof. Tobias Knopp
Admission Requirements	None
Recommended Previous Knowledge	procedural programming, linear algebra
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	The students can efficiently solve scientific problems in a modern programming language. are familiar with the concept of reproducible science. can handle multidimensional arrays, sparse arrays, data frames and missing data. They know the advantages and disadvantages of specific data structures. know various ways of presenting data, data relationships and error measures in a suitable way. They are familiar with known data formats for storing scientific data and can select a suitable format for specific data.
Skills	Students are able to translate complex problems from a mathematical formulation into a suitable program. to divide a complex problem into subproblems which can be implemented modularly. to identify numerical standard problems and to use suitable standard algorithms which are available in libraries. to write maintainable program code, the correctness of which is verified by suitable tests. to measure the runtime of programs, to identify bottlenecks and to apply suitable acceleration techniques.
Personal Competence
Social Competence	Students can work on complex problems both independently and in teams. They can exchange ideas with each other and use their individual strengths to solve the problem.
Autonomy	Students are able to independently investigate a complex problem and assess which competencies are required to solve it.
Workload in Hours	Independent Study Time 110, Study Time in Lecture 70
Credit points	6
Course achievement	None
Examination	Subject theoretical and practical work
Examination duration and scale	exercise task, group project with presentation, and written test
Assignment for the Following Curricula	General Engineering Science (German program, 7 semester): Specialisation Data Science: Elective Compulsory Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory Data Science: Core Qualification: Compulsory Engineering Science: Specialisation Data Science: Elective Compulsory Mechatronics: Specialisation Dynamic Systems and AI: Compulsory Technomathematics: Specialisation II. Informatics: Elective Compulsory

Course L2405: Scientific Programming

Typ	Lecture
Hrs/wk	3
CP	4
Workload in Hours	Independent Study Time 78, Study Time in Lecture 42
Lecturer	Prof. Tobias Knopp
Language	DE/EN
Cycle	SoSe
Content	Elementary Data Types and the Relationship to Mathematics Scientific data types: Multidimensional Arrays, sparse Arrays, Data Frames, Missing Data Multiple Dispatch as an Efficient Paradigm for Scientific Programming Literate Programming Profiling and benchmarks Acceleration techniques: caching, multi-threading, SIMD, GPGPU Scientific data formats: CSV, TOML, HDF5, and selected examples Data visualization Standard numerical techniques and efficient program libraries (BLAS, LAPACK, FFTW, ...) Tests, code management, documentation Reproducible science
Literature	Ben Lauwens, Allen Downey: Think Julia: How to Think Like a Computer Scientist

Course L2406: Scientific Programming

Typ	Recitation Section (small)
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Tobias Knopp
Language	DE/EN
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Module M0730: Computer Engineering

Courses

Title	Typ	Hrs/wk	CP
Computer Engineering (L0321)	Lecture	3	4
Computer Engineering (L0324)	Recitation Section (small)	1	2

Module Responsible

Prof. Heiko Falk

Admission Requirements

None

Recommended Previous Knowledge

Basic knowledge in electrical engineering

Educational Objectives

After taking part successfully, students have reached the following learning results

Professional Competence

Knowledge

This module deals with the foundations of the functionality of computing systems. It covers the layers from the assembly-level programming down to gates. The module includes the following topics:

Introduction
Combinational logic: Gates, Boolean algebra, Boolean functions, hardware synthesis, combinational networks
Sequential logic: Flip-flops, automata, systematic hardware design
Technological foundations
Computer arithmetic: Integer addition, subtraction, multiplication and division
Basics of computer architecture: Programming models, MIPS single-cycle architecture, pipelining
Memories: Memory hierarchies, SRAM, DRAM, caches
Input/output: I/O from the perspective of the CPU, principles of passing data, point-to-point connections, busses

Skills

The students perceive computer systems from the architect's perspective, i.e., they identify the internal structure and the physical composition of computer systems. The students can analyze, how highly specific and individual computers can be built based on a collection of few and simple components. They are able to distinguish between and to explain the different abstraction layers of today's computing systems - from gates and circuits up to complete processors.

After successful completion of the module, the students are able to judge the interdependencies between a physical computer system and the software executed on it. In particular, they shall understand the consequences that the execution of software has on the hardware-centric abstraction layers from the assembly language down to gates. This way, they will be enabled to evaluate the impact that these low abstraction levels have on an entire system's performance and to propose feasible options.

Personal Competence

Social Competence

Students are able to solve similar problems alone or in a group and to present the results accordingly.

Autonomy

Students are able to acquire new knowledge from specific literature and to associate this knowledge with other classes.

Workload in Hours

Independent Study Time 124, Study Time in Lecture 56

Credit points

Course achievement

Compulsory	Bonus	Form	Description
Yes	10 %	Excercises

Examination

Written exam

Examination duration and scale

90 minutes, contents of course and labs

Assignment for the Following Curricula

General Engineering Science (German program, 7 semester): Specialisation Computer Science: Compulsory
General Engineering Science (German program, 7 semester): Specialisation Electrical Engineering: Compulsory
Computer Science: Core Qualification: Compulsory
Data Science: Specialisation I. Mathematics/Computer Science: Elective Compulsory
Electrical Engineering: Core Qualification: Compulsory
Computer Science in Engineering: Core Qualification: Compulsory
Mechatronics: Core Qualification: Elective Compulsory
Technomathematics: Specialisation II. Informatics: Elective Compulsory

Course L0321: Computer Engineering

Typ	Lecture
Hrs/wk	3
CP	4
Workload in Hours	Independent Study Time 78, Study Time in Lecture 42
Lecturer	Prof. Heiko Falk
Language	DE/EN
Cycle	WiSe
Content	Introduction Combinational Logic Sequential Logic Technological Foundations Representations of Numbers, Computer Arithmetics Foundations of Computer Architecture Memories Input/Output
Literature	A. Clements. The Principles of Computer Hardware. 3. Auflage, Oxford University Press, 2000. A. Tanenbaum, J. Goodman. Computerarchitektur. Pearson, 2001. D. Patterson, J. Hennessy. Rechnerorganisation und -entwurf. Elsevier, 2005.

Course L0324: Computer Engineering

Typ	Recitation Section (small)
Hrs/wk	1
CP	2
Workload in Hours	Independent Study Time 46, Study Time in Lecture 14
Lecturer	Prof. Heiko Falk
Language	DE/EN
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Module M0731: Functional Programming

Courses

Title	Typ	Hrs/wk	CP
Functional Programming (L0624)	Lecture	2	2
Functional Programming (L0625)	Recitation Section (large)	2	2
Functional Programming (L0626)	Recitation Section (small)	2	2

Module Responsible

Prof. Sibylle Schupp

Admission Requirements

None

Recommended Previous Knowledge

Discrete mathematics at high-school level

Educational Objectives

After taking part successfully, students have reached the following learning results

Professional Competence

Knowledge

Students apply the principles, constructs, and simple design techniques of functional programming. They demonstrate their ability to read Haskell programs and to explain Haskell syntax as well as Haskell's read-eval-print loop. They interpret warnings and find errors in programs. They apply the fundamental data structures, data types, and type constructors. They employ strategies for unit tests of functions and simple proof techniques for partial and total correctness. They distinguish laziness from other evaluation strategies.

Skills

Students break a natural-language description down in parts amenable to a formal specification and develop a functional program in a structured way. They assess different language constructs, make conscious selections both at specification and implementations level, and justify their choice. They analyze given programs and rewrite them in a controlled way. They design and implement unit tests and can assess the quality of their tests. They argue for the correctness of their program.

Personal Competence

Social Competence

Students practice peer programming with varying peers. They explain problems and solutions to their peer. They defend their programs orally. They communicate in English.

Autonomy

In programming labs, students learn under supervision (a.k.a. "Betreutes Programmieren") the mechanics of programming. In exercises, they develop solutions individually and independently, and receive feedback.

Workload in Hours

Independent Study Time 96, Study Time in Lecture 84

Credit points

Course achievement

Compulsory	Bonus	Form	Description
Yes	15 %	Excercises

Examination

Written exam

Examination duration and scale

90 min

Assignment for the Following Curricula

General Engineering Science (German program, 7 semester): Specialisation Computer Science: Elective Compulsory
Computer Science: Core Qualification: Compulsory
Data Science: Specialisation I. Mathematics/Computer Science: Elective Compulsory
Engineering Science: Specialisation Information and Communication Systems: Compulsory
Engineering Science: Specialisation Mechatronics: Elective Compulsory
General Engineering Science (English program, 7 semester): Specialisation Mechatronics: Elective Compulsory
Computer Science in Engineering: Specialisation I. Computer Science: Elective Compulsory
Technomathematics: Specialisation II. Informatics: Elective Compulsory

Course L0624: Functional Programming

Typ	Lecture
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Sibylle Schupp
Language	EN
Cycle	WiSe
Content	Functions, Currying, Recursive Functions, Polymorphic Functions, Higher-Order Functions Conditional Expressions, Guarded Expressions, Pattern Matching, Lambda Expressions Types (simple, composite), Type Classes, Recursive Types, Algebraic Data Type Type Constructors: Tuples, Lists, Trees, Associative Lists (Dictionaries, Maps) Modules Interactive Programming Lazy Evaluation, Call-by-Value, Strictness Design Recipes Testing (axiom-based, invariant-based, against reference implementation) Reasoning about Programs (equation-based, inductive) Idioms of Functional Programming Haskell Syntax and Semantics
Literature	Graham Hutton, Programming in Haskell, Cambridge University Press 2007.

Course L0625: Functional Programming

Typ	Recitation Section (large)
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Sibylle Schupp
Language	EN
Cycle	WiSe
Content	Functions, Currying, Recursive Functions, Polymorphic Functions, Higher-Order Functions Conditional Expressions, Guarded Expressions, Pattern Matching, Lambda Expressions Types (simple, composite), Type Classes, Recursive Types, Algebraic Data Type Type Constructors: Tuples, Lists, Trees, Associative Lists (Dictionaries, Maps) Modules Interactive Programming Lazy Evaluation, Call-by-Value, Strictness Design Recipes Testing (axiom-based, invariant-based, against reference implementation) Reasoning about Programs (equation-based, inductive) Idioms of Functional Programming Haskell Syntax and Semantics
Literature	Graham Hutton, Programming in Haskell, Cambridge University Press 2007., 2nd edition 2016.

Course L0626: Functional Programming

Typ	Recitation Section (small)
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Sibylle Schupp
Language	EN
Cycle	WiSe
Content	Functions, Currying, Recursive Functions, Polymorphic Functions, Higher-Order Functions Conditional Expressions, Guarded Expressions, Pattern Matching, Lambda Expressions Types (simple, composite), Type Classes, Recursive Types, Algebraic Data Type Type Constructors: Tuples, Lists, Trees, Associative Lists (Dictionaries, Maps) Modules Interactive Programming Lazy Evaluation, Call-by-Value, Strictness Design Recipes Testing (axiom-based, invariant-based, against reference implementation) Reasoning about Programs (equation-based, inductive) Idioms of Functional Programming Haskell Syntax and Semantics
Literature	Graham Hutton, Programming in Haskell, Cambridge University Press 2007.

Module M0834: Computernetworks and Internet Security

Courses

Title	Typ	Hrs/wk	CP
Computer Networks and Internet Security (L1098)	Lecture	3	5
Computer Networks and Internet Security (L1099)	Recitation Section (small)	1	1

Module Responsible	Prof. Andreas Timm-Giel
Admission Requirements	None
Recommended Previous Knowledge	Basic of Computer Science
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	In this course, an introduction to computer networks with focus on the Internet and its security is given. Basic functionality of complex protocols are introduced. Students learn to understand these and identify common principles. In the exercises and lecture discussions, these basic principles and an introduction to performance modelling are addressed using exercises, homework assignments and labs.  This comprises of: What's the Internet? Application layer protocols (HTTP, SMTP, DNS) Transport layer protocols (TCP, UDP) Network Layer (Internet Protocol, routing in the Internet) Data link layer with media access at the example of Ethernet and WLAN Internet security: IPSec  Internet security: communication security, security of address resolution, firewalls
Skills	Students are able to explain Internet protocols in detail and classify them Students are able to analyze and develop networked systems in further studies and job Students can apply their hands on experiences gained for networking protocols in real settings in further studies and job
Personal Competence
Social Competence	Students are able to work together in teams for labs and homework assignments. In doing so, they learn how to collaborate according to the needs of other students Students are asked to explain the exercises and solutions within the team to determine how much content they have understood from the (pre-recorded) lectures. This fosters students' self-confidence and enhances their presentation skills
Autonomy	Students can select relevant parts out of a high amount of professional knowledge and can independently learn and understand it
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Credit points	6
Course achievement	None
Examination	Written exam
Examination duration and scale	120 min
Assignment for the Following Curricula	General Engineering Science (German program, 7 semester): Specialisation Computer Science: Elective Compulsory Computer Science: Core Qualification: Compulsory Data Science: Specialisation I. Mathematics/Computer Science: Elective Compulsory Electrical Engineering: Core Qualification: Elective Compulsory Engineering Science: Specialisation Mechatronics: Elective Compulsory Engineering Science: Specialisation Electrical Engineering: Elective Compulsory Engineering Science: Specialisation Information and Communication Systems: Compulsory General Engineering Science (English program, 7 semester): Specialisation Mechatronics: Elective Compulsory Computer Science in Engineering: Core Qualification: Compulsory Technomathematics: Specialisation II. Informatics: Elective Compulsory

Course L1098: Computer Networks and Internet Security

Typ	Lecture
Hrs/wk	3
CP	5
Workload in Hours	Independent Study Time 108, Study Time in Lecture 42
Lecturer	Dr. Koojana Kuladinithi, Prof. Sibylle Fröschle
Language	EN
Cycle	WiSe
Content	In this class an introduction to computer networks with focus on the Internet and its security is given. Basic functionality of complex protocols are introduced. Students learn to understand these and identify common principles. In the exercises these basic principles and an introduction to performance modelling are addressed using computing tasks and physical labs. In the second part of the lecture an introduction to Internet security is given. This class comprises: Introduction to the Internet (TCP/IP model) Application layer protocols (HTTP, SMTP, DNS) Transport layer protocols (TCP, UDP) Network Layer (Internet Protocol IPv4 & IPv6, routing in the Internet) Data link layer with media access at the example of WLAN Introduction to Internet Security Security Aspects of Address Resolution (DNS/DNSSEC, ARP/SEND) Communication Security (IPSec) - From Address Resolution to Routing (Securing BGP) Botnets + Firewalls
Literature	Kurose, Ross, Computer Networking - A Top-Down Approach, 8th Edition, Addison-Wesley Kurose, Ross, Computernetzwerke - Der Top-Down-Ansatz, Pearson Studium; Auflage: 8. Auflage W. Stallings: Cryptography and Network Security: Principles and Practice, 8th edition Further literature is announced at the beginning of the lecture.

Course L1099: Computer Networks and Internet Security

Typ	Recitation Section (small)
Hrs/wk	1
CP	1
Workload in Hours	Independent Study Time 16, Study Time in Lecture 14
Lecturer	Dr. Koojana Kuladinithi, Prof. Sibylle Fröschle
Language	EN
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Module M1423: Algorithms and Data Structures

Courses

Title	Typ	Hrs/wk	CP
Algorithms and Data Structures (L2046)	Lecture	4	4
Algorithms and Data Structures (L2047)	Recitation Section (small)	1	2

Module Responsible

Prof. Matthias Mnich

Admission Requirements

None

Recommended Previous Knowledge

Discrete Algebraic Structures
Mathematics I
Mathematics II
Procedual Programming
Objectoriented Programming

Educational Objectives

After taking part successfully, students have reached the following learning results

Professional Competence

Knowledge

Students can name the basic concepts in algorithm design, algorithm analysis and problem reductions. They are able to explain them using appropriate examples.
Students can discuss logical connections between these concepts. They are capable of illustrating these connections with the help of examples.
They know proof strategies and can reproduce them.

Skills

Students can model discrete decision, search and optimization problems with the help of the concepts studied in this course. Moreover, they are capable of solving them, and reducing them to each other, by applying established methods.
Students are able to discover and verify further logical connections between the concepts studied in the course.
For a given problem, the students can develop and execute a suitable approach, and are able to critically evaluate the results.

Personal Competence

Social Competence

Students are able to work together in teams. They are capable to use mathematics as a common language.
In doing so, they can communicate new concepts according to the needs of their cooperating partners. Moreover, they can design examples to check and deepen the understanding of their peers.

Autonomy

Students are capable of checking their understanding of complex concepts on their own. They can specify open questions precisely and know where to get help in solving them.
Students have developed sufficient persistence to be able to work for longer periods in a goal-oriented manner on hard problems.

Workload in Hours

Independent Study Time 110, Study Time in Lecture 70

Credit points

Course achievement

Compulsory	Bonus	Form	Description
No	20 %	Excercises

Examination

Written exam

Examination duration and scale

90 min

Assignment for the Following Curricula

General Engineering Science (German program, 7 semester): Specialisation Computer Science: Compulsory
General Engineering Science (German program, 7 semester): Specialisation Data Science: Compulsory
Computer Science: Core Qualification: Compulsory
Data Science: Core Qualification: Compulsory
Engineering Science: Specialisation Data Science: Compulsory
Engineering Science: Specialisation Information and Communication Systems: Compulsory
Computer Science in Engineering: Core Qualification: Compulsory
Logistics and Mobility: Specialisation Information Technology: Elective Compulsory
Technomathematics: Specialisation II. Informatics: Elective Compulsory
Engineering and Management - Major in Logistics and Mobility: Specialisation II. Information Technology: Elective Compulsory

Course L2046: Algorithms and Data Structures

Typ	Lecture
Hrs/wk	4
CP	4
Workload in Hours	Independent Study Time 64, Study Time in Lecture 56
Lecturer	Prof. Matthias Mnich
Language	DE/EN
Cycle	WiSe
Content	Insertion sort Register machines Asymptotic analysis, Landau notation Polynomial-time algorithms and NP-completeness Divide-and-conquer, merge sort Strassen algorithm Greedy algorithm Dynamic programming Quick sort AVL-trees, B-trees Hashing Depth first search, breadth first search Shortest paths Flow problems, Ford-Fulkerson algorithm
Literature	T. Cormen, Ch. Leiserson, R. Rivest, C. Stein: Introduction to Algorithms. MIT Press, 2013 S. Skiena: The Algorithm Design Manual. Springer, 2008 J. M. Kleinberg and É. Tardos. Algorithm Design. Addison-Wesley, 2005.

Course L2047: Algorithms and Data Structures

Typ	Recitation Section (small)
Hrs/wk	1
CP	2
Workload in Hours	Independent Study Time 46, Study Time in Lecture 14
Lecturer	Prof. Matthias Mnich
Language	DE/EN
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Module M1594: Machine Learning II

Courses

Title	Typ	Hrs/wk	CP
Machine Learning II (L2436)	Lecture	2	3
Machine Learning II (L2941)	Recitation Section (small)	3	3

Module Responsible

Prof. Nihat Ay

Admission Requirements

None

Recommended Previous Knowledge

Successful participation in the modules:

Scientific Programming
Algorithms and Data Structures
Machine Learning

Educational Objectives

After taking part successfully, students have reached the following learning results

Professional Competence

Knowledge

Students get to know tools used by development teams to

plan development flows,
mine, process and analyze data
train and validate data-orientated models
follow good practice in software engineering

Skills

Students work in teams on a larger data project. The required competences are learned and practically applied. These are for example:

project specification based on user requirements
creating a data-orientated software architecture
mining, preprocessing and analyzing larger datasets
implementing a learning platform in a team
comparison of different learning methods
performing statistical tests

Personal Competence

Social Competence

Team work has its own challenges with respect to interaction of team members as well as finding the necessary agreement during joint software development. During the project students learn the required competences and experience the practical needs.

Autonomy

During team work it is mandatory to take and explain a certain position, to independently complete assigned tasks, and to present results to the team. Open issues must be identified and returned into the team to find an agreed resolution.

Workload in Hours

Independent Study Time 110, Study Time in Lecture 70

Credit points

Course achievement

Compulsory	Bonus	Form	Description
No	20 %	Excercises

Examination

Written exam

Examination duration and scale

90 min

Assignment for the Following Curricula

General Engineering Science (German program, 7 semester): Specialisation Data Science: Elective Compulsory
Data Science: Core Qualification: Compulsory
Engineering Science: Specialisation Data Science: Elective Compulsory
Mechatronics: Specialisation Dynamic Systems and AI: Elective Compulsory
Technomathematics: Specialisation II. Informatics: Elective Compulsory

Course L2436: Machine Learning II

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Nihat Ay
Language	DE/EN
Cycle	WiSe
Content	Supervised statistical learning and generalisation The empirical risk minimisation principle The law of large numbers and the Glivenko-Cantellit heorem Shatter coefficients, VC dimension, and Rademacher complexity Fast convergence theorem of Vapnik and Chervonenkis VC dimensions of discrete neural networks The structural risk minimisation principle Learning from samples as an inverse problem Reproducing kernel Hilbert space Moore-Penrose inverse Ill-posed inverse problems and regularisation Tikhonov regularisation Regularised empirical risk minimisation covering numbers The bias variance problem
Literature	Martin Anthony and Peter L. Bartlett. Neural Network Learning: Theoretical Foundations. Cambridge University Press, 1999. Martin Anthony. Discrete Mathematics of Neural Networks: Selected Topics. SIAM Monographs on Discrete Mathematics & Applications, 1987. Mehryar Mohri, Afshin Rostamizadeh and Ameet Talwalkar. Foundations of Machine Learning, Second Edition. MIT Press, 2018. Christopher M. Bishop. Pattern Recognition and Machine Learning. Information Science and Statistics. Springer-Verlag, 2008. Bernhard Schölkopf, Alexander Smola. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Adaptive Computation and Machine Learning series. MIT Press, Cambridge, MA, 2002. Luc Devroye, László Györfi, Gábor Lugosi. A Probabilistic Theory of Pattern Recognition. Springer, 1996. Vladimir Vapnik. The Nature of Statistical Learning Theory. Springer-Verlag: New York, Berlin, Heidelberg, 1995.

Course L2941: Machine Learning II

Typ	Recitation Section (small)
Hrs/wk	3
CP	3
Workload in Hours	Independent Study Time 48, Study Time in Lecture 42
Lecturer	Prof. Nihat Ay
Language	DE/EN
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Module M1593: Data Mining

Courses

Title	Typ	Hrs/wk	CP
Data Mining (L2434)	Lecture	2	3
Data Mining (L2435)	Project-/problem-based Learning	2	3

Module Responsible

Prof. Stefan Schulte

Admission Requirements

None

Recommended Previous Knowledge

Databases
Machine learning

Educational Objectives

After taking part successfully, students have reached the following learning results

Professional Competence

Knowledge

After successful completion of the course, students know:

Basic concepts for data preparation
Similarity and distance measures
Methods to mine data patterns
Procedures to analyse clusters
Approaches to identify outliers
Data mining for different types of data, e.g., data streams, text data, time series data

Skills

Students are able to analyze large, heterogeneous volumes of data. They know methods and their application to recognize patterns in data sets and data clusters. The students are able to apply the studied methods in different domains, e.g., for data streams, text data, or time series data.

Personal Competence

Social Competence

Students can work on complex problems both independently and in teams. They can exchange ideas with each other and use their individual strengths to solve the problem.

Autonomy

Students are able to independently investigate a complex problem and assess which competencies are required to solve it.

Workload in Hours

Independent Study Time 124, Study Time in Lecture 56

Credit points

Course achievement

Compulsory	Bonus	Form	Description
Yes	20 %	Subject theoretical and practical work	Praktische Arbeiten zu bestimmten Themen aus dem Bereich Data Mining

Examination

Written exam

Examination duration and scale

90 min

Assignment for the Following Curricula

General Engineering Science (German program, 7 semester): Specialisation Data Science: Compulsory
Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory
Data Science: Core Qualification: Compulsory
Engineering Science: Specialisation Data Science: Compulsory
Logistics and Mobility: Specialisation Information Technology: Elective Compulsory
Mechatronics: Specialisation Dynamic Systems and AI: Elective Compulsory
Technomathematics: Specialisation II. Informatics: Elective Compulsory
Engineering and Management - Major in Logistics and Mobility: Specialisation II. Information Technology: Elective Compulsory

Course L2434: Data Mining

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Stefan Schulte, Dr. Dominik Schallmoser
Language	EN
Cycle	WiSe
Content	Data preparation Similarity and distance measures Pattern mining Cluster analysis Outliers detection Data mining for different types of data, e.g., data streams, text data, time series data
Literature	Charu C. Aggarwal: Text Mining - The Textbook, Springer, 2015. Available at https://link.springer.com/book/10.1007/978-3-319-14142-8

Course L2435: Data Mining

Typ	Project-/problem-based Learning
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Stefan Schulte, Dr. Dominik Schallmoser
Language	EN
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Module M1249: Medical Imaging

Courses

Title	Typ	Hrs/wk	CP
Medical Imaging (L1694)	Lecture	2	3
Medical Imaging (L1695)	Recitation Section (small)	2	3

Module Responsible	Prof. Tobias Knopp
Admission Requirements	None
Recommended Previous Knowledge	Basic knowledge in linear algebra, numerics, and signal processing
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	After successful completion of the module, students are able to describe reconstruction methods for different tomographic imaging modalities such as computed tomography and magnetic resonance imaging. They know the necessary basics from the fields of signal processing and inverse problems and are familiar with both analytical and iterative image reconstruction methods. The students have a deepened knowledge of the imaging operators of computed tomography and magnetic resonance imaging.
Skills	The students are able to implement reconstruction methods and test them using tomographic measurement data. They can visualize the reconstructed images and evaluate the quality of their data and results. In addition, students can estimate the temporal complexity of imaging algorithms.
Personal Competence
Social Competence	Students can work on complex problems both independently and in teams. They can exchange ideas with each other and use their individual strengths to solve the problem.
Autonomy	Students are able to independently investigate a complex problem and assess which competencies are required to solve it.
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Credit points	6
Course achievement	None
Examination	Written exam
Examination duration and scale	90 min
Assignment for the Following Curricula	Computer Science: Specialisation II: Intelligence Engineering: Elective Compulsory Data Science: Specialisation IV. Special Focus Area: Elective Compulsory Data Science: Specialisation III. Applications: Elective Compulsory Electrical Engineering: Specialisation Medical Technology: Elective Compulsory Computer Science in Engineering: Specialisation I. Computer Science: Elective Compulsory Interdisciplinary Mathematics: Specialisation Computational Methods in Biomedical Imaging: Compulsory Microelectronics and Microsystems: Specialisation Communication and Signal Processing: Elective Compulsory Technomathematics: Specialisation II. Informatics: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Bio- and Medical Technology: Elective Compulsory

Course L1694: Medical Imaging

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Tobias Knopp
Language	EN
Cycle	WiSe
Content	Overview about different imaging methods Signal processing Inverse problems Computed tomography Magnetic resonance imaging Compressed Sensing Magnetic particle imaging
Literature	Bildgebende Verfahren in der Medizin; O. Dössel; Springer, Berlin, 2000 Bildgebende Systeme für die medizinische Diagnostik; H. Morneburg (Hrsg.); Publicis MCD, München, 1995 Introduction to the Mathematics of Medical Imaging; C. L.Epstein; Siam, Philadelphia, 2008 Medical Image Processing, Reconstruction and Restoration; J. Jan; Taylor and Francis, Boca Raton, 2006 Principles of Magnetic Resonance Imaging; Z.-P. Liang and P. C. Lauterbur; IEEE Press, New York, 1999

Course L1695: Medical Imaging

Typ	Recitation Section (small)
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Tobias Knopp
Language	EN
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Module M0625: Databases

Courses

Title	Typ	Hrs/wk	CP
Databases (L0337)	Lecture	3	4
Databases - Exercise (L1150)	Recitation Section (small)	2	2

Module Responsible	Prof. Stefan Schulte
Admission Requirements	None
Recommended Previous Knowledge	Students should have basic knowledge in the following areas: Discrete Algebraic Structures Procedural Programming Automata Theory and Formal Languages Programming Paradigms
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	After successful completion of the course, students know: Introduction to database systems Design instruments for relational databases, especially entity-relationship The relational model Relational query languages, especially SQL Normalization Physical data organization Transaction management Query optimization Data representation Object-oriented and object-relational databases Paradigms and concepts of current technologies for data modelling and database systems
Skills	The students acquire the ability to model a database and to work with it. This comprises especially the application of design methodologies and query and definition languages. Furthermore, students are able to apply basic functionalities needed to run a database.
Personal Competence
Social Competence	Students can work on complex problems both independently and in teams. They can exchange ideas with each other and use their individual strengths to solve the problem.
Autonomy	Students are able to independently investigate a complex problem and assess which competencies are required to solve it.
Workload in Hours	Independent Study Time 110, Study Time in Lecture 70
Credit points	6
Course achievement	None
Examination	Written exam
Examination duration and scale	90 min
Assignment for the Following Curricula	General Engineering Science (German program, 7 semester): Specialisation Data Science: Compulsory Computer Science: Core Qualification: Compulsory Data Science: Core Qualification: Compulsory Engineering Science: Specialisation Data Science: Compulsory Engineering Science: Specialisation Information and Communication Systems: Elective Compulsory Computer Science in Engineering: Specialisation I. Computer Science: Elective Compulsory Technomathematics: Specialisation II. Informatics: Elective Compulsory

Course L0337: Databases

Typ	Lecture
Hrs/wk	3
CP	4
Workload in Hours	Independent Study Time 78, Study Time in Lecture 42
Lecturer	Prof. Stefan Schulte
Language	EN
Cycle	WiSe
Content	Introduction to database systems Design instruments for relational databases, especially entity-relationship The relational model Relational query languages, especially SQL Normalization Physical data organization Transaction management Query optimization Data representation Object-oriented and object-relational databases Paradigms and concepts of current technologies for data modelling and database systems
Literature	A. Kemper, A. Eickler, Datenbanksysteme, 10. Auflage, De Gruyter, Oldenbourg, 2015 R. Elmasri, S. B. Navathe, Fundamentals of Database Systems, 7th edition, Pearson, 2016

Course L1150: Databases - Exercise

Typ	Recitation Section (small)
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Stefan Schulte
Language	EN
Cycle	WiSe
Content	Introduction to database systems Design instruments for relational databases, especially entity-relationship The relational model Relational query languages, especially SQL Normalization Physical data organization Transaction management Query optimization Data representation Object-oriented and object-relational databases Paradigms and concepts of current technologies for data modelling and database systems
Literature	A. Kemper, A. Eickler, Datenbanksysteme, 10. Auflage, De Gruyter, Oldenbourg, 2015 R. Elmasri, S. B. Navathe, Fundamentals of Database Systems, 7th edition, Pearson, 2016

Module M2046: Introduction to Quantum Computing

Courses

Title	Typ	Hrs/wk	CP
Introduction to Quantum Computing (L3109)	Lecture	2	3
Introduction to Quantum Computing (L3110)	Recitation Section (large)	2	3

Module Responsible

Prof. Martin Kliesch

Admission Requirements

None

Recommended Previous Knowledge

Linear algebra and very good mathematical skills
Prior knowledge in theoretical computer science or quantum mechanics is helpful but not required

Educational Objectives

After taking part successfully, students have reached the following learning results

Professional Competence

Knowledge

Quantum computing is among the most exciting applications of quantum mechanics. Quantum algorithms can efficiently solve computational problems that have a prohibitive runtime on traditional computers. Such problems include, for instance, factoring of integer numbers or energy estimation problems from quantum chemistry and material science.

This course provides an introduction to the topic. An emphasis will be put on conceptual and mathematical aspects.

Skills

Rigorous understanding of how quantum algorithms work and the ability to analyze them
Connection of concepts in quantum mechanics and computer science
Basic knowledge required to start programming a quantum computer
Ability to solve exercises related to quantum algorithms

Personal Competence

Social Competence

After completing this module, students are expected to be able to work on subject-specific tasks alone or in a group and to present the results appropriately. Moreover, students will be trained to identify and defuse misleading statements related to quantum computing, which can often be found in popular media.

Autonomy

After completion of this module, students are able to work out sub-areas of the subject independently using textbooks and other literature, to summarize and present the acquired knowledge and to link it to the contents of other courses.

Workload in Hours

Independent Study Time 124, Study Time in Lecture 56

Credit points

Course achievement

Compulsory	Bonus	Form	Description
No	15 %	Excercises

Examination

Written exam

Examination duration and scale

120 min

Assignment for the Following Curricula

General Engineering Science (German program, 7 semester): Specialisation Computer Science: Elective Compulsory
General Engineering Science (German program, 7 semester): Specialisation Data Science: Elective Compulsory
Computer Science: Specialisation II. Mathematics and Engineering Science: Elective Compulsory
Data Science: Specialisation I. Mathematics/Computer Science: Elective Compulsory
Engineering Science: Specialisation Data Science: Elective Compulsory
Engineering Science: Specialisation Information and Communication Systems: Elective Compulsory
Engineering Science: Specialisation Mechatronics: Elective Compulsory
Computer Science in Engineering: Specialisation I. Computer Science: Elective Compulsory
Technomathematics: Specialisation II. Informatics: Elective Compulsory

Course L3109: Introduction to Quantum Computing

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Martin Kliesch
Language	EN
Cycle	WiSe
Content	Quantum computing is among the most exciting applications of quantum mechanics. Quantum algorithms can efficiently solve computational problems that have a prohibitive runtime on traditional computers. Such problems include, for instance, factoring of integer numbers or energy estimation problems from quantum chemistry and material science. This course provides an introduction to the topic. An emphasis will be put on conceptual and mathematical aspects.
Literature	Course specific lecture notes will be provided Nielsen and Chuang, Quantum Computation and Quantum Information Sevag Gharibian’s lecture notes, Introduction to Quantum Computation

Course L3110: Introduction to Quantum Computing

Typ	Recitation Section (large)
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Martin Kliesch
Language	EN
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Module M1977: Logic in Computer Science

Courses

Title	Typ	Hrs/wk	CP
Logic in Computer Science (L3225)	Lecture	2	3
Logic in Computer Science (L3232)	Recitation Section (small)	2	3

Module Responsible	Prof. Antoine Mottet
Admission Requirements	None
Recommended Previous Knowledge	Automata theory and formal languages
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	The students know: propositional logic and its applications, the declarative languages Datalog and Prolog, the classical modal and temporal logics and their semantics.
Skills	Students are able to employ the language of logic to formalize specifications of information systems.
Personal Competence
Social Competence	Students are able to solve specific problems alone or in a group and to present the results accordingly.
Autonomy	Students are able to acquire new knowledge from specific standard books and to associate the acquired knowledge to other classes.
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Credit points	6
Course achievement	None
Examination	Oral exam
Examination duration and scale	30 min
Assignment for the Following Curricula	Computer Science: Specialisation II. Mathematics and Engineering Science: Elective Compulsory Data Science: Specialisation I. Mathematics/Computer Science: Elective Compulsory Computer Science in Engineering: Specialisation I. Computer Science: Elective Compulsory Technomathematics: Specialisation II. Informatics: Elective Compulsory

Course L3225: Logic in Computer Science

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Antoine Mottet
Language	EN
Cycle	SoSe
Content	This course will cover some topics from mathematical logic that are relevant for computer scientists. These topics include for example: Logic programming, a logical paradigm used to write programs in a declarative form instead of the typical imperative or functional programming paradigms, Modal logics, the logic of possibility and necessity. These logics are used for example to formally describe the states of a system that can evolve, Temporal logics (LTL, CTL), close relatives to modal logics and which are for examples used to describe specifications that a system should satisfy at every point in time.
Literature	Logik für Informatiker, Martin Kreuzer u. Stefan Kühling

Course L3232: Logic in Computer Science

Typ	Recitation Section (small)
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Antoine Mottet
Language	EN
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Module M1965: Mathematics of Constraint Satisfaction

Courses

Title	Typ	Hrs/wk	CP
Mathematics of Constraint Satisfaction (L3209)	Lecture	2	3
Mathematics of Constraint Satisfaction (L3210)	Recitation Section (small)	2	3

Module Responsible	Prof. Antoine Mottet
Admission Requirements	None
Recommended Previous Knowledge	The students should have followed the courses Computability and Complexity Theory, Discrete Algebraic Structures.
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge
Skills
Personal Competence
Social Competence
Autonomy
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Credit points	6
Course achievement	None
Examination	Oral exam
Examination duration and scale	30 min
Assignment for the Following Curricula	Computer Science: Specialisation III. Mathematics: Elective Compulsory Technomathematics: Specialisation II. Informatics: Elective Compulsory

Course L3209: Mathematics of Constraint Satisfaction

Typ

Lecture

Hrs/wk

Workload in Hours

Independent Study Time 62, Study Time in Lecture 28

Lecturer

Prof. Antoine Mottet

Language

Cycle

SoSe

Content

This course gives an introduction to the topic of constraint satisfaction problems and their complexity. A constraint satisfaction problem (CSP) is a computational problem of the form "Given variables and constraints on the variables, does there exist an assignment of the variables to some concrete domain that satisfies all the constraints?" The framework of CSPs is very general and captures a variety of problems from boolean satisfiability to graph coloring problems, including linear programming or even solving Sudokus. In fact, every computational problem is equivalent to a CSP. The study of CSPs has been very prolific in the past, both in practice (e.g., with SAT solvers) and in complexity theory, a prominent field of theoretical computer science. The research of the last three decades has shown an intimate connection between CSPs and various fields of mathematics, in particular with universal algebra.

In this course, we will review the theoretical aspects of CSPs. The course will cover the basics of the theory such as the universal-algebraic approach to constraint satisfaction and several classical algorithms such as local consistency checking and the Bulatov-Dalmau algorithm.

Basic knowledge in predicate logic and an affinity to abstract mathematical thinking are highly recommended in order to follow this course.

Literature

A script for the lecture
Polymorphisms and How to Use Them, Barto, Krokhin, Willard.

Course L3210: Mathematics of Constraint Satisfaction

Typ	Recitation Section (small)
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Antoine Mottet
Language	EN
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Module M0668: Algebra and Control

Courses

Title	Typ	Hrs/wk	CP
Algebra and Control (L0428)	Lecture	2	4
Algebra and Control (L0429)	Recitation Section (small)	2	2

Module Responsible	Dr. Prashant Batra
Admission Requirements	None
Recommended Previous Knowledge	Basics of Real Analysis and Linear Algebra of Vector Spaces and either of: Introduction to Control Theory or: Discrete Mathematics
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students can Describe input-output systems polynomially Explain factorization approaches to transfer functions Name stabilization conditions for systems in coprime stable factorization.
Skills	Students are able to Undertake a synthesis of stable control loops Apply suitable methods of analysis and synthesis to describe all stable control loops Ensure the fulfillment of specified performance measurements.
Personal Competence
Social Competence	After completing the module, students are able to solve subject-related tasks and to present the results.
Autonomy	Students are provided with tasks which are exam-related so that they can examine their learning progress and reflect on it.
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Credit points	6
Course achievement	None
Examination	Oral exam
Examination duration and scale	30 min
Assignment for the Following Curricula	Computer Science: Specialisation II. Mathematics and Engineering Science: Elective Compulsory Technomathematics: Specialisation II. Informatics: Elective Compulsory

Course L0428: Algebra and Control

Typ	Lecture
Hrs/wk	2
CP	4
Workload in Hours	Independent Study Time 92, Study Time in Lecture 28
Lecturer	Dr. Prashant Batra
Language	DE/EN
Cycle	SoSe
Content	- Algebraic control methods, polynomial and fractional approach -Single input - single output (SISO) control systems synthesis by algebraic methods, - Simultaneous stabilization - Parametrization of all stabilizing controllers - Selected methods of pole assignment. - Filtering and sensitivity minimization - Polynomial matrices, left and right polynomial fractions. - Euclidean algorithm, diophantine equations over rings - Smith-McMillan normal form - Multiple input - multiple output control system synthesis by polynomial methods, condition of stability.
Literature	Vidyasagar, M.: Control system synthesis: a factorization approach. The MIT Press,Cambridge/Mass. - London, 1985. Vardulakis, A.I.G.: Linear multivariable control. Algebraic analysis and synthesis methods, John Wiley & Sons,Chichester,UK,1991. Chen, Chi-Tsong: Analog and digital control system design. Transfer-function, state-space, and algebraic methods. Oxford Univ. Press,1995. Kučera, V.: Analysis and Design of Discrete Linear Control Systems. Praha: Academia, 1991.

Course L0429: Algebra and Control

Typ	Recitation Section (small)
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Dr. Prashant Batra
Language	DE/EN
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Module M0754: Compiler Construction

Courses

Title	Typ	Hrs/wk	CP
Compiler Construction (L0703)	Lecture	2	2
Compiler Construction (L0704)	Recitation Section (small)	2	4

Module Responsible	Prof. Sibylle Schupp
Admission Requirements	None
Recommended Previous Knowledge	Practical programming experience Automata theory and formal languages Functional programming or procedural programming Object-oriented programming, algorithms, and data structures Basic knowledge of software engineering
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students explain the workings of a compiler and break down a compilation task in different phases. They apply and modify the major algorithms for compiler construction and code improvement. They can re-write those algorithms in a programming language, run and test them. They choose appropriate internal languages and representations and justify their choice. They explain and modify implementations of existing compiler frameworks and experiment with frameworks and tools.
Skills	Students design and implement arbitrary compilation phases. They integrate their code in existing compiler frameworks. They organize their compiler code properly as a software project. They generalize algorithms for compiler construction to algorithms that analyze or synthesize software.
Personal Competence
Social Competence	Students develop the software in a team. They explain problems and solutions to their team members. They present and defend their software in class. They communicate in English.
Autonomy	Students develop their software independently and define milestones by themselves. They receive feedback throughout the entire project. They organize the software project so that they can assess their progress themselves.
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Credit points	6
Course achievement	None
Examination	Subject theoretical and practical work
Examination duration and scale	Software (Compiler)
Assignment for the Following Curricula	Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory Computer Science in Engineering: Specialisation I. Computer Science: Elective Compulsory Technomathematics: Specialisation II. Informatics: Elective Compulsory

Course L0703: Compiler Construction

Typ	Lecture
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Sibylle Schupp
Language	EN
Cycle	SoSe
Content	Lexical and syntactic analysis Semantic analysis High-level optimization Intermediate languages and code generation Compilation pipeline
Literature	Alfred Aho, Jeffrey Ullman, Ravi Sethi, and Monica S. Lam, Compilers: Principles, Techniques, and Tools, 2nd edition Aarne Ranta, Implementing Programming Languages, An Introduction to Compilers and Interpreters, with an appendix coauthored by Markus Forsberg, College Publications, London, 2012

Course L0704: Compiler Construction

Typ	Recitation Section (small)
Hrs/wk	2
CP	4
Workload in Hours	Independent Study Time 92, Study Time in Lecture 28
Lecturer	Prof. Sibylle Schupp
Language	EN
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Module M0562: Computability and Complexity Theory

Courses

Title	Typ	Hrs/wk	CP
Computability and Complexity Theory (L0166)	Lecture	2	3
Computability and Complexity Theory (L0167)	Recitation Section (small)	2	3

Module Responsible

Prof. Martin Kliesch

Admission Requirements

None

Recommended Previous Knowledge

Discrete Algebraic Structures, Automata Theory, Logic, and Formal Language Theory

Educational Objectives

After taking part successfully, students have reached the following learning results

Professional Competence

Knowledge

To goal is this course is to gain some basic understanding of the limits of computation and, in particular, knowledge and understanding of the topics of the associated Lehrveranstaltungen.

Skills

After completing this module, students are able to

reproduce the knowledge taught in the course,
reproduce simpler proofs of the course and reproduce the ideas of the more complicated ones,
establish connections between the concepts taught, and
apply the learned knowledge to concrete problems.

Personal Competence

Social Competence

After completing this module, students are able to work on subject-specific tasks alone or in a group and to present the results appropriately.

Autonomy

After completion of this module, students are able to work out sub-areas of the subject area independently on the basis of textbooks and other literature, to summarize and present the acquired knowledge and to link it to the contents of other courses.

Workload in Hours

Independent Study Time 124, Study Time in Lecture 56

Credit points

Course achievement

Compulsory	Bonus	Form	Description
Yes	15 %	Excercises

Examination

Written exam

Examination duration and scale

120 min

Assignment for the Following Curricula

Course L0166: Computability and Complexity Theory

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Martin Kliesch
Language	EN
Cycle	SoSe
Content	Basic models of computation (finite state machines, Turing machines) Decision problems and formal languages Church Turing thesis Decidability of problems related to computational models (acceptance, emptyness and equivalence problems for DFAs, CFGs, LBAs, TMs) Undecidable problems such as the halting problem, diagonalization (Mapping) reducibility The computation history method and the Post correspondence problem Time complexity, model dependence, class P, example graph problems in P Class NP (2 definitions + equivalence) Polynomial time mapping reductions, NP-completeness Problems: Hamiltonian path, k-clique, SAT, 3SAT Cook-Levin theorem (SAT and 3SAT) Probabilistic Turing machines, class BPP Read once branching programs (ROBPs), arithmetization, the equivalence problem of ROBPs Space complexity, classes PSPACE True quantified Boolean formulae are PSPACE-complete NPSPACE and Savitch's theorem with proof idea The generalized geography game
Literature	Michael Sipser, Introduction to the Theory of Computation

Course L0167: Computability and Complexity Theory

Typ	Recitation Section (small)
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Martin Kliesch
Language	EN
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Specialization III. Engineering Science

Module M0706: Geotechnics I

Courses

Title	Typ	Hrs/wk	CP
Soil Mechanics (L0550)	Lecture	2	2
Soil Mechanics (L0551)	Recitation Section (large)	2	2
Soil Mechanics (L1493)	Recitation Section (small)	2	2

Module Responsible

Prof. Jürgen Grabe

Admission Requirements

None

Recommended Previous Knowledge

Modules :

Mechanics I-II

Educational Objectives

After taking part successfully, students have reached the following learning results

Professional Competence

Knowledge

The students know the basics of soil mechanics as the structure and characteristics of soil, stress distribution due to weight, water or structures, consolidation and settlement calculations, as well as failure of the soil due to ground- or slope failure.

Skills

After the successful completion of the module the students should be able to describe the mechanical properties and to evaluate them with the help of geotechnical standard tests. They can calculate stresses and deformation in the soils due to weight or influence of structures. They are are able to prove the usability (settlements) for shallow foundations.

Personal Competence

Social Competence

Autonomy

Workload in Hours

Independent Study Time 96, Study Time in Lecture 84

Credit points

Course achievement

Compulsory	Bonus	Form	Description
No	20 %	Attestation

Examination

Written exam

Examination duration and scale

90 minutes

Assignment for the Following Curricula

General Engineering Science (German program, 7 semester): Specialisation Civil Engineering: Compulsory
Civil- and Environmental Engineering: Core Qualification: Compulsory
Logistics and Mobility: Specialisation Traffic Planning and Systems: Elective Compulsory
Technomathematics: Specialisation III. Engineering Science: Elective Compulsory
Engineering and Management - Major in Logistics and Mobility: Specialisation Traffic Planning and Systems: Elective Compulsory

Course L0550: Soil Mechanics

Typ	Lecture
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Jürgen Grabe
Language	DE
Cycle	WiSe
Content	Structure of the soil Ground surveying Compsitition and properties of the soil Groundwater One-dimensional compression Spreading of stresses Settlement calculation Consolidation Shear strength Earth pressure Slope failure Ground failure Suspension based earth tenches
Literature	Vorlesungsumdruck, s. ww.tu-harburg.de/gbt Grabe, J. (2004): Bodenmechanik und Grundbau Gudehus, G. (1981): Bodenmechanik Kolymbas, D. (1998): Geotechnik - Bodenmechanik und Grundbau Grundbau-Taschenbuch, Teil 1, aktuelle Auflage

Course L0551: Soil Mechanics

Typ	Recitation Section (large)
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Jürgen Grabe
Language	DE
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Course L1493: Soil Mechanics

Typ	Recitation Section (small)
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Jürgen Grabe
Language	DE
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Module M0536: Fundamentals of Fluid Mechanics

Courses

Title	Typ	Hrs/wk	CP
Fundamentals of Fluid Mechanics (L0091)	Lecture	2	2
Fundamentals on Fluid Mechanics (L2933)	Recitation Section (small)	2	2
Fluid Mechanics for Process Engineering (L0092)	Recitation Section (large)	2	2

Module Responsible

Prof. Michael Schlüter

Admission Requirements

None

Recommended Previous Knowledge

Mathematics I+II+III
Technical Mechanics I+II
Technical Thermodynamics I+II
Working with force balances
Simplification and solving of partial differential equations
Integration

Educational Objectives

After taking part successfully, students have reached the following learning results

Professional Competence

Knowledge

Students are able to:

explain the difference between different types of flow
give an overview for different applications of the Reynolds Transport-Theorem in process engineering
explain simplifications of the Continuity- and Navier-Stokes-Equation by using physical boundary conditions

Skills

The students are able to

describe and model incompressible flows mathematically
reduce the governing equations of fluid mechanics by simplifications to archive quantitative solutions e.g. by integration
notice the dependency between theory and technical applications
use the learned basics for fluid dynamical applications in fields of process engineering

Personal Competence

Social Competence

The students

are capable to gather information from subject related, professional publications and relate that information to the context of the lecture and
able to work together on subject related tasks in small groups. They are able to present their results effectively in English (e.g. during small group exercises)
are able to work out solutions for exercises by themselves, to discuss the solutions orally and to present the results.

Autonomy

The students are able to

search further literature for each topic and to expand their knowledge with this literature,
work on their exercises by their own and to evaluate their actual knowledge with the feedback.

Workload in Hours

Independent Study Time 96, Study Time in Lecture 84

Credit points

Course achievement

Compulsory	Bonus	Form	Description
No	5 %	Midterm

Examination

Written exam

Examination duration and scale

3 hours

Assignment for the Following Curricula

General Engineering Science (German program, 7 semester): Specialisation Green Technologies: Compulsory
General Engineering Science (German program, 7 semester): Specialisation Chemical and Bioengineering: Compulsory
Bioprocess Engineering: Core Qualification: Compulsory
Chemical and Bioprocess Engineering: Core Qualification: Compulsory
Green Technologies: Energy, Water, Climate: Core Qualification: Compulsory
Integrated Building Technology: Core Qualification: Compulsory
Logistics and Mobility: Specialisation Traffic Planning and Systems: Elective Compulsory
Technomathematics: Specialisation III. Engineering Science: Elective Compulsory
Process Engineering: Core Qualification: Compulsory
Engineering and Management - Major in Logistics and Mobility: Specialisation Traffic Planning and Systems: Elective Compulsory

Course L0091: Fundamentals of Fluid Mechanics

Typ	Lecture
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Michael Schlüter
Language	DE
Cycle	SoSe
Content	fluid properties hydrostatic overall balances - theory of streamline overall balances- conservation equations differential balances - Navier Stokes equations irrotational flows - Potenzialströmungen flow around bodies - theory of physical similarity turbulent flows compressible flows
Literature	Crowe, C. T.: Engineering fluid mechanics. Wiley, New York, 2009. Durst, F.: Strömungsmechanik: Einführung in die Theorie der Strömungen von Fluiden. Springer-Verlag, Berlin, Heidelberg, 2006. Fox, R.W.; et al.: Introduction to Fluid Mechanics. J. Wiley & Sons, 1994 Herwig, H.: Strömungsmechanik: Eine Einführung in die Physik und die mathematische Modellierung von Strömungen. Springer Verlag, Berlin, Heidelberg, New York, 2006 Herwig, H.: Strömungsmechanik: Einführung in die Physik von technischen Strömungen: Vieweg+Teubner Verlag / GWV Fachverlage GmbH, Wiesbaden, 2008 Kuhlmann, H.C.: Strömungsmechanik. München, Pearson Studium, 2007 Oertl, H.: Strömungsmechanik: Grundlagen, Grundgleichungen, Lösungsmethoden, Softwarebeispiele. Vieweg+ Teubner Verlag / GWV Fachverlage GmbH, Wiesbaden, 2009 Schade, H.; Kunz, E.: Strömungslehre. Verlag de Gruyter, Berlin, New York, 2007 Truckenbrodt, E.: Fluidmechanik 1: Grundlagen und elementare Strömungsvorgänge dichtebeständiger Fluide. Springer-Verlag, Berlin, Heidelberg, 2008 Schlichting, H. : Grenzschicht-Theorie. Springer-Verlag, Berlin, 2006 van Dyke, M.: An Album of Fluid Motion. The Parabolic Press, Stanford California, 1882. White, F.: Fluid Mechanics, Mcgraw-Hill, ISBN-10: 0071311211, ISBN-13: 978-0071311212, 2011

Course L2933: Fundamentals on Fluid Mechanics

Typ	Recitation Section (small)
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Michael Schlüter
Language	DE
Cycle	SoSe
Content	In the group exercise, the contents of the lecture are taken up and deepened by means of exercises. The exercise tasks correspond in quality and scope to the tasks of the written exam. Topics: Reynolds transport-theorem, pipe flow, free jet, angular momentum, Navier-Stokes equations, potential theory, mock exam, pipe hydraulics, pump design.
Literature	Heinz Herwig: Strömungsmechanik, Eine Einführung in die Physik und die mathematische Modellierung von Strömungen, Springer Verlag, Berlin, 978-3-540-32441-6 (ISBN) Herbert Oertel, Martin Böhle, Thomas Reviol: Strömungsmechanik für Ingenieure und Naturwissenschaftler, Springer Verlag, Berlin, ISBN: 978-3-658-07786-0 Joseph Spurk, Nuri Aksel: Strömungslehre, Einführung in die Theorie der Strömungen, Springer Verlag, Berlin, ISBN: 978-3-642-13143-1.

Course L0092: Fluid Mechanics for Process Engineering

Typ	Recitation Section (large)
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Michael Schlüter
Language	DE
Cycle	SoSe
Content	In the exercise-lecture the topics from the main lecture are discussed intensively and transferred into application. For that, the students receive example tasks for download. The students solve these problems based on the lecture material either independently or in small groups. The solution is discussed with the students under scientific supervision and parts of the solutions are presented on the chalk board. At the end of each exercise-lecture, the correct solution is presented on the chalk board. Parallel to the exercise-lecture tutorials are held where the student solve exam questions under a set time-frame in small groups and discuss the solutions afterwards.
Literature	Crowe, C. T.: Engineering fluid mechanics. Wiley, New York, 2009. Durst, F.: Strömungsmechanik: Einführung in die Theorie der Strömungen von Fluiden. Springer-Verlag, Berlin, Heidelberg, 2006. Fox, R.W.; et al.: Introduction to Fluid Mechanics. J. Wiley & Sons, 1994 Herwig, H.: Strömungsmechanik: Eine Einführung in die Physik und die mathematische Modellierung von Strömungen. Springer Verlag, Berlin, Heidelberg, New York, 2006 Herwig, H.: Strömungsmechanik: Einführung in die Physik von technischen Strömungen: Vieweg+Teubner Verlag / GWV Fachverlage GmbH, Wiesbaden, 2008 Kuhlmann, H.C.: Strömungsmechanik. München, Pearson Studium, 2007 Oertl, H.: Strömungsmechanik: Grundlagen, Grundgleichungen, Lösungsmethoden, Softwarebeispiele. Vieweg+ Teubner Verlag / GWV Fachverlage GmbH, Wiesbaden, 2009 Schade, H.; Kunz, E.: Strömungslehre. Verlag de Gruyter, Berlin, New York, 2007 Truckenbrodt, E.: Fluidmechanik 1: Grundlagen und elementare Strömungsvorgänge dichtebeständiger Fluide. Springer-Verlag, Berlin, Heidelberg, 2008 Schlichting, H. : Grenzschicht-Theorie. Springer-Verlag, Berlin, 2006 van Dyke, M.: An Album of Fluid Motion. The Parabolic Press, Stanford California, 1882. White, F.: Fluid Mechanics, Mcgraw-Hill, ISBN-10: 0071311211, ISBN-13: 978-0071311212, 2011

Module M0634: Introduction into Medical Technology and Systems

Courses

Title	Typ	Hrs/wk	CP
Introduction into Medical Technology and Systems (L0342)	Lecture	2	3
Introduction into Medical Technology and Systems (L0343)	Project Seminar	2	2
Introduction into Medical Technology and Systems (L1876)	Recitation Section (large)	1	1

Module Responsible

Prof. Alexander Schlaefer

Admission Requirements

None

Recommended Previous Knowledge

principles of math (algebra, analysis/calculus)
principles of stochastics
principles of programming, R/Matlab

Educational Objectives

After taking part successfully, students have reached the following learning results

Professional Competence

Knowledge

The students can explain principles of medical technology, including imaging systems, computer aided surgery, and medical information systems. They are able to give an overview of regulatory affairs and standards in medical technology.

Skills

The students are able to evaluate systems and medical devices in the context of clinical applications.

Personal Competence

Social Competence

The students describe a problem in medical technology as a project, and define tasks that are solved in a joint effort.
The students can critically reflect on the results of other groups and make constructive suggestions for improvement.

Autonomy

The students can assess their level of knowledge and document their work results. They can critically evaluate the results achieved and present them in an appropriate manner.

Workload in Hours

Independent Study Time 110, Study Time in Lecture 70

Credit points

Course achievement

Compulsory	Bonus	Form	Description
Yes	10 %	Written elaboration
Yes	10 %	Presentation

Examination

Written exam

Examination duration and scale

90 minutes

Assignment for the Following Curricula

General Engineering Science (German program, 7 semester): Specialisation Biomedical Engineering: Compulsory
Computer Science: Specialisation II. Mathematics and Engineering Science: Elective Compulsory
Data Science: Specialisation II. Application: Elective Compulsory
Data Science: Core Qualification: Elective Compulsory
Electrical Engineering: Core Qualification: Elective Compulsory
Engineering Science: Specialisation Biomedical Engineering: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Biomedical Engineering: Compulsory
Computer Science in Engineering: Specialisation II. Mathematics & Engineering Science: Elective Compulsory
Mechatronics: Specialisation Medical Engineering: Compulsory
Biomedical Engineering: Specialisation Artificial Organs and Regenerative Medicine: Elective Compulsory
Biomedical Engineering: Specialisation Implants and Endoprostheses: Elective Compulsory
Biomedical Engineering: Specialisation Medical Technology and Control Theory: Elective Compulsory
Biomedical Engineering: Specialisation Management and Business Administration: Elective Compulsory
Technomathematics: Specialisation III. Engineering Science: Elective Compulsory

Course L0342: Introduction into Medical Technology and Systems

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Alexander Schlaefer
Language	DE
Cycle	SoSe
Content	- imaging systems - computer aided surgery - medical sensor systems - medical information systems - regulatory affairs - standard in medical technology The students will work in groups to apply the methods introduced during the lecture using problem based learning.
Literature	Bernhard Priem, "Visual Computing for Medicine", 2014 Heinz Handels, "Medizinische Bildverarbeitung", 2009 (https://katalog.tub.tuhh.de/Record/745558097) Valery Tuchin, "Tissue Optics - Light Scattering Methods and Instruments for Medical Diagnosis", 2015 Olaf Drössel, "Biomedizinische Technik - Medizinische Bildgebung", 2014 H. Gross, "Handbook of Optical Systems", 2008 (https://katalog.tub.tuhh.de/Record/856571687) Wolfgang Drexler, "Optical Coherence Tomography", 2008 Kramme, "Medizintechnik", 2011 Thorsten M. Buzug, "Computed Tomography", 2008 Otmar Scherzer, "Handbook of Mathematical Methods in Imaging", 2015 Weishaupt, "Wie funktioniert MRI?", 2014 Paul Suetens, "Fundamentals of Medical Imaging", 2009 Vorlesungsunterlagen

Course L0343: Introduction into Medical Technology and Systems

Typ	Project Seminar
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Alexander Schlaefer
Language	DE
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Course L1876: Introduction into Medical Technology and Systems

Typ	Recitation Section (large)
Hrs/wk	1
CP	1
Workload in Hours	Independent Study Time 16, Study Time in Lecture 14
Lecturer	Prof. Alexander Schlaefer
Language	DE
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Module M0680: Fluid Dynamics

Courses

Title	Typ	Hrs/wk	CP
Fluid Mechanics (L0454)	Lecture	3	4
Fluid Mechanics (L0455)	Recitation Section (large)	2	2

Module Responsible	Prof. Thomas Rung
Admission Requirements	None
Recommended Previous Knowledge	Students should have sound knowledge of engineering mathematics, engineering mechanics and thermodynamics.
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students will have the required sound knowledge to explain the general principles of fluid engineering and physics of fluids. They are familiar with the similarities and differences between fluid mechanics and neighbouring subjects (thermodynamics, structural mechanics). Students can scientifically outline the rationale of flow physics using mathematical models. They are familiar with most performance analysis methods -in particular their realms and limitations- and the prediction of fluid engineering devices.
Skills	Students are able to apply fluid-engineering principles and flow-physics models for the analysis of technical systems. They are able to explain physical relationships used to design fluid engineering devices. The lecture enables the student to carry out all necessary theoretical calculations for the fluid dynamic design of engineering devices on a scientific level.
Personal Competence
Social Competence	The students are able to discuss problems, present the results of their own analysis, and jointly develop solution strategies that address given technical goals.
Autonomy	The students are able to develop solution strategies for complex problems self-consistent. They are able to critically analyse own results as well as external data with regards to the plausibility and reliability.
Workload in Hours	Independent Study Time 110, Study Time in Lecture 70
Credit points	6
Course achievement	None
Examination	Written exam
Examination duration and scale	180 min
Assignment for the Following Curricula	General Engineering Science (German program, 7 semester): Specialisation Mechanical Engineering: Compulsory General Engineering Science (German program, 7 semester): Specialisation Biomedical Engineering: Compulsory General Engineering Science (German program, 7 semester): Specialisation Naval Architecture: Compulsory Mechanical Engineering: Core Qualification: Compulsory Naval Architecture: Core Qualification: Compulsory Technomathematics: Specialisation III. Engineering Science: Elective Compulsory

Course L0454: Fluid Mechanics

Typ	Lecture
Hrs/wk	3
CP	4
Workload in Hours	Independent Study Time 78, Study Time in Lecture 42
Lecturer	Prof. Thomas Rung
Language	DE/EN
Cycle	SoSe
Content	continuum physics definition of fluids, difference to solids/structures and material properties of fluids dimensional analysis and similitude fluid forces and fluid statics transport and conservation of mass, momentum & energy fluid kinematics technically relevant flow models for incompressible fluids control volume & stream tube analysis vortical flow models potential flows boundary layer flows different types of conservation equations and their realm (Navier-Stokes/Euler/Bernoulli equations) analytical solutions for Navier-Stokes systems Analysis of internal flows (channels, pipes, open channels) and external flows, fundamentals of wing aerodynamics turbulent flows fundamentals of gas dynamics (1D compressible flows)
Literature	the course primarily refers to / das Modul stütz sich bevorzugt auf : Munson, B.R.; Rothmayer, A.P.; Okiishi, T.H.; Huebsch, W.W.: Fundamentals of Fluid Mechanics, John Wiley & Sons. Spurk, J.; Aksel, N.: Strömungslehre, Springer. Schade, H.; Kunz, E., Kameier, F.; Paschereit, C.O.: Strömungslehere, De Gruyter. Herwig, H.: Strömungsmechanik, Springer. Herwig, H.: Strömungsmechanik von A-Z, Vieweg.

Course L0455: Fluid Mechanics

Typ	Recitation Section (large)
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Thomas Rung
Language	DE/EN
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Module M0757: Biochemistry and Microbiology

Courses

Title	Typ	Hrs/wk	CP
Biochemistry (L0351)	Lecture	2	2
Biochemistry (L0728)	Project-/problem-based Learning	1	1
Microbiology (L0881)	Lecture	2	2
Microbiology (L0888)	Project-/problem-based Learning	1	1

Module Responsible	Prof. Johannes Gescher
Admission Requirements	None
Recommended Previous Knowledge	none
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	At the end of this module the students can: - explain the methods of biological and biochemical research to determine the properties of biomolecules - name the basic components of a living organism - explain the principles of metabolism - describe the structure of living cells -
Skills
Personal Competence
Social Competence	The students are able, - to gather knowledge in groups of about 10 students - to introduce their own knowledge and to argue their view in discussions in teams - to divide a complex task into subtasks, solve these and to present the combined results
Autonomy	The students are able to present the results of their subtasks in a written report
Workload in Hours	Independent Study Time 96, Study Time in Lecture 84
Credit points	6
Course achievement	None
Examination	Written exam
Examination duration and scale	90 min
Assignment for the Following Curricula	Bioprocess Engineering: Core Qualification: Compulsory Green Technologies: Energy, Water, Climate: Specialisation Biotechnologies: Elective Compulsory Technomathematics: Specialisation III. Engineering Science: Elective Compulsory

Course L0351: Biochemistry

Typ	Lecture
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Dr. Paul Bubenheim
Language	DE
Cycle	SoSe
Content	The molecular logic of Life Biomolecules: Amino acids, peptides, proteins Carbohydrates Lipids Protein functions, Enzymes: Michaelis-Menten kinetics Enzyme regulation Enzyme nomenclature Cofactors and cosubstrates, vitamines Metabolism: Basic principles Photosynthesis Glycolysis Citric acid cycle Respiration Anaerobic respirations Fatty acid metabolism Amino acid metabolism
Literature	Biochemie, H. Robert Horton, Laurence A. Moran, K. Gray Scrimeour, Marc D. Perry, J. David Rawn, Pearson Studium, München Prinzipien der Biochemie, A. L. Lehninger, de Gruyter Verlag Berlin

Course L0728: Biochemistry

Typ	Project-/problem-based Learning
Hrs/wk	1
CP	1
Workload in Hours	Independent Study Time 16, Study Time in Lecture 14
Lecturer	Dr. Paul Bubenheim
Language	DE
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Course L0881: Microbiology

Typ	Lecture
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Johannes Gescher
Language	DE
Cycle	SoSe
Content	1. The procaryotic cell evolution taxonomy and specific properties of Archaea, Bacteria, and viruses structure and properties of the cell growth 2. Metabolism fermentation and anaerobic respiration methanogenesis and the anaerobic food chain degradation of polymers chemolithotrophy 3. Microorganisms in relation to the environment chemotaxis and motility Elemental cycle of carbon, nitrogen and sulfur biofilms symbiotic relationships extremophiles biotechnology
Literature	• Allgemeine Mikrobiologie, 8. Aufl., 2007, Fuchs, G. (Hrsg.), Thieme Verlag (54,95 €) • Mikrobiologie, 13 Aufl., 2013, Madigan, M., Martinko, J. M., Stahl, D. A., Clark, D. P. (Hrsg.), ehemals „Brock“, Pearson Verlag (89,95 €) • Taschenlehrbuch Biologie Mikrobiologie, 2008, Munk, K. (Hrsg.), Thieme Verlag • Grundlagen der Mikrobiologie, 4. Aufl., 2010, Cypionka, H., Springer Verlag (29,95 €), http://www.grundlagen-der-mikrobiologie.icbm.de/

Course L0888: Microbiology

Typ	Project-/problem-based Learning
Hrs/wk	1
CP	1
Workload in Hours	Independent Study Time 16, Study Time in Lecture 14
Lecturer	Prof. Johannes Gescher
Language	DE
Cycle	SoSe
Content	1. The procaryotic cell evolution taxonomy and specific properties of Archaea, Bacteria, and viruses structure and properties of the cell growth 2. Metabolism fermentation and anaerobic respiration methanogenesis and the anaerobic food chain degradation of polymers chemolithotrophy 3. Microorganisms in relation to the environment chemotaxis and motility Elemental cycle of carbon, nitrogen and sulfur biofilms symbiotic relationships extremophiles biotechnology
Literature	• Allgemeine Mikrobiologie, 8. Aufl., 2007, Fuchs, G. (Hrsg.), Thieme Verlag (54,95 €) • Mikrobiologie, 13 Aufl., 2013, Madigan, M., Martinko, J. M., Stahl, D. A., Clark, D. P. (Hrsg.), ehemals „Brock“, Pearson Verlag (89,95 €) • Taschenlehrbuch Biologie Mikrobiologie, 2008, Munk, K. (Hrsg.), Thieme Verlag • Grundlagen der Mikrobiologie, 4. Aufl., 2010, Cypionka, H., Springer Verlag (29,95 €), http://www.grundlagen-der-mikrobiologie.icbm.de/

Module M0938: Bioprocess Engineering - Fundamentals

Courses

Title	Typ	Hrs/wk	CP
Bioprocess Engineering - Fundamentals (L0841)	Lecture	2	3
Bioprocess Engineering- Fundamentals (L0842)	Recitation Section (large)	2	1
Bioprocess Engineering - Fundamental Practical Course (L0843)	Practical Course	2	2

Module Responsible

Prof. Andreas Liese

Admission Requirements

None

Recommended Previous Knowledge

module "organic chemistry", module "fundamentals for process engineering"

Educational Objectives

After taking part successfully, students have reached the following learning results

Professional Competence

Knowledge

Students are able to describe the basic concepts of bioprocess engineering. They are able to classify different types of kinetics for enzymes and microorganisms, as well as to differentiate different types of inhibition. The parameters of stoichiometry and rheology can be named and mass transport processes in bioreactors can be explained. The students are capable to explain fundamental bioprocess management, sterilization technology and downstream processing in detail.

Skills

After successful completion of this module, students should be able to

describe different kinetic approaches for growth and substrate-uptake and to calculate the corresponding parameters
predict qualitatively the influence of energy generation, regeneration of redox equivalents and growth inhibition on the fermentation process
analyze bioprocesses on basis of stoichiometry and to set up / solve metabolic flux equations
distinguish between scale-up criteria for different bioreactors and bioprocesses (anaerobic, aerobic as well as microaerobic) to compare them as well as to apply them to current biotechnical problem
propose solutions to complicated biotechnological problems and to deduce the corresponding models

to explore new knowledge resources and to apply the newly gained contents
identify scientific problems with concrete industrial use and to formulate solutions.
to document and discuss their procedures as well as results in a scientific manner

Personal Competence

Social Competence

After completion of this module participants should be able to debate technical questions in small teams to enhance the ability to take position to their own opinions and increase their capacity for teamwork in engineering and scientific environments.

Autonomy

After completion of this module participants will be able to solve a technical problem in a team independently by organizing their workflow and to present their results in a plenum.

Workload in Hours

Independent Study Time 96, Study Time in Lecture 84

Credit points

Course achievement

Compulsory	Bonus	Form	Description
Yes	5 %	Subject theoretical and practical work

Examination

Written exam

Examination duration and scale

90 min

Assignment for the Following Curricula

Bioprocess Engineering: Core Qualification: Compulsory
Green Technologies: Energy, Water, Climate: Specialisation Biotechnologies: Elective Compulsory
Biomedical Engineering: Specialisation Artificial Organs and Regenerative Medicine: Compulsory
Biomedical Engineering: Specialisation Implants and Endoprostheses: Elective Compulsory
Biomedical Engineering: Specialisation Medical Technology and Control Theory: Elective Compulsory
Biomedical Engineering: Specialisation Management and Business Administration: Elective Compulsory
Technomathematics: Specialisation III. Engineering Science: Elective Compulsory
Process Engineering: Core Qualification: Compulsory

Course L0841: Bioprocess Engineering - Fundamentals

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Andreas Liese
Language	DE
Cycle	SoSe
Content	Introduction: state-of-the-art and development trends in the biotechnology, introduction to the lecture Enzyme kinetics: Michaelis-Menten, differnt types of enzyme inhibition, linearization, conversion, yield, selectivity (Prof. Liese) Stoichiometry: coefficient of respiration, electron balance, degree of reduction, coefficient of yield, theoretical oxygen demand (Prof. Liese) Microbial growth kinetic: batch- and chemostat culture (Prof. Zeng) Kinetic of subtrate consumption and product formation (Prof. Zeng) Rheology: non-newtonian fluids, viscosity, agitators, energy input (Prof. Liese) Transport process in a bioreactor (Prof. Zeng) Technology of sterilization (Prof. Zeng) Fundamentals of bioprocess management: bioreactors and calculation of batch, fed-batch and continuouse bioprocesses (Prof. Zeng/Prof. Liese) Downstream technology in biotechnology: cell breakdown, zentrifugation, filtration, aqueous two phase systems (Prof. Liese)
Literature	K. Buchholz, V. Kasche, U. Bornscheuer: Biocatalysts and Enzyme Technology, 2. Aufl. Wiley-VCH, 2012 H. Chmiel: Bioprozeßtechnik, Elsevier, 2006 R.H. Balz et al.: Manual of Industrial Microbiology and Biotechnology, 3. edition, ASM Press, 2010 H.W. Blanch, D. Clark: Biochemical Engineering, Taylor & Francis, 1997 P. M. Doran: Bioprocess Engineering Principles, 2. edition, Academic Press, 2013

Course L0842: Bioprocess Engineering- Fundamentals

Typ	Recitation Section (large)
Hrs/wk	2
CP	1
Workload in Hours	Independent Study Time 2, Study Time in Lecture 28
Lecturer	Prof. Andreas Liese
Language	DE
Cycle	SoSe
Content	1. Introduction (Prof. Liese, Prof. Zeng) 2. Enzymatic kinetics (Prof. Liese) 3. Stoichiometry I + II (Prof. Liese) 4. Microbial Kinetics I+II (Prof. Zeng) 5. Rheology (Prof. Liese) 6. Mass transfer in bioprocess (Prof. Zeng) 7. Continuous culture (Chemostat) (Prof. Zeng) 8. Sterilisation (Prof. Zeng) 9. Downstream processing (Prof. Liese) 10. Repetition (Reserve) (Prof. Liese, Prof. Zeng)
Literature	siehe Vorlesung

Course L0843: Bioprocess Engineering - Fundamental Practical Course

Typ	Practical Course
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Andreas Liese
Language	DE
Cycle	SoSe
Content	In this course fermentation and downstream technologies on the example of the production of an enzyme by means of a recombinant microorganism is learned. Detailed characterization and simulation of enzyme kinetics as well as application of the enzyme in a bioreactor is carried out. The students document their experiments and results in a protocol.
Literature	Skript

Module M1277: MED I: Introduction to Anatomy

Courses

Title	Typ	Hrs/wk	CP
Introduction to Anatomy (L0384)	Lecture	2	3

Module Responsible	Prof. Udo Schumacher
Admission Requirements	None
Recommended Previous Knowledge	Students can listen to the lectures without any prior knowledge. Basic school knowledge of biology, chemistry / biochemistry, physics and Latin can be useful.
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	The lectures are about microscopic anatomy, describing the microscopic structure of tissues and organs, and about macroscopic anatomy which is about organs and organ systems. The lectures also contain an introduction to cell biology, human development and to the central nervous system. The fundamentals of radiologic imaging are described as well, using projectional x-ray and cross-sectional images. The Latin terms are introduced.
Skills	At the end of the lecture series the students are able to describe the microscopic as well as the macroscopic assembly and functions of the human body. The Latin terms are the prerequisite to understand medical literature. This knowledge is needed to understand und further develop medical devices. These insights in human anatomy are the fundamentals to explain the role of structure and function for the development of common diseases and their impact on the human body.
Personal Competence
Social Competence	The students can participate in current discussions in biomedical research and medicine on a professional level. The Latin terms are prerequisite for communication with physicians on a professional level.
Autonomy	The lectures are an introduction to the basics of anatomy and should encourage students to improve their knowledge by themselves. Advice is given as to which further literature is suitable for this purpose. Likewise, the lecture series encourages students to recognize and think critically about biomedical problems.
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Credit points	3
Course achievement	None
Examination	Written exam
Examination duration and scale	90 minutes
Assignment for the Following Curricula	General Engineering Science (German program, 7 semester): Specialisation Biomedical Engineering: Compulsory General Engineering Science (German program, 7 semester): Specialisation Mechanical Engineering, Focus Biomechanics: Compulsory Data Science: Specialisation II. Application: Elective Compulsory Electrical Engineering: Specialisation Medical Technology: Elective Compulsory Engineering Science: Specialisation Biomedical Engineering: Compulsory General Engineering Science (English program, 7 semester): Specialisation Biomedical Engineering: Compulsory Mechanical Engineering: Specialisation Biomechanics: Compulsory Mechatronics: Specialisation Medical Engineering: Compulsory Biomedical Engineering: Specialisation Medical Technology and Control Theory: Elective Compulsory Biomedical Engineering: Specialisation Management and Business Administration: Elective Compulsory Biomedical Engineering: Specialisation Artificial Organs and Regenerative Medicine: Elective Compulsory Biomedical Engineering: Specialisation Implants and Endoprostheses: Elective Compulsory Technomathematics: Specialisation III. Engineering Science: Elective Compulsory

Course L0384: Introduction to Anatomy

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Dr. Thorsten Frenzel
Language	DE
Cycle	SoSe
Content	General Anatomy 1^st week: The Eucaryote Cell 2^ndweek: The Tissues 3^rd week: Cell Cycle, Basics in Development 4^th week: Musculoskeletal System 5^th week: Cardiovascular System 6^th week: Respiratory System 7^th week: Genito-urinary System 8^th week: Immune system 9^th week: Digestive System I 10^th week: Digestive System II 11^th week: Endocrine System 12^th week: Nervous System 13^th week: Exam
Literature	Adolf Faller/Michael Schünke, Der Körper des Menschen, 18. Auflage, Thieme Verlag Stuttgart, 2020, 704 Seiten, ISBN 978-3-13-243820-0

Module M0610: Electrical Machines and Actuators

Courses

Title	Typ	Hrs/wk	CP
Electrical Machines and Actuators (L0293)	Lecture	3	4
Electrical Machines and Actuators (L0294)	Recitation Section (large)	2	2

Module Responsible	Prof. Thorsten Kern
Admission Requirements	None
Recommended Previous Knowledge	Basics of mathematics, in particular complexe numbers, integrals, differentials Basics of electrical engineering and mechanical engineering
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students can to draw and explain the basic principles of electric and magnetic fields. They can describe the function of the standard types of electric machines and present the corresponding equations and characteristic curves. For typically used drives they can explain the major parameters of the energy efficiency of the whole system from the power grid to the driven engine.
Skills	Students are able to calculate two-dimensional electric and magnetic fields in particular ferromagnetic circuits with air gap. For this they apply the usual methods of the design auf electric machines. They can calulate the operational performance of electric machines from their given characteristic data and selected quantities and characteristic curves. They apply the usual equivalent circuits and graphical methods.
Personal Competence
Social Competence	none
Autonomy	Students are able independently to calculate electric and magnatic fields for applications. They are able to analyse independently the operational performance of electric machines from the charactersitic data and theycan calculate thereof selected quantities and characteristic curves.
Workload in Hours	Independent Study Time 110, Study Time in Lecture 70
Credit points	6
Course achievement	None
Examination	Subject theoretical and practical work
Examination duration and scale	Design of four machines and actuators, review of design files
Assignment for the Following Curricula	General Engineering Science (German program, 7 semester): Specialisation Mechanical Engineering, Focus Energy Systems: Compulsory General Engineering Science (German program, 7 semester): Specialisation Mechanical Engineering, Focus Mechatronics: Compulsory General Engineering Science (German program, 7 semester): Specialisation Mechanical Engineering, Focus Theoretical Mechanical Engineering: Elective Compulsory General Engineering Science (German program, 7 semester): Specialisation Electrical Engineering: Elective Compulsory Digital Mechanical Engineering: Core Qualification: Compulsory Electrical Engineering: Core Qualification: Elective Compulsory Engineering Science: Specialisation Electrical Engineering: Elective Compulsory Engineering Science: Specialisation Electrical Engineering: Elective Compulsory Green Technologies: Energy, Water, Climate: Specialisation Energy Technology: Elective Compulsory Green Technologies: Energy, Water, Climate: Specialisation Maritime Technologies: Elective Compulsory Computer Science in Engineering: Specialisation II. Mathematics & Engineering Science: Elective Compulsory Logistics and Mobility: Specialisation Traffic Planning and Systems: Elective Compulsory Logistics and Mobility: Specialisation Production Management and Processes: Elective Compulsory Mechanical Engineering: Core Qualification: Elective Compulsory Mechatronics: Specialisation Naval Engineering: Compulsory Mechatronics: Core Qualification: Compulsory Mechatronics: Specialisation Robot- and Machine-Systems: Compulsory Mechatronics: Specialisation Electrical Systems: Elective Compulsory Technomathematics: Specialisation III. Engineering Science: Elective Compulsory Engineering and Management - Major in Logistics and Mobility: Specialisation Traffic Planning and Systems: Elective Compulsory Engineering and Management - Major in Logistics and Mobility: Specialisation Information Technology: Elective Compulsory Engineering and Management - Major in Logistics and Mobility: Specialisation Production Management and Processes: Elective Compulsory Engineering and Management - Major in Logistics and Mobility: Specialisation Production Management and Processes: Elective Compulsory

Course L0293: Electrical Machines and Actuators

Typ	Lecture
Hrs/wk	3
CP	4
Workload in Hours	Independent Study Time 78, Study Time in Lecture 42
Lecturer	Prof. Thorsten Kern, Dennis Kähler
Language	DE
Cycle	SoSe
Content	Electric field: Coulomb´s law, flux (field) line, work, potential, capacitor, energy, force, capacitive actuators Magnetic field: force, flux line, Ampere´s law, field at bounderies, flux, magnetic circuit, hysteresis, induction, self-induction, mutual inductance, transformer, electromagnetic actuators Synchronous machines, construction and layout, equivalent single line diagrams, no-load and short-cuircuit characteristics, vector diagrams, motor and generator operation, stepper motors DC-Machines: Construction and layout, torque generation mechanismen, torque vs speed characteristics, commutation, Asynchronous Machines. Magnetic field, construction and layout, equivalent single line diagram, complex stator current diagram (Heylands´diagram), torque vs. speed characteristics, rotor layout (squirrel-cage vs. sliprings), Drives with variable speed, inverter fed operation, special drives
Literature	Hermann Linse, Roland Fischer: "Elektrotechnik für Maschinenbauer", Vieweg-Verlag; Signatur der Bibliothek der TUHH: ETB 313 Ralf Kories, Heinz Schmitt-Walter: "Taschenbuch der Elektrotechnik"; Verlag Harri Deutsch; Signatur der Bibliothek der TUHH: ETB 122 "Grundlagen der Elektrotechnik" - anderer Autoren Fachbücher "Elektrische Maschinen"

Course L0294: Electrical Machines and Actuators

Typ	Recitation Section (large)
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Thorsten Kern, Dennis Kähler
Language	DE
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Module M1278: MED I: Introduction to Radiology and Radiation Therapy

Courses

Title	Typ	Hrs/wk	CP
Introduction to Radiology and Radiation Therapy (L0383)	Lecture	2	3

Module Responsible	Prof. Ulrich Carl
Admission Requirements	None
Recommended Previous Knowledge	None
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Therapy The students can distinguish different types of currently used equipment with respect to its use in radiation therapy. The students can explain treatment plans used in radiation therapy in interdisciplinary contexts (e.g. surgery, internal medicine). The students can describe the patients' passage from their initial admittance through to follow-up care. Diagnostics The students can illustrate the technical base concepts of projection radiography, including angiography and mammography, as well as sectional imaging techniques (CT, MRT, US). The students can explain the diagnostic as well as therapeutic use of imaging techniques, as well as the technical basis for those techniques. The students can choose the right treatment method depending on the patient's clinical history and needs. The student can explain the influence of technical errors on the imaging techniques. The student can draw the right conclusions based on the images' diagnostic findings or the error protocol.
Skills	Therapy The students can distinguish curative and palliative situations and motivate why they came to that conclusion. The students can develop adequate therapy concepts and relate it to the radiation biological aspects. The students can use the therapeutic principle (effects vs adverse effects) The students can distinguish different kinds of radiation, can choose the best one depending on the situation (location of the tumor) and choose the energy needed in that situation (irradiation planning). The student can assess what an individual psychosocial service should look like (e.g. follow-up treatment, sports, social help groups, self-help groups, social services, psycho-oncology). Diagnostics The students can suggest solutions for repairs of imaging instrumentation after having done error analyses. The students can classify results of imaging techniques according to different groups of diseases based on their knowledge of anatomy, pathology and pathophysiology.
Personal Competence
Social Competence	The students can assess the special social situation of tumor patients and interact with them in a professional way. The students are aware of the special, often fear-dominated behavior of sick people caused by diagnostic and therapeutic measures and can meet them appropriately.
Autonomy	The students can apply their new knowledge and skills to a concrete therapy case. The students can introduce younger students to the clinical daily routine. The students are able to access anatomical knowledge by themselves, can participate competently in conversations on the topic and acquire the relevant knowledge themselves.
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Credit points	3
Course achievement	None
Examination	Written exam
Examination duration and scale	90 minutes
Assignment for the Following Curricula	General Engineering Science (German program, 7 semester): Specialisation Biomedical Engineering: Compulsory General Engineering Science (German program, 7 semester): Specialisation Mechanical Engineering, Focus Biomechanics: Compulsory Data Science: Specialisation II. Application: Elective Compulsory Electrical Engineering: Specialisation Medical Technology: Elective Compulsory Engineering Science: Specialisation Biomedical Engineering: Compulsory General Engineering Science (English program, 7 semester): Specialisation Biomedical Engineering: Compulsory Mechanical Engineering: Specialisation Biomechanics: Compulsory Mechatronics: Specialisation Medical Engineering: Compulsory Biomedical Engineering: Specialisation Medical Technology and Control Theory: Elective Compulsory Biomedical Engineering: Specialisation Management and Business Administration: Elective Compulsory Biomedical Engineering: Specialisation Artificial Organs and Regenerative Medicine: Elective Compulsory Biomedical Engineering: Specialisation Implants and Endoprostheses: Elective Compulsory Technomathematics: Specialisation III. Engineering Science: Elective Compulsory

Course L0383: Introduction to Radiology and Radiation Therapy

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Dr. Thorsten Frenzel
Language	DE
Cycle	SoSe
Content	The students will be given an understanding of the technological possibilities in the field of medical imaging, interventional radiology and radiation therapy/radiation oncology. It is assumed, that students in the beginning of the course have heard the word “X-ray” at best. It will be distinguished between the two arms of diagnostic (Prof. Dr. med. Thomas Vestring) and therapeutic (Prof. Dr. med. Ulrich Carl) use of X-rays. Both arms depend on special big units, which determine a predefined sequence in their respective departments
Literature	"Technik der medizinischen Radiologie" von T. + J. Laubenberg - 7. Auflage - Deutscher Ärzteverlag - erschienen 1999 "Klinische Strahlenbiologie" von Th. Herrmann, M. Baumann und W. Dörr - 4. Auflage - Verlag Urban & Fischer - erschienen 02.03.2006 ISBN: 978-3-437-23960-1 "Strahlentherapie und Onkologie für MTA-R" von R. Sauer - 5. Auflage 2003 - Verlag Urban & Schwarzenberg - erschienen 08.12.2009 ISBN: 978-3-437-47501-6 "Taschenatlas der Physiologie" von S. Silbernagel und A. Despopoulus‑ 8. Auflage - Georg Thieme Verlag - erschienen 19.09.2012 ISBN: 978-3-13-567708-8 "Der Körper des Menschen " von A. Faller u. M. Schünke - 16. Auflage 2004 - Georg Thieme Verlag - erschienen 18.07.2012 ISBN: 978-3-13-329716-5 „Praxismanual Strahlentherapie“ von Stöver / Feyer - 1. Auflage - Springer-Verlag GmbH - erschienen 02.06.2000

Module M0671: Technical Thermodynamics I

Courses

Title	Typ	Hrs/wk	CP
Technical Thermodynamics I (L0437)	Lecture	2	4
Technical Thermodynamics I (L0439)	Recitation Section (large)	1	1
Technical Thermodynamics I (L0441)	Recitation Section (small)	1	1

Module Responsible	Prof. Arne Speerforck
Admission Requirements	None
Recommended Previous Knowledge	Elementary knowledge in Mathematics and Mechanics
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students are familiar with the laws of Thermodynamics. They know the relation of the kinds of energy according to 1^st law of Thermodynamics and are aware about the limits of energy conversions according to 2^nd law of Thermodynamics. They are able to distinguish between state variables and process variables and know the meaning of different state variables like temperature, enthalpy, entropy and also the meaning of exergy and anergy. They are able to draw the Carnot cycle in a Thermodynamics related diagram. They know the physical difference between an ideal and a real gas and are able to use the related equations of state. They know the meaning of a fundamental state of equation and know the basics of two phase Thermodynamics.
Skills	Students are able to calculate the internal energy, the enthalpy, the kinetic and the potential energy as well as work and heat for simple change of states and to use this calculations for the Carnot cycle. They are able to calculate state variables for an ideal and for a real gas from measured thermal state variables.
Personal Competence
Social Competence	The students can discuss in small groups and work out a solution. You can answer comprehension questions about the content that are provided in the lecture with the ClickerOnline tool "TurningPoint" after discussions with other students.
Autonomy	Students can understand the problems posed in tasks physically. They are able to select the methods taught in the lecture and exercise to solve problems and apply them independently to different types of tasks.
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Credit points	6
Course achievement	None
Examination	Written exam
Examination duration and scale	90 min
Assignment for the Following Curricula	General Engineering Science (German program, 7 semester): Core Qualification: Compulsory Bioprocess Engineering: Core Qualification: Compulsory Chemical and Bioprocess Engineering: Core Qualification: Compulsory Digital Mechanical Engineering: Core Qualification: Compulsory Engineering Science: Specialisation Mechanical Engineering: Compulsory Engineering Science: Specialisation Mechatronics: Elective Compulsory Engineering Science: Specialisation Biomedical Engineering: Compulsory Engineering Science: Specialisation Advanced Materials: Elective Compulsory Green Technologies: Energy, Water, Climate: Core Qualification: Compulsory Integrated Building Technology: Core Qualification: Compulsory Logistics and Mobility: Specialisation Traffic Planning and Systems: Elective Compulsory Mechanical Engineering: Core Qualification: Compulsory Mechatronics: Core Qualification: Compulsory Mechatronics: Core Qualification: Elective Compulsory Orientation Studies: Core Qualification: Elective Compulsory Naval Architecture: Core Qualification: Compulsory Technomathematics: Specialisation III. Engineering Science: Elective Compulsory Process Engineering: Core Qualification: Compulsory Engineering and Management - Major in Logistics and Mobility: Specialisation Traffic Planning and Systems: Elective Compulsory

Course L0437: Technical Thermodynamics I

Typ	Lecture
Hrs/wk	2
CP	4
Workload in Hours	Independent Study Time 92, Study Time in Lecture 28
Lecturer	Prof. Arne Speerforck
Language	DE
Cycle	SoSe
Content	Introduction Fundamental terms Thermal Equilibrium and temperature 3.1 Thermal equation of state First law 4.1 Heat and work 4.2 First law for closed systems 4.3 First law for open systems 4.4 Examples Equations of state and changes of state 5.1 Changes of state 5.2 Cycle processes Second law 6.1 Carnot process 6.2 Entropy 6.3 Examples 6.4 Exergy Thermodynamic properties of pure fluids 7.1 Fundamental equations of Thermodynamics 7.2 Thermodynamic potentials 7.3 Calorific state variables for arbritary fluids 7.4 state equations (van der Waals u.a.)
Literature	Schmitz, G.: Technische Thermodynamik, TuTech Verlag, Hamburg, 2009 Baehr, H.D.; Kabelac, S.: Thermodynamik, 15. Auflage, Springer Verlag, Berlin 2012 Potter, M.; Somerton, C.: Thermodynamics for Engineers, Mc GrawHill, 1993

Course L0439: Technical Thermodynamics I

Typ	Recitation Section (large)
Hrs/wk	1
CP	1
Workload in Hours	Independent Study Time 16, Study Time in Lecture 14
Lecturer	Prof. Arne Speerforck
Language	DE
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Course L0441: Technical Thermodynamics I

Typ	Recitation Section (small)
Hrs/wk	1
CP	1
Workload in Hours	Independent Study Time 16, Study Time in Lecture 14
Lecturer	Prof. Arne Speerforck
Language	DE
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Module M0567: Theoretical Electrical Engineering I: Time-Independent Fields

Courses

Title	Typ	Hrs/wk	CP
Theoretical Electrical Engineering I: Time-Independent Fields (L0180)	Lecture	3	5
Theoretical Electrical Engineering I: Time-Independent Fields (L0181)	Recitation Section (small)	2	1

Module Responsible	Prof. Christian Schuster
Admission Requirements	None
Recommended Previous Knowledge	Basic principles of electrical engineering and advanced mathematics
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students can explain the fundamental formulas, relations, and methods of the theory of time-independent electromagnetic fields. They can explicate the principal behavior of electrostatic, magnetostatic, and current density fields with regard to respective sources. They can describe the properties of complex electromagnetic fields by means of superposition of solutions for simple fields. The students are aware of applications for the theory of time-independent electromagnetic fields and are able to explicate these.
Skills	Students can apply Maxwell’s Equations in integral notation in order to solve highly symmetrical, time-independent, electromagnetic field problems. Furthermore, they are capable of applying a variety of methods that require solving Maxwell’s Equations for more general problems. The students can assess the principal effects of given time-independent sources of fields and analyze these quantitatively. They can deduce meaningful quantities for the characterization of electrostatic, magnetostatic, and electrical flow fields (capacitances, inductances, resistances, etc.) from given fields and dimension them for practical applications.
Personal Competence
Social Competence	Students are able to work together on subject related tasks in small groups. They are able to present their results effectively (e.g. during exercise sessions).
Autonomy	Students are capable to gather necessary information from provided references and relate this information to the lecture. They are able to continually reflect their knowledge by means of activities that accompany the lecture, such as short oral quizzes during the lectures and exercises that are related to the exam. Based on respective feedback, students are expected to adjust their individual learning process. They are able to draw connections between their knowledge obtained in this lecture and the content of other lectures (e.g. Electrical Engineering I, Linear Algebra, and Analysis).
Workload in Hours	Independent Study Time 110, Study Time in Lecture 70
Credit points	6
Course achievement	None
Examination	Written exam
Examination duration and scale	90-150 minutes
Assignment for the Following Curricula	General Engineering Science (German program, 7 semester): Specialisation Electrical Engineering: Compulsory Electrical Engineering: Core Qualification: Compulsory Computer Science in Engineering: Specialisation II. Mathematics & Engineering Science: Elective Compulsory Mechatronics: Specialisation Electrical Systems: Compulsory Technomathematics: Specialisation III. Engineering Science: Elective Compulsory

Course L0180: Theoretical Electrical Engineering I: Time-Independent Fields

Typ	Lecture
Hrs/wk	3
CP	5
Workload in Hours	Independent Study Time 108, Study Time in Lecture 42
Lecturer	Prof. Christian Schuster
Language	DE
Cycle	SoSe
Content	- Maxwell’s Equations in integral and differential notation - Boundary conditions - Laws of conservation for energy and charge - Classification of electromagnetic field properties - Integral characteristics of time-independent fields (R, L, C) - Generic approaches to solving Poisson’s Equation - Electrostatic fields and specific methods of solving - Magnetostatic fields and specific methods of solving - Fields of electrical current density and specific methods of solving - Action of force within time-independent fields - Numerical methods for solving time-independent problems The practical application of numerical methods will be trained within specifically prepared lectures in an interactive manner using small MATLAB programs.
Literature	- G. Lehner, "Elektromagnetische Feldtheorie: Für Ingenieure und Physiker", Springer (2010) - H. Henke, "Elektromagnetische Felder: Theorie und Anwendung", Springer (2011) - W. Nolting, "Grundkurs Theoretische Physik 3: Elektrodynamik", Springer (2011) - D. Griffiths, "Introduction to Electrodynamics", Pearson (2012) - J. Edminister, " Schaum's Outline of Electromagnetics", Mcgraw-Hill (2013) - Richard Feynman, "Feynman Lectures on Physics: Volume 2", Basic Books (2011)

Course L0181: Theoretical Electrical Engineering I: Time-Independent Fields

Typ	Recitation Section (small)
Hrs/wk	2
CP	1
Workload in Hours	Independent Study Time 2, Study Time in Lecture 28
Lecturer	Prof. Christian Schuster
Language	DE
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Module M0755: Geotechnics II

Courses

Title	Typ	Hrs/wk	CP
Foundation Engineering (L0552)	Lecture	2	2
Foundation Engineering (L0553)	Recitation Section (large)	2	2
Foundation Engineering (L1494)	Recitation Section (small)	2	2

Module Responsible

Prof. Jürgen Grabe

Admission Requirements

None

Recommended Previous Knowledge

Modules:

Mechanics I-II
Geotechnics I

Educational Objectives

After taking part successfully, students have reached the following learning results

Professional Competence

Knowledge

The students know the basic principles and methods which are required to verificate the stability of geotechnical structures.

Skills

After successful completion of the module the students are able to:

verificate the stability and usability of foundations,
know individual methods of ground improvement and apply them in their range of application,
design retaining walls.

Personal Competence

Social Competence

Autonomy

Workload in Hours

Independent Study Time 96, Study Time in Lecture 84

Credit points

Course achievement

Compulsory	Bonus	Form	Description
No	20 %	Attestation

Examination

Written exam

Examination duration and scale

90 minutes

Assignment for the Following Curricula

General Engineering Science (German program, 7 semester): Specialisation Civil Engineering: Elective Compulsory
Civil- and Environmental Engineering: Specialisation Civil Engineering: Compulsory
Civil- and Environmental Engineering: Specialisation Traffic and Mobility: Elective Compulsory
Civil- and Environmental Engineering: Specialisation Water and Environment: Elective Compulsory
Technomathematics: Specialisation III. Engineering Science: Elective Compulsory

Course L0552: Foundation Engineering

Typ	Lecture
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Jürgen Grabe
Language	DE
Cycle	SoSe
Content	Shallow foundations Pile foundations Ground improvement Retaining walls Underpinning Groundwater Conservation Cut-off Walls
Literature	Vorlesung/Übung s. www.tu-harburg.de/gbt Grabe, J. (2004): Bodenmechanik und Grundbau Kolymbas, D. (1998): Geotechnik - Bodenmechanik und Grundbau Grundbau-Taschenbuch, neueste Auflage

Course L0553: Foundation Engineering

Typ	Recitation Section (large)
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Jürgen Grabe
Language	DE
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Course L1494: Foundation Engineering

Typ	Recitation Section (small)
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Jürgen Grabe
Language	DE
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Module M0672: Signals and Systems

Courses

Title	Typ	Hrs/wk	CP
Signals and Systems (L0432)	Lecture	3	4
Signals and Systems (L0433)	Recitation Section (small)	2	2

Module Responsible	Prof. Gerhard Bauch
Admission Requirements	None
Recommended Previous Knowledge	Mathematics 1-3 The modul is an introduction to the theory of signals and systems. Good knowledge in maths as covered by the moduls Mathematik 1-3 is expected. Further experience with spectral transformations (Fourier series, Fourier transform, Laplace transform) is useful but not required.
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	The students are able to classify and describe signals and linear time-invariant (LTI) systems using methods of signal and system theory. They are able to apply the fundamental transformations of continuous-time and discrete-time signals and systems. They can describe and analyse deterministic signals and systems mathematically in both time and image domain. In particular, they understand the effects in time domain and image domain which are caused by the transition of a continuous-time signal to a discrete-time signal. The students are familiar with the contents of lecture and tutorials. They can explain and apply them to new problems.
Skills	The students are able to describe and analyse deterministic signals and linear time-invariant systems using methods of signal and system theory. They can analyse and design basic systems regarding important properties such as magnitude and phase response, stability, linearity etc.. They can assess the impact of LTI systems on the signal properties in time and frequency domain.
Personal Competence
Social Competence	The students can jointly solve specific problems.
Autonomy	The students are able to acquire relevant information from appropriate literature sources. They can control their level of knowledge during the lecture period by solving tutorial problems, software tools, clicker system.
Workload in Hours	Independent Study Time 110, Study Time in Lecture 70
Credit points	6
Course achievement	None
Examination	Written exam
Examination duration and scale	90 min
Assignment for the Following Curricula	General Engineering Science (German program, 7 semester): Core Qualification: Compulsory Computer Science: Specialisation II. Mathematics and Engineering Science: Elective Compulsory Data Science: Core Qualification: Compulsory Electrical Engineering: Core Qualification: Compulsory Computer Science in Engineering: Core Qualification: Compulsory Integrated Building Technology: Core Qualification: Compulsory Mechanical Engineering: Specialisation Mechatronics: Elective Compulsory Mechatronics: Core Qualification: Compulsory Technomathematics: Specialisation III. Engineering Science: Elective Compulsory

Course L0432: Signals and Systems

Typ	Lecture
Hrs/wk	3
CP	4
Workload in Hours	Independent Study Time 78, Study Time in Lecture 42
Lecturer	Prof. Gerhard Bauch
Language	DE/EN
Cycle	SoSe
Content	Introduction to signal and system theory Signals Classification of signals Continuous-time and discrete-time signals Analog and digital signals Deterministic and random signals Description of LTI systems by differential equations or difference equations, respectively Basic properties of signals and operations on signals Elementary signals Distributions (Generalized Functions) Power and energy of signals Correlation functions of deterministic signals Autocorrelation function Crosscorrelation function Orthogonal signals Applications of correlation Linear time-invariant (LTI) systems Linearity Time-invariance Description of LTI systems by impulse response and frequency response Convolution Convolution and correlation Properties of LTI-systems Causal systems Stable systems Memoryless systems Fourier Series and Fourier Transform Fourier transform of continuous-time signals, discrete-time signals, periodic signals, non-periodic signals Properties of the Fourier transform Fourier transform of some basic signals Parseval’s theorem Analysis of LTI-systems and signals in the frequency domain Frequency response, magnitude response and phase response Transmission factor, attenuation, gain Frequency-flat and frequency-selective LTI-systems Bandwidth definitions Basic types of systems (filters), lowpass, highpass, bandpass, bandstop systems Phase delay and group delay Linear-phase systems Distortion-free systems Spectrum analysis with limited observation window: Leakage effect Laplace Transform Relation of Fourier transform and Laplace transform Properties of the Laplace transform Laplace transform of some basic signals Analysis of LTI-systems in the s-domain Transfer function of LTI-systems Relation of Laplace transform, magnitude response and phase response Analysis of LTI-systems using pole-zero plots Allpass filters Minimum-phase, maximum-phase and mixed phase filters Stable systems Sampling Sampling theorem Reconstruction of continuous-time signals in frequency domain and time domain Oversampling Aliasing Sampling with pulses of finite duration, sample and hold Decimation and interpolation Discrete-Time Fourier Transform (DTFT) Relation of Fourier transform and DTFT Properties of the DTFT Discrete Fourier Transform (DFT) Relation of DTFT and DFT Cyclic properties of the DFT DFT matrix Zero padding Cyclic convolution Fast Fourier Transform (FFT) Application of the DFT: Orthogonal Frequency Division Multiplex (OFDM) Z-Transform Relation of Laplace transform, DTFT, and z-transform Properties of the z-transform Z-transform of some basic discrete-time signals Discrete-time systems, digital filters FIR and IIR filters Z-transform of digital filters Analysis of discrete-time systems using pole-zero plots in the z-domain Stability Allpass filters Minimum-phase, maximum-phase and mixed-phase filters Linear phase filters
Literature	T. Frey , M. Bossert , Signal- und Systemtheorie, B.G. Teubner Verlag 2004 K. Kammeyer, K. Kroschel, Digitale Signalverarbeitung, Teubner Verlag. B. Girod ,R. Rabensteiner , A. Stenger , Einführung in die Systemtheorie, B.G. Teubner, Stuttgart, 1997 J.R. Ohm, H.D. Lüke , Signalübertragung, Springer-Verlag 8. Auflage, 2002 S. Haykin, B. van Veen: Signals and systems. Wiley. Oppenheim, A.S. Willsky: Signals and Systems. Pearson. Oppenheim, R. W. Schafer: Discrete-time signal processing. Pearson.

Course L0433: Signals and Systems

Typ	Recitation Section (small)
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Gerhard Bauch
Language	DE/EN
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Module M1803: Engineering Mechanics II (Elastostatics)

Courses

Title	Typ	Hrs/wk	CP
Engineering Mechanics II (Elastostatics) (L0493)	Lecture	2	2
Engineering Mechanics II (Elastostatics) (L1691)	Recitation Section (large)	2	2
Engineering Mechanics II (Elastostatics) (L0494)	Recitation Section (small)	2	2

Module Responsible	Prof. Christian Cyron
Admission Requirements	None
Recommended Previous Knowledge	Engineering Mechanics I, Mathematics I (basic knowledge of rigid body mechanics such as balance of linear and angular momentum, basic knowledge of linear algebra like vector-matrix calculus, basic knowledge of analysis such as differential and integral calculus)
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Having accomplished this module, the students know and understand the basic concepts of continuum mechanics and elastostatics, in particular stress, strain, constitutive laws, stretching, bending, torsion, failure analysis, energy methods and stability of structures.
Skills	Having accomplished this module, the students are able to - apply the fundamental concepts of mathematical and mechanical modeling and analysis to problems of their choice - apply the basic methods of elastostatics to problems of engineering, in particular in the design of mechanical structures - to educate themselves about more advanced aspects of elastostatics
Personal Competence
Social Competence	Ability to communicate complex problems in elastostatics, to work out solution to these problems together with others, and to communicate these solutions.
Autonomy	Self-discipline and endurance in tackling independently complex challenges in elastostatics; ability to learn also very abstract knowledge.
Workload in Hours	Independent Study Time 96, Study Time in Lecture 84
Credit points	6
Course achievement	None
Examination	Written exam
Examination duration and scale	90 min
Assignment for the Following Curricula	General Engineering Science (German program, 7 semester): Core Qualification: Compulsory Civil- and Environmental Engineering: Core Qualification: Compulsory Bioprocess Engineering: Core Qualification: Compulsory Chemical and Bioprocess Engineering: Core Qualification: Compulsory Electrical Engineering: Core Qualification: Elective Compulsory Green Technologies: Energy, Water, Climate: Core Qualification: Compulsory Integrated Building Technology: Core Qualification: Compulsory Mechanical Engineering: Core Qualification: Compulsory Mechatronics: Core Qualification: Compulsory Orientation Studies: Core Qualification: Elective Compulsory Naval Architecture: Core Qualification: Compulsory Technomathematics: Specialisation III. Engineering Science: Elective Compulsory Process Engineering: Core Qualification: Compulsory Engineering and Management - Major in Logistics and Mobility: Core Qualification: Compulsory

Course L0493: Engineering Mechanics II (Elastostatics)

Typ	Lecture
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Christian Cyron
Language	DE
Cycle	SoSe
Content	The lecture Engineering Mechanics II introduces the fundamental concepts of stress and strain and explains how these can be used to characterize and compute elastic deformations of mechanical bodies under loading. The focus of the lecture lies on: basis of continuum mechanics: stress, strain, constitutive laws truss torsion bar beam theory: bending, moment of inertia of area, transverse shear energy methods: Maxwell-Betti reciprocal work theorem, Castigliano's second theorem, theorem of Menabrea strength of materials: maximum principle stress criterion, yield criteria according to Tresca and von Mises stability of mechanical structures: Euler buckling strut
Literature	Gross, D., Hauger, W., Schröder, J., Wall, W.A.: Technische Mechanik 1, Springer Gross, D., Hauger, W., Schröder, J., Wall, W.A.: Technische Mechanik 2 Elastostatik, Springer

Course L1691: Engineering Mechanics II (Elastostatics)

Typ	Recitation Section (large)
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Christian Cyron
Language	DE
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Course L0494: Engineering Mechanics II (Elastostatics)

Typ	Recitation Section (small)
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Christian Cyron
Language	DE
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Module M0580: Principles of Building Materials and Building Physics

Courses

Title	Typ	Hrs/wk	CP
Building Physics (L0217)	Lecture	2	2
Building Physics (L0219)	Recitation Section (large)	1	1
Building Physics (L0247)	Recitation Section (small)	1	1
Principles of Building Materials (L0215)	Lecture	2	2

Module Responsible	Prof. Frank Schmidt-Döhl
Admission Requirements	None
Recommended Previous Knowledge	Knowledge of physics, chemistry and mathematics from school
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	The students are able to identify fundamental effects of action to materials and structures, to explain different types of mechanical behaviour, to describe the structure of building materials and the correlations between structure and other properties, to show methods of joining and of corrosion processes and to describe the most important regularities and properties of building materials and structures and their measurement in the field of protection against moisture, coldness, fire and noise.
Skills	The students are able to work with the most important standardized methods and regularities in the field of moisture protection, the German regulation for energy saving, fire protection and noise protection in the case of a small building.
Personal Competence
Social Competence	The students are able to support each other to learn the very extensive specialist knowledge.
Autonomy	The students are able to make the timing and the operation steps to learn the specialist knowledge of a very extensive field.
Workload in Hours	Independent Study Time 96, Study Time in Lecture 84
Credit points	6
Course achievement	None
Examination	Written exam
Examination duration and scale	2 h written exam
Assignment for the Following Curricula	General Engineering Science (German program, 7 semester): Specialisation Civil Engineering: Compulsory Civil- and Environmental Engineering: Core Qualification: Compulsory Orientation Studies: Core Qualification: Elective Compulsory Technomathematics: Specialisation III. Engineering Science: Elective Compulsory

Course L0217: Building Physics

Typ	Lecture
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Frank Schmidt-Döhl
Language	DE
Cycle	WiSe
Content	Heat transport, thermal bridges, balances of energy consumption, German regulation for energy saving, heat protection in summer, moisture transport, condensation moisture, protection against mold, fire protection, noise protection
Literature	Fischer, H.-M. ; Freymuth, H.; Häupl, P.; Homann, M.; Jenisch, R.; Richter, E.; Stohrer, M.: Lehrbuch der Bauphysik. Vieweg und Teubner Verlag, Wiesbaden, ISBN 978-3-519-55014-3

Course L0219: Building Physics

Typ	Recitation Section (large)
Hrs/wk	1
CP	1
Workload in Hours	Independent Study Time 16, Study Time in Lecture 14
Lecturer	Prof. Frank Schmidt-Döhl
Language	DE
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Course L0247: Building Physics

Typ	Recitation Section (small)
Hrs/wk	1
CP	1
Workload in Hours	Independent Study Time 16, Study Time in Lecture 14
Lecturer	Prof. Frank Schmidt-Döhl
Language	DE
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Course L0215: Principles of Building Materials

Typ	Lecture
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Frank Schmidt-Döhl
Language	DE
Cycle	WiSe
Content	Structure of building materials Effects of action Fundamentals of mechanical behaviour Material testing Principles of metals Joining methods
Literature	Wendehorst, R.: Baustoffkunde. ISBN 3-8351-0132-3 Scholz, W.:Baustoffkenntnis. ISBN 3-8041-4197-8

Module M0687: Chemistry

Courses

Title	Typ	Hrs/wk	CP
Chemistry I+II (L0460)	Lecture	4	4
Chemistry I+II (L0475)	Recitation Section (large)	2	2

Module Responsible	Dr. Dorothea Rechtenbach
Admission Requirements	None
Recommended Previous Knowledge	none
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	The students are able to name and to describe basic principles and applications of general chemistry (structure of matter, periodic table, chemical bonds), physical chemistry (aggregate states, separating processes, thermodynamics, kinetics), inorganic chemistry (acid/base, pH-value, salts, solubility, redox, metals) and organic chemistry (aliphatic hydrocarbons, functional groups, carbonyl compounds, aromates, reaction mechanisms, natural products, synthetic polymers). Furthermore students are able to explain basic chemical terms.
Skills	After successful completion of this module students are able to describe substance groups and chemical compounds. On this basis, they are capable of explaining, choosing and applying specific methods and various reaction mechanisms.
Personal Competence
Social Competence	Students are able to take part in discussions on chemical issues and problems as a member of an interdisciplinary team. They can contribute to those discussion by their own statements.
Autonomy	After successful completion of this module students are able to solve chemical problems independently by defending proposed approaches with arguments. They can also document their approaches.
Workload in Hours	Independent Study Time 96, Study Time in Lecture 84
Credit points	6
Course achievement	None
Examination	Written exam
Examination duration and scale	120 min
Assignment for the Following Curricula	General Engineering Science (German program, 7 semester): Core Qualification: Compulsory Civil- and Environmental Engineering: Core Qualification: Compulsory Technomathematics: Specialisation III. Engineering Science: Elective Compulsory

Course L0460: Chemistry I+II

Typ	Lecture
Hrs/wk	4
CP	4
Workload in Hours	Independent Study Time 64, Study Time in Lecture 56
Lecturer	Dr. Christoph Wutz
Language	DE
Cycle	WiSe
Content	Chemistry I: - Structure of matter - Periodic table - Electronegativity - Chemical bonds - Solid compounds and solutions - Chemistry of water - Chemical reactions and equilibria - Acid-base reactions - Redox reactions Chemistry II: - Simple compounds of carbon, aliphatic hydrocarbons, aromatic hydrocarbons, - Alkohols, phenols, ether, aldehydes, ketones, carbonic acids, ester, amines, amino acids, fats, sugars - Reaction mechanisms, radical reactions, nucleophilic substitution, elimination reactions, addition reaction - Practical apllications and examples
Literature	- Blumenthal, Linke, Vieth: Chemie - Grundwissen für Ingenieure - Kickelbick: Chemie für Ingenieure (Pearson) - Mortimer: Chemie. Basiswissen der Chemie. - Brown, LeMay, Bursten: Chemie. Studieren kompakt. - Schmuck: Basisbuch Organische Chemie (Pearson)

Course L0475: Chemistry I+II

Typ	Recitation Section (large)
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Dr. Dorothea Rechtenbach
Language	DE
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Module M0740: Structural Analysis I

Courses

Title	Typ	Hrs/wk	CP
Structural Analysis I (L0666)	Lecture	2	3
Structural Analysis I (L0667)	Recitation Section (large)	3	3

Module Responsible

Prof. Bastian Oesterle

Admission Requirements

None

Recommended Previous Knowledge

Mechanics I, Mathematics I

Educational Objectives

After taking part successfully, students have reached the following learning results

Professional Competence

Knowledge

After successfully completing this module, students can express the basic aspects of linear frame analysis of statically determinate and indeterminate systems.

Skills

After successful completion of this module, the students are able to distinguish between statically determinate and indeterminate structures. They are able to analyze state variables and to construct influence lines of statically determinate plane and spatial frame and truss structures.

Personal Competence

Social Competence

Students can

participate in subject-specific and interdisciplinary discussions,
defend their own work results in front of others
promote the scientific development of colleagues
Furthermore, they can give and accept professional constructive criticism

Autonomy

The students are able work in-term homework assignments. Due to the in-term feedback, they are enabled to self-assess their learning progress during the lecture period, already.

Workload in Hours

Independent Study Time 110, Study Time in Lecture 70

Credit points

Course achievement

Compulsory	Bonus	Form	Description
No	10 %	Written elaboration	Hausübungen mit Testat, betreut durch Studentische Tutoren (Tutorium)

Examination

Written exam

Examination duration and scale

90 minutes

Assignment for the Following Curricula

Course L0666: Structural Analysis I

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Bastian Oesterle
Language	DE
Cycle	WiSe
Content	modeling of structures theory of plane and spacial structures assessment of structural behaviour, degree of static indeterminacy and kinematics analysis of forces and moments, as well as diplscements and rotations principle of virtual work influence lines Force Method for statically indeterminate structures
Literature	Vorlesungsmanuskript Bletzinger et al.: Aufgabensammlung zur Baustatik: Übungsaufgaben zur Berechnung ebener Stabtragwerke. Hanser. Dinkler: Grundlagen der Baustatik. Springer. Marti: Baustatik. Ernst und Sohn.

Course L0667: Structural Analysis I

Typ	Recitation Section (large)
Hrs/wk	3
CP	3
Workload in Hours	Independent Study Time 48, Study Time in Lecture 42
Lecturer	Prof. Bastian Oesterle
Language	DE
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Module M0933: Fundamentals of Materials Science

Courses

Title	Typ	Hrs/wk	CP
Fundamentals of Materials Science I (L1085)	Lecture	2	2
Fundamentals of Materials Science II (Advanced Ceramic Materials, Polymers and Composites) (L0506)	Lecture	2	2
Physical and Chemical Basics of Materials Science (L1095)	Lecture	2	2

Module Responsible	Prof. Jörg Weißmüller
Admission Requirements	None
Recommended Previous Knowledge	Highschool-level physics, chemistry und mathematics
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	The students have acquired a fundamental knowledge on metals, ceramics and polymers and can describe this knowledge comprehensively. Fundamental knowledge here means specifically the issues of atomic structure, microstructure, phase diagrams, phase transformations, corrosion and mechanical properties. The students know about the key aspects of characterization methods for materials and can identify relevant approaches for characterizing specific properties. They are able to trace materials phenomena back to the underlying physical and chemical laws of nature.
Skills	The students are able to trace materials phenomena back to the underlying physical and chemical laws of nature. Materials phenomena here refers to mechanical properties such as strength, ductility, and stiffness, chemical properties such as corrosion resistance, and to phase transformations such as solidification, precipitation, or melting. The students can explain the relation between processing conditions and the materials microstructure, and they can account for the impact of microstructure on the material’s behavior.
Personal Competence
Social Competence	-
Autonomy	-
Workload in Hours	Independent Study Time 96, Study Time in Lecture 84
Credit points	6
Course achievement	None
Examination	Written exam
Examination duration and scale	180 min
Assignment for the Following Curricula	General Engineering Science (German program, 7 semester): Specialisation Mechanical Engineering: Compulsory General Engineering Science (German program, 7 semester): Specialisation Biomedical Engineering: Compulsory General Engineering Science (German program, 7 semester): Specialisation Naval Architecture: Compulsory General Engineering Science (German program, 7 semester): Specialisation Advanced Materials: Compulsory Data Science: Specialisation II. Application: Elective Compulsory Green Technologies: Energy, Water, Climate: Specialisation Energy Technology: Elective Compulsory Green Technologies: Energy, Water, Climate: Specialisation Maritime Technologies: Elective Compulsory Logistics and Mobility: Specialisation Production Management and Processes: Elective Compulsory Mechanical Engineering: Core Qualification: Compulsory Mechatronics: Core Qualification: Compulsory Naval Architecture: Core Qualification: Compulsory Technomathematics: Specialisation III. Engineering Science: Elective Compulsory Engineering and Management - Major in Logistics and Mobility: Specialisation II. Production Management and Processes: Elective Compulsory

Course L1085: Fundamentals of Materials Science I

Typ	Lecture
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Jörg Weißmüller
Language	DE
Cycle	WiSe
Content
Literature	Vorlesungsskript W.D. Callister: Materials Science and Engineering - An Introduction. 5th ed., John Wiley & Sons, Inc., New York, 2000, ISBN 0-471-32013-7 P. Haasen: Physikalische Metallkunde. Springer 1994

Course L0506: Fundamentals of Materials Science II (Advanced Ceramic Materials, Polymers and Composites)

Typ	Lecture
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Bodo Fiedler, Prof. Gerold Schneider
Language	DE
Cycle	WiSe
Content	Chemische Bindungen und Aufbau von Festkörpern; Kristallaufbau; Werkstoffprüfung; Schweißbarkeit; Herstellung von Keramiken; Aufbau und Eigenschaften der Keramik; Herstellung, Aufbau und Eigenschaften von Gläsern; Polymerwerkstoffe, Makromolekularer Aufbau; Struktur und Eigenschaften der Polymere; Polymerverarbeitung; Verbundwerkstoffe
Literature	Vorlesungsskript W.D. Callister: Materials Science and Engineering -An Introduction-5th ed., John Wiley & Sons, Inc., New York, 2000, ISBN 0-471-32013-7

Course L1095: Physical and Chemical Basics of Materials Science

Typ	Lecture
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Gregor Vonbun-Feldbauer
Language	DE
Cycle	WiSe
Content	Motivation: „Atoms in Mechanical Engineering?“ Basics: Force and Energy The electromagnetic Interaction „Detour“: Mathematics (complex e-funktion etc.) The atom: Bohr's model of the atom Chemical bounds The multi part problem: Solutions and strategies Descriptions of using statistical thermodynamics Elastic theory of atoms Consequences of atomar properties on makroskopic Properties: Discussion of examples (metals, semiconductors, hybrid systems)
Literature	Für den Elektromagnetismus: Bergmann-Schäfer: „Lehrbuch der Experimentalphysik“, Band 2: „Elektromagnetismus“, de Gruyter Für die Atomphysik: Haken, Wolf: „Atom- und Quantenphysik“, Springer Für die Materialphysik und Elastizität: Hornbogen, Warlimont: „Metallkunde“, Springer

Module M0945: Bioprocess Engineering - Advanced

Courses

Title	Typ	Hrs/wk	CP
Bioprocess Engineering - Advanced (L1107)	Lecture	2	4
Bioprocess Engineering - Advanced (L1108)	Recitation Section (small)	2	2

Module Responsible	Prof. Anna-Lena Heins
Admission Requirements	None
Recommended Previous Knowledge	Content of module "Biochemisty and Microbiology" Content of module "Biochemical Engineering I"
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	After successful completion of this module, students should be able - explain the microbial, energetic and engineering principles of fermentation process, - explain different kinetic approaches for cell growth, substrate uptake and product formation and apply them for process development, - understand and quantify transport phenomena in bioreactor and consider them for bioprocess scale-up - identify specific scientific problems and solutions for different types of fermentation processes
Skills	After successful completion of this module, students should be able to - to identify scientific questions or possible practical problems for concrete industrial applications (eg cultivation of microorganisms and animal cells) and to formulate solutions , - to assess the application of scale-up criteria for different types of bioreactors and processes and to apply these criteria to given problems (anaerobic , aerobic or microaerobic bioprocesses), - to formulate questions for the analysis and optimization of real biotechnological production processes appropriate solutions, - to describe the effects of the energy generation, the regeneration of reduction equivalents , and the growth inhibition of the behavior of microorganisms and to the total fermentation process qualitatively, - to establish material balance and fermentation equations and solve them to determine the kinetic parameters of different approaches, - to select process control strategies (batch , fed-batch ,or continuous culture) appropriately and to calculate basic types and evaluate them.
Personal Competence
Social Competence	After completion of this module participants should be able to debate technical questions in small teams to enhance the ability to take position to their own opinions and increase their capacity for teamwork.
Autonomy	After completion of this module participants are able to acquire new sources of knowledge and apply their knowledge to previously unknown issues and to present these.
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Credit points	6
Course achievement	None
Examination	Written exam
Examination duration and scale	90 min
Assignment for the Following Curricula	Bioprocess Engineering: Core Qualification: Compulsory Technomathematics: Specialisation III. Engineering Science: Elective Compulsory

Course L1107: Bioprocess Engineering - Advanced

Typ	Lecture
Hrs/wk	2
CP	4
Workload in Hours	Independent Study Time 92, Study Time in Lecture 28
Lecturer	Prof. Anna-Lena Heins, Prof. Andreas Liese, Prof. Ralf Pörtner
Language	DE
Cycle	WiSe
Content	Introduction: state-of-the-art and development trends of microbial and biocatalytic bioprocesses, introduction to the lecture Microbial principles of fermentation, Energetic fundamentals of bioreaction Medium design and optimization, sterilization Kinetics of cell growth Kinetics of substrate consumption and product formation Material balances and metabolic flux analysis Transport phenomena in bioreactor and bioprocess scale-u Anaerobic fermentation process, integrated downstream processin Microaerobic bioprocess: optimal O2 supply, process control and scale-u Aerobic process and high cell density culture Problem-based learning with selected bioprocesses
Literature	P. F. Stanbury, A. Whitaker, S. J. Hall, Principles of Fermentation Technology, 3^rd. Edition, Butterworth-Heinemann, 2016. H. Chmiel: Bioprozeßtechnik, Elsevier, 2006 R.H. Balz et al.: Manual of Industrial Microbiology and Biotechnology, 3. edition, ASM Press, 2010 H.W. Blanch, D. Clark: Biochemical Engineering, Taylor & Francis, 1997 P. M. Doran: Bioprocess Engineering Principles, 2. edition, Academic Press, 2013 Skripte für die Vorlesung

Course L1108: Bioprocess Engineering - Advanced

Typ	Recitation Section (small)
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Anna-Lena Heins, Prof. Andreas Liese, Prof. Ralf Pörtner
Language	DE
Cycle	WiSe
Content	Introduction: state-of-the-art and development trends of microbial and biocatalytic bioprocesses, introduction to the lecture Microbial principles of fermentation, Energetic fundamentals of bioreaction Medium design and optimization, sterilization Kinetics of cell growth Kinetics of substrate consumption and product formation Material balances and metabolic flux analysis Transport phenomena in bioreactor and bioprocess scale-u Anaerobic fermentation process, integrated downstream processin Microaerobic bioprocess: optimal O2 supply, process control and scale-u Aerobic process and high cell density culture Problem-based learning with selected bioprocesses The students present exercises and discuss them with their fellow students and faculty statt. In the PBL part of the class the students discuss scientific questions in teams. They acquire knowledge and apply it to unknown questions, present their results and argue their opinions.
Literature	P. F. Stanbury, A. Whitaker, S. J. Hall, Principles of Fermentation Technology, 3^rd. Edition, Butterworth-Heinemann, 2016. H. Chmiel: Bioprozeßtechnik, Elsevier, 2006 R.H. Balz et al.: Manual of Industrial Microbiology and Biotechnology, 3. edition, ASM Press, 2010 P. M. Doran: Bioprocess Engineering Principles, 2. edition, Academic Press, 2013 Skripte für die Vorlesung

Module M0808: Finite Elements Methods

Courses

Title	Typ	Hrs/wk	CP
Finite Element Methods (L0291)	Lecture	2	3
Finite Element Methods (L0804)	Recitation Section (large)	2	3

Module Responsible	Prof. Benedikt Kriegesmann
Admission Requirements	None
Recommended Previous Knowledge	Mechanics I (Statics, Mechanics of Materials) and Mechanics II (Hydrostatics, Kinematics, Dynamics) Mathematics I, II, III (in particular differential equations)
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	The students possess an in-depth knowledge regarding the derivation of the finite element method and are able to give an overview of the theoretical and methodical basis of the method.
Skills	The students are capable to handle engineering problems by formulating suitable finite elements, assembling the corresponding system matrices, and solving the resulting system of equations.
Personal Competence
Social Competence	Students can work in small groups on specific problems to arrive at joint solutions.
Autonomy	The students are able to independently solve challenging computational problems and develop own finite element routines. Problems can be identified and the results are critically scrutinized.
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Credit points	6
Course achievement	None
Examination	Written exam
Examination duration and scale	90 min
Assignment for the Following Curricula	Energy Systems: Core Qualification: Elective Compulsory Aircraft Systems Engineering: Core Qualification: Elective Compulsory International Management and Engineering: Specialisation II. Mechatronics: Elective Compulsory International Management and Engineering: Specialisation II. Product Development and Production: Elective Compulsory Aeronautics: Core Qualification: Elective Compulsory Mechatronics: Core Qualification: Compulsory Biomedical Engineering: Specialisation Implants and Endoprostheses: Compulsory Biomedical Engineering: Specialisation Management and Business Administration: Elective Compulsory Biomedical Engineering: Specialisation Medical Technology and Control Theory: Elective Compulsory Biomedical Engineering: Specialisation Artificial Organs and Regenerative Medicine: Elective Compulsory Product Development, Materials and Production: Core Qualification: Compulsory Technomathematics: Specialisation III. Engineering Science: Elective Compulsory Theoretical Mechanical Engineering: Core Qualification: Compulsory

Course L0291: Finite Element Methods

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Benedikt Kriegesmann
Language	EN
Cycle	WiSe
Content	- General overview on modern engineering - Displacement method - Hybrid formulation - Isoparametric elements - Numerical integration - Solving systems of equations (statics, dynamics) - Eigenvalue problems - Non-linear systems - Applications - Programming of elements (Matlab, hands-on sessions) - Applications
Literature	Bathe, K.-J. (2000): Finite-Elemente-Methoden. Springer Verlag, Berlin

Course L0804: Finite Element Methods

Typ	Recitation Section (large)
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Benedikt Kriegesmann
Language	EN
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Module M1279: MED II: Introduction to Biochemistry and Molecular Biology

Courses

Title	Typ	Hrs/wk	CP
Introduction to Biochemistry and Molecular Biology (L0386)	Lecture	2	3

Module Responsible	Prof. Hans-Jürgen Kreienkamp
Admission Requirements	None
Recommended Previous Knowledge	None
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	The students can describe basic biomolecules; explain how genetic information is coded in the DNA; explain the connection between DNA and proteins;
Skills	The students can recognize the importance of molecular parameters for the course of a disease; describe selected molecular-diagnostic procedures; explain the relevance of these procedures for some diseases
Personal Competence
Social Competence	The students can participate in discussions in research and medicine on a technical level. Students will have an improved understanding of current medical problems (e.g. Corona pandemic)and will be able to explain these issues to others.
Autonomy	The students can develop an understanding of topics from the course, using technical literature, by themselves. Students will be better equipped to recognize fake news in the media regarding medical research topics.
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Credit points	3
Course achievement	None
Examination	Written exam
Examination duration and scale	60 minutes
Assignment for the Following Curricula	General Engineering Science (German program, 7 semester): Specialisation Biomedical Engineering: Compulsory General Engineering Science (German program, 7 semester): Specialisation Mechanical Engineering, Focus Biomechanics: Compulsory Electrical Engineering: Specialisation Medical Technology: Elective Compulsory Engineering Science: Specialisation Biomedical Engineering: Compulsory General Engineering Science (English program, 7 semester): Specialisation Biomedical Engineering: Compulsory Mechanical Engineering: Specialisation Biomechanics: Compulsory Mechatronics: Specialisation Medical Engineering: Compulsory Biomedical Engineering: Specialisation Management and Business Administration: Elective Compulsory Biomedical Engineering: Specialisation Artificial Organs and Regenerative Medicine: Elective Compulsory Biomedical Engineering: Specialisation Medical Technology and Control Theory: Elective Compulsory Biomedical Engineering: Specialisation Implants and Endoprostheses: Elective Compulsory Technomathematics: Specialisation III. Engineering Science: Elective Compulsory

Course L0386: Introduction to Biochemistry and Molecular Biology

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Hans-Jürgen Kreienkamp
Language	DE
Cycle	WiSe
Content
Literature	Müller-Esterl, Biochemie, Spektrum Verlag, 2010; 2. Auflage Löffler, Basiswissen Biochemie, 7. Auflage, Springer, 2008

Module M0783: Measurements: Methods and Data Processing

Courses

Title	Typ	Hrs/wk	CP
EE Experimental Lab (L0781)	Practical Course	2	2
Measurements: Methods and Data Processing (L0779)	Lecture	2	3
Measurements: Methods and Data Processing (L0780)	Recitation Section (small)	1	1

Module Responsible

Prof. Alexander Schlaefer

Admission Requirements

None

Recommended Previous Knowledge

principles of mathematics
principles of electrical engineering

Educational Objectives

After taking part successfully, students have reached the following learning results

Professional Competence

Knowledge

The students are able to explain the purpose of metrology and the acquisition and processing of measurements. They can detail aspects of probability theory and errors, and explain the processing of stochastic signals. Students know methods to digitalize and describe measured signals.

Skills

The students are able to evaluate problems of metrology and to apply methods for describing and processing of measurements.

Personal Competence

Social Competence

The students solve problems in small groups.

Autonomy

The students can reflect their knowledge and discuss and evaluate their results.

Workload in Hours

Independent Study Time 110, Study Time in Lecture 70

Credit points

Course achievement

Compulsory	Bonus	Form	Description
Yes	10 %	Excercises

Examination

Written exam

Examination duration and scale

90 min

Assignment for the Following Curricula

General Engineering Science (German program, 7 semester): Specialisation Electrical Engineering: Elective Compulsory
Electrical Engineering: Core Qualification: Compulsory
Engineering Science: Specialisation Electrical Engineering: Elective Compulsory
Computer Science in Engineering: Specialisation II. Mathematics & Engineering Science: Elective Compulsory
Technomathematics: Specialisation III. Engineering Science: Elective Compulsory

Course L0781: EE Experimental Lab

Typ	Practical Course
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Alexander Schlaefer, Dozenten des SD E, Prof. Alexander Kölpin, Prof. Bernd-Christian Renner, Prof. Christian Becker, Prof. Heiko Falk, Prof. Herbert Werner, Prof. Thorsten Kern
Language	DE
Cycle	WiSe
Content	lab experiments: digital circuits, semiconductors, micro controllers, analog circuits, AC power, electrical machines
Literature	Wird in der Lehrveranstaltung festgelegt

Course L0779: Measurements: Methods and Data Processing

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Alexander Schlaefer
Language	DE
Cycle	WiSe
Content	introduction, systems and errors in metrology, probability theory, stochastic processes, Bayes and Kalman filter, acquisition of analog signals, applied metrology, regression, interpolation, and classification based on measurements
Literature	Puente León, Kiencke: Messtechnik, Springer 2012 Lerch: Elektrische Messtechnik, Springer 2012 Weitere Literatur wird in der Veranstaltung bekanntgegeben.

Course L0780: Measurements: Methods and Data Processing

Typ	Recitation Section (small)
Hrs/wk	1
CP	1
Workload in Hours	Independent Study Time 16, Study Time in Lecture 14
Lecturer	Prof. Alexander Schlaefer
Language	DE
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Module M0688: Technical Thermodynamics II

Courses

Title	Typ	Hrs/wk	CP
Technical Thermodynamics II (L0449)	Lecture	2	4
Technical Thermodynamics II (L0450)	Recitation Section (large)	1	1
Technical Thermodynamics II (L0451)	Recitation Section (small)	1	1

Module Responsible	Prof. Arne Speerforck
Admission Requirements	None
Recommended Previous Knowledge	Elementary knowledge in Mathematics, Mechanics and Technical Thermodynamics I
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students are familiar with different cycle processes like Joule, Otto, Diesel, Stirling, Seiliger and Clausius-Rankine. They are able to derive energetic and exergetic efficiencies and know the influence different factors. They know the difference between anti clockwise and clockwise cycles (heat-power cycle, cooling cycle). They have increased knowledge of steam cycles and are able to draw the different cycles in Thermodynamics related diagrams. They know the laws of gas mixtures, especially of humid air processes and are able to perform simple combustion calculations. They are provided with basic knowledge in gas dynamics and know the definition of the speed of sound and know about a Laval nozzle.
Skills	Students are able to use thermodynamic laws for the design of technical processes. Especially they are able to formulate energy, exergy- and entropy balances and by this to optimise technical processes. They are able to perform simple safety calculations in regard to an outflowing gas from a tank. They are able to transform a verbal formulated message into an abstract formal procedure.
Personal Competence
Social Competence	The students are able to discuss in small groups and develop an approach. You can answer comprehension questions about the content that are provided in the lecture with the ClickerOnline tool "TurningPoint" after discussions with other students.
Autonomy	Students can physically understand and explain the complex problems (cycle processes, air conditioning processes, combustion processes) set in tasks. They are able to select the methods taught in the lecture and exercise to solve complex problems and apply them independently to different types of tasks.
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Credit points	6
Course achievement	None
Examination	Written exam
Examination duration and scale	90 min
Assignment for the Following Curricula	General Engineering Science (German program, 7 semester): Core Qualification: Compulsory Bioprocess Engineering: Core Qualification: Compulsory Chemical and Bioprocess Engineering: Core Qualification: Compulsory Energy Systems: Technical Complementary Course Core Studies: Elective Compulsory Engineering Science: Specialisation Mechanical Engineering: Compulsory General Engineering Science (English program, 7 semester): Specialisation Mechanical Engineering: Elective Compulsory Green Technologies: Energy, Water, Climate: Core Qualification: Compulsory Mechanical Engineering: Core Qualification: Compulsory Mechatronics: Core Qualification: Compulsory Mechatronics: Specialisation Robot- and Machine-Systems: Elective Compulsory Technomathematics: Specialisation III. Engineering Science: Elective Compulsory Process Engineering: Core Qualification: Compulsory

Course L0449: Technical Thermodynamics II

Typ	Lecture
Hrs/wk	2
CP	4
Workload in Hours	Independent Study Time 92, Study Time in Lecture 28
Lecturer	Prof. Arne Speerforck
Language	DE
Cycle	WiSe
Content	8. Cycle processes 7. Gas - vapor - mixtures 10. Open sytems with constant flow rates 11. Combustion processes 12. Special fields of Thermodynamics
Literature	Schmitz, G.: Technische Thermodynamik, TuTech Verlag, Hamburg, 2009 Baehr, H.D.; Kabelac, S.: Thermodynamik, 15. Auflage, Springer Verlag, Berlin 2012 Potter, M.; Somerton, C.: Thermodynamics for Engineers, Mc GrawHill, 1993

Course L0450: Technical Thermodynamics II

Typ	Recitation Section (large)
Hrs/wk	1
CP	1
Workload in Hours	Independent Study Time 16, Study Time in Lecture 14
Lecturer	Prof. Arne Speerforck
Language	DE
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Course L0451: Technical Thermodynamics II

Typ	Recitation Section (small)
Hrs/wk	1
CP	1
Workload in Hours	Independent Study Time 16, Study Time in Lecture 14
Lecturer	Prof. Arne Speerforck
Language	DE
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Module M0568: Theoretical Electrical Engineering II: Time-Dependent Fields

Courses

Title	Typ	Hrs/wk	CP
Theoretical Electrical Engineering II: Time-Dependent Fields (L0182)	Lecture	3	5
Theoretical Electrical Engineering II: Time-Dependent Fields (L0183)	Recitation Section (small)	2	1

Module Responsible	Prof. Christian Schuster
Admission Requirements	None
Recommended Previous Knowledge	Electrical Engineering I, Electrical Engineering II, Theoretical Electrical Engineering I Mathematics I, Mathematics II, Mathematics III, Mathematics IV
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students are able to explain fundamental formulas, relations, and methods related to the theory of time-dependent electromagnetic fields. They can assess the principal behavior and characteristics of quasistationary and fully dynamic fields with regard to respective sources. They can describe the properties of complex electromagnetic fields by means of superposition of solutions for simple fields. The students are aware of applications for the theory of time-dependent electromagnetic fields and are able to explicate these.
Skills	Students are able to apply a variety of procedures in order to solve the diffusion and the wave equation for general time-dependent field problems. They can assess the principal effects of given time-dependent sources of fields and analyze these quantitatively. They can deduce meaningful quantities for the characterization of fully dynamic fields (wave impedance, skin depth, Poynting-vector, radiation resistance, etc.) from given fields and interpret them with regard to practical applications.
Personal Competence
Social Competence	Students are able to work together on subject related tasks in small groups. They are able to present their results effectively (e.g. during exercise sessions).
Autonomy	Students are capable to gather necessary information from provided references and relate this information to the lecture. They are able to continually reflect their knowledge by means of activities that accompany the lecture, such as short oral quizzes during the lectures and exercises that are related to the exam. Based on respective feedback, students are expected to adjust their individual learning process. They are able to draw connections between acquired knowledge and ongoing research at the Hamburg University of Technology (TUHH), e.g. in the area of high frequency engineering and optics.
Workload in Hours	Independent Study Time 110, Study Time in Lecture 70
Credit points	6
Course achievement	None
Examination	Written exam
Examination duration and scale	90-150 minutes
Assignment for the Following Curricula	General Engineering Science (German program, 7 semester): Specialisation Electrical Engineering: Compulsory Electrical Engineering: Core Qualification: Compulsory Engineering Science: Specialisation Electrical Engineering: Compulsory Engineering Science: Specialisation Mechatronics: Elective Compulsory Mechatronics: Specialisation Electrical Systems: Compulsory Technomathematics: Specialisation III. Engineering Science: Elective Compulsory

Course L0182: Theoretical Electrical Engineering II: Time-Dependent Fields

Typ	Lecture
Hrs/wk	3
CP	5
Workload in Hours	Independent Study Time 108, Study Time in Lecture 42
Lecturer	Prof. Christian Schuster
Language	DE
Cycle	WiSe
Content	- Theory and principal characteristics of quasistationary electromagnetic fields - Electromagnetic induction and law of induction - Skin effect and eddy currents - Shielding of time variable magnetic fields - Theory and principal characteristics of fully dynamic electromagnetic fields - Wave equations and properties of planar waves - Polarization and superposition of planar waves - Reflection and refraction of planar waves at boundary surfaces - Waveguide theory - Rectangular waveguide, planar optical waveguide - Elektrical and magnetical dipol radiation - Simple arrays of antennas The practical application of numerical methods will be trained within specifically prepared lectures in an interactive manner using small MATLAB programs.
Literature	- G. Lehner, "Elektromagnetische Feldtheorie: Für Ingenieure und Physiker", Springer (2010) - H. Henke, "Elektromagnetische Felder: Theorie und Anwendung", Springer (2011) - W. Nolting, "Grundkurs Theoretische Physik 3: Elektrodynamik", Springer (2011) - D. Griffiths, "Introduction to Electrodynamics", Pearson (2012) - J. Edminister, "Schaum's Outline of Electromagnetics", Mcgraw-Hill (2013) - Richard Feynman, "Feynman Lectures on Physics: Volume 2", Basic Books (2011)

Course L0183: Theoretical Electrical Engineering II: Time-Dependent Fields

Typ	Recitation Section (small)
Hrs/wk	2
CP	1
Workload in Hours	Independent Study Time 2, Study Time in Lecture 28
Lecturer	Prof. Christian Schuster
Language	DE
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Module M0538: Heat and Mass Transfer

Courses

Title	Typ	Hrs/wk	CP
Heat and Mass Transfer (L0101)	Lecture	2	2
Heat and Mass Transfer (L0102)	Recitation Section (small)	2	2
Heat and Mass Transfer (L1868)	Recitation Section (large)	1	2

Module Responsible	Prof. Irina Smirnova
Admission Requirements	None
Recommended Previous Knowledge	Basic knowledge: Technical Thermodynamics
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	The students are capable of explaining qualitative and determining quantitative heat transfer in procedural apparatus (e. g. heat exchanger, chemical reactors). They are capable of distinguish and characterize different kinds of heat transfer mechanisms namely heat conduction, heat transfer and thermal radiation. The students have the ability to explain the physical basis for mass transfer in detail and to describe mass transfer qualitative and quantitative by using suitable mass transfer theories. They are able to depict the analogy between heat- and mass transfer and to describe complex linked processes in detail.
Skills	The students are able to set reasonable system boundaries for a given transport problem by using the gained knowledge and to balance the corresponding energy and mass flow, respectively. They are capable to solve specific heat transfer problems (e.g. heated chemical reactors, temperature alteration in fluids) and to calculate the corresponding heat flows. Using dimensionless quantities, the students can execute scaling up of technical processes or apparatus. They are able to distinguish between diffusion, convective mass transition and mass transfer. They can use this knowledge for the description and design of apparatus (e.g. extraction column, rectification column). In this context, the students are capable to choose and design fundamental types of heat and mass exchanger for a specific application considering their advantages and disadvantages, respectively. In addition, they can calculate both, steady-state and non-steady-state processes in procedural apparatus. The students are capable to connect their knowledge obtained in this course with knowlegde of other courses (In particular the courses thermodynamics, fluid mechanics and chemical process engineering) to solve concrete technical problems.
Personal Competence
Social Competence	The students are capable to work on subject-specific challenges in teams and to present the results orally in a reasonable manner to tutors and other students.
Autonomy	The students are able to find and evaluate necessary information from suitable sources They are able to prove their level of knowledge during the course with accompanying procedure continuously (clicker-system, exam-like assignments) and on this basis they can control their learning processes.
Workload in Hours	Independent Study Time 110, Study Time in Lecture 70
Credit points	6
Course achievement	None
Examination	Written exam
Examination duration and scale	120 minutes; theoretical questions and calculations
Assignment for the Following Curricula	General Engineering Science (German program, 7 semester): Specialisation Green Technologies: Compulsory General Engineering Science (German program, 7 semester): Specialisation Chemical and Bioengineering: Compulsory Bioprocess Engineering: Core Qualification: Compulsory Chemical and Bioprocess Engineering: Core Qualification: Compulsory Engineering Science: Specialisation Chemical and Bioprocess Engineering: Compulsory Green Technologies: Energy, Water, Climate: Core Qualification: Compulsory Technomathematics: Specialisation III. Engineering Science: Elective Compulsory Process Engineering: Core Qualification: Compulsory

Course L0101: Heat and Mass Transfer

Typ	Lecture
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Irina Smirnova
Language	DE
Cycle	WiSe
Content	Heat transfer Introduction, one-dimensional heat conduction Convective heat transfer Multidimensional heat conduction Non-steady heat conduction Thermal radiation Mass transfer one-way diffusion, equimolar countercurrent diffusion boundary layer theory, non-steady mass transfer Heat and mass transfer single particle/ fixed bed Mass transfer and chemical reactions
Literature	H.D. Baehr und K. Stephan: Wärme- und Stoffübertragung, Springer VDI-Wärmeatlas

Course L0102: Heat and Mass Transfer

Typ	Recitation Section (small)
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Irina Smirnova
Language	DE
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Course L1868: Heat and Mass Transfer

Typ	Recitation Section (large)
Hrs/wk	1
CP	2
Workload in Hours	Independent Study Time 46, Study Time in Lecture 14
Lecturer	Prof. Irina Smirnova
Language	DE
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Module M0675: Introduction to Communications and Random Processes

Courses

Title	Typ	Hrs/wk	CP
Introduction to Communications and Random Processes (L0442)	Lecture	3	4
Introduction to Communications and Random Processes (L0443)	Recitation Section (large)	1	1
Introduction to Communications and Random Processes (L2354)	Recitation Section (small)	1	1

Module Responsible	Prof. Gerhard Bauch
Admission Requirements	None
Recommended Previous Knowledge	Mathematics 1-3 Signals and Systems
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	The students know and understand the fundamental building blocks of a communications system. They can describe and analyse the individual building blocks using knowledge of signal and system theory as well as the theory of stochastic processes. The are aware of the essential resources and evaluation criteria of information transmission and are able to design and evaluate a basic communications system. The students are familiar with the contents of lecture and tutorials. They can explain and apply them to new problems.
Skills	The students are able to design and evaluate a basic communications system. In particular, they can estimate the required resources in terms of bandwidth and power. They are able to assess essential evaluation parameters of a basic communications system such as bandwidth efficiency or bit error rate and to decide for a suitable transmission method.
Personal Competence
Social Competence	The students can jointly solve specific problems.
Autonomy	The students are able to acquire relevant information from appropriate literature sources. They can control their level of knowledge during the lecture period by solving tutorial problems, software tools, clicker system.
Workload in Hours	Independent Study Time 110, Study Time in Lecture 70
Credit points	6
Course achievement	None
Examination	Written exam
Examination duration and scale	90 min
Assignment for the Following Curricula	General Engineering Science (German program, 7 semester): Specialisation Electrical Engineering: Compulsory Data Science: Specialisation I. Mathematics/Computer Science: Elective Compulsory Electrical Engineering: Core Qualification: Compulsory Engineering Science: Specialisation Information and Communication Systems: Elective Compulsory Computer Science in Engineering: Core Qualification: Compulsory Mechatronics: Specialisation Electrical Systems: Compulsory Technomathematics: Specialisation III. Engineering Science: Elective Compulsory

Course L0442: Introduction to Communications and Random Processes

Typ	Lecture
Hrs/wk	3
CP	4
Workload in Hours	Independent Study Time 78, Study Time in Lecture 42
Lecturer	Prof. Gerhard Bauch
Language	DE/EN
Cycle	WiSe
Content	Introduction to communications engineering Open Systems Interconnection (OSI) reference model Components of a digital communications system Fundamentals of signals and systems Analog and digital signals Principles of Analog-to-digital (A/D) conversion Deterministic and random signals Power and energy of signals Linear time-invariant (LTI) systems Quadrature amplitude modulation (QAM) Introduction to stochastics Probability theory Random experiments Probability model, probability space, sample space Definitions of probability Probability according to Bernoulli/Laplace Probability according to van Mises, relative frequency Bertrand’s paradox Axiomatic definition of probability according to Kolmogorov Probability of disjoint and non-disjoint events Venn diagrams Continuous and discrete random variables Probability density function (pdf), cululative distribution function (cdf) Expected value, mean, median, quadratic mean, variance, standard deviation, higher moments Examples for probability distributions (Bernoulli distribution, two-point distribution, uniform distribution, Gaussian (normal) distribution, Rayleigh distribution, etc.) Multiple random variables Conditional probability, joint probability Conditional and joint probability density function Bayes’ rule Correlation coefficient Two-dimensional Gaussian distribution Statistically independent, uncorrelated and orthogonal random variables Independent identically distributed (iid) random variables Properties of expected value and variance Covariance Probability density function (pdf) and cumulative distribution function (cdf) of the sum of statistically independent random variables Central limit theorem Probability density functions (pdfs) in data transmission Continuous-time and discrete-time random processes Examples for random processes Ensemble average and time average Ergodic random processes Quadratic mean and variance Probability density function (pdf) and cumulative distribution function (cdf) Joint probability density function (pdf) and joint cumulative distribution function (cdf) Statistically independent, uncorrelated and orthogonal random processes Stationary random processes Correlation functions: Autocorrelation function, crosscorrelation function, average autocorrelation function of non-stationary random processes, autocorrelation and crosscorrelation function of stationary processes, autocovariance function, crosscovariance function Autocorrelation matrix, crosscorrelation matrix, autocovariance matrix, crosscovariance matrix Pseudo-noise sequences, example: Code division multiple access (CDMA) Autocorrelation function, power spectral density (psd), signal power, Einstein-Wiener-Khintchine relations White (Gaussian) noise Filtering of random processes by LTI systems Transformation of the probability density function (pdf) Transformation of the mean Transformation of the power spectral density (psd) Correlation functions of input and output signal Filtering of white Gaussian noise Bandlimitation for noise power limitation Preemphasis and deemphasis Companding, mu-law, A-law Functions of random variables Transformation of probabilities and of the probability density function (pdf) Application: Non-linear amplifiers Functions of two random variables Probability density function Examples: Rayleigh distribution, magnitude of an OFDM signal, magnitude of a received radio signal Transmission channels and channel models Wireline channels: Telephone cable, coaxial cable, optical fiber Wireless channels: Fading radio channel, underwater channels Frequency-flat and frequency-selective channels Additive white Gaussian noise (AWGN) channel Signal to noise power ratio (SNR) Discrete-time channel models Discrete memoryless channels (DMC) Analog-to-digital conversion Sampling Sampling theorem Pulse modulation Pulse-amplitude modulation (PAM) Pulse-duration modulation (PDM), pulse-width modulation (PWM) Pulse-position modulation (PPM) Pulse-code modulation (PCM) Quantization Linear quantizaton, midtread and midrise characteristic Quantization error, quantization noise Signal-to-quantization noise ratio Non-linear quantization, compressor characteristics, mu-law, A-law Speech transmission with PCM Differential pulse-code modulation (DPCM) Linear prediction according to the minimum mean squared error (MMSE) criterion. DPCM with forward prediction and backward prediction SNR gain of DPCM over PCM Delta modulation Fundamentals of information theory and coding Definitions of information: Self-information, entropy Binary entropy function Source coding theorem Source coding: Huffman code Mutual information and channel capacity Channel capacity of the AWGN channel and the binary input AWGN channel Channel coding theorem Principles of channel coding: Code rate and data rate, Hamming distance, minimum Hamming distance, error detection and error correction Examples for channel codes: Block codes and convolutional codes, repetition code, single parity check code, Hamming code, Turbo codes Combinatorics Variation with and without repetition Combination with and without repetition Permutation, Permutation of multisets Word error probabilities of linear block codes Baseband transmission Pulse shaping: Non-return to zero (NRZ) rectangular pulses, Manchester pulses, raised-cosine pulses, square-root raised-cosine pulses, Gaussian pulses Transmit signal energy, average energy per symbol Power spectral density (psd) of baseband signals Definitions of signal bandwidth Bandwidth efficiency Intersymbol interference (ISI) First and second Nyquist criterion Eye patterns Receive filter design: Matched filter Matched-filter receiver and correlation receiver Square-root Nyquist pulse shaping Discrete-time AWGN channel model Maximum a posteriori probability (MAP) and maximum likelihood (ML) detection Bit error probability in AWGN channels for binary antipodal and on-off signaling Band-pass transmission via carrier modulation Amplitude modulation, frequency modulation, phase modulation Linear digital modulation methods: On-off keying (OOK), phase-shift keying (PSK), amplitude shift keying (ASK), quadrature amplitude shift keying (QAM)
Literature	K. Kammeyer: Nachrichtenübertragung, Teubner P.A. Höher: Grundlagen der digitalen Informationsübertragung, Teubner. M. Bossert: Einführung in die Nachrichtentechnik, Oldenbourg. J.G. Proakis, M. Salehi: Grundlagen der Kommunikationstechnik. Pearson Studium. J.G. Proakis, M. Salehi: Digital Communications. McGraw-Hill. S. Haykin: Communication Systems. Wiley J.G. Proakis, M. Salehi: Communication Systems Engineering. Prentice-Hall. J.G. Proakis, M. Salehi, G. Bauch, Contemporary Communication Systems. Cengage Learning.

Course L0443: Introduction to Communications and Random Processes

Typ	Recitation Section (large)
Hrs/wk	1
CP	1
Workload in Hours	Independent Study Time 16, Study Time in Lecture 14
Lecturer	Prof. Gerhard Bauch
Language	DE/EN
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Course L2354: Introduction to Communications and Random Processes

Typ	Recitation Section (small)
Hrs/wk	1
CP	1
Workload in Hours	Independent Study Time 16, Study Time in Lecture 14
Lecturer	Prof. Gerhard Bauch
Language	DE/EN
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Module M1333: BIO I: Implants and Fracture Healing

Courses

Title	Typ	Hrs/wk	CP
Implants and Fracture Healing (L0376)	Lecture	2	3

Module Responsible	Prof. Michael Morlock
Admission Requirements	None
Recommended Previous Knowledge	It is recommended to participate in "Introduction into Anatomie" before attending "Implants and Fracture Healing".
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	The students can describe the different ways how bones heal, and the requirements for their existence. The students can name different treatments for the spine and hollow bones under given fracture morphologies.
Skills	The students can determine the forces acting within the human body under quasi-static situations under specific assumptions.
Personal Competence
Social Competence	The students can, in groups, solve basic numerical modeling tasks for the calculation of internal forces.
Autonomy	The students can, in groups, solve basic numerical modeling tasks for the calculation of internal forces.
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Credit points	3
Course achievement	None
Examination	Written exam
Examination duration and scale	90 min
Assignment for the Following Curricula	General Engineering Science (German program, 7 semester): Specialisation Mechanical Engineering, Focus Biomechanics: Compulsory General Engineering Science (German program, 7 semester): Specialisation Biomedical Engineering: Compulsory Engineering Science: Specialisation Biomedical Engineering: Compulsory General Engineering Science (English program, 7 semester): Specialisation Biomedical Engineering: Compulsory Mechanical Engineering: Specialisation Biomechanics: Compulsory Biomedical Engineering: Specialisation Implants and Endoprostheses: Elective Compulsory Biomedical Engineering: Specialisation Artificial Organs and Regenerative Medicine: Elective Compulsory Biomedical Engineering: Specialisation Management and Business Administration: Elective Compulsory Biomedical Engineering: Specialisation Medical Technology and Control Theory: Elective Compulsory Orientation Studies: Core Qualification: Elective Compulsory Technomathematics: Specialisation III. Engineering Science: Elective Compulsory

Course L0376: Implants and Fracture Healing

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Michael Morlock
Language	DE
Cycle	WiSe
Content	Topics to be covered include: 1. Introduction (history, definitions, background importance) 2. Bone (anatomy, properties, biology, adaptations in femur, tibia, humerus, radius) 3. Spine (anatomy, biomechanics, function, vertebral bodies, intervertebral disc, ligaments) 3.1 The spine in its entirety 3.2 Cervical spine 3.3 Thoracic spine 3.4 Lumbar spine 3.5 Injuries and diseases 4. Pelvis (anatomy, biomechanics, fracture treatment) 5 Fracture Healing 5.1 Basics and biology of fracture repair 5.2 Clinical principals and terminology of fracture treatment 5.3 Biomechanics of fracture treatment 5.3.1 Screws 5.3.2 Plates 5.3.3 Nails 5.3.4 External fixation devices 5.3.5 Spine implants 6.0 New Implants
Literature	Cochran V.B.: Orthopädische Biomechanik Mow V.C., Hayes W.C.: Basic Orthopaedic Biomechanics White A.A., Panjabi M.M.: Clinical biomechanics of the spine Nigg, B.: Biomechanics of the musculo-skeletal system Schiebler T.H., Schmidt W.: Anatomie Platzer: dtv-Atlas der Anatomie, Band 1 Bewegungsapparat

Module M0655: Computational Fluid Dynamics I

Courses

Title	Typ	Hrs/wk	CP
Computational Fluid Dynamics I (L0235)	Lecture	2	3
Computational Fluid Dynamics I (L0419)	Recitation Section (large)	2	3

Module Responsible	Prof. Thomas Rung
Admission Requirements	None
Recommended Previous Knowledge	Students should have sound knowledge of engineering mathematics (series expansions, internal & vector calculus), and be familiar with the foundations of partial/ordinary differential equations. They should also be familiar with engineering fluid mechanics and thermodynamics.
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students will have the required combined knowledge of thermo-/fluid dynamics and numerical analysis to translate general principles of thermo-/fluid engineering into discrete algorithms on the basis of local (finite differences/volumes) and global (potential theory) ansatz functions. They are familiar with the similarities and differences between different discretisation and approximation concepts for investigating coupled systems of non-linear, convective partial differential equations (PDE), and explain the motivation for applying them. Students have the required background knowledge to develop, code, explain and apply numerical algorithms dedicated to the solution of thermofluid dynamic PDEs. They are familiar with most numerical methods used to predict thermofluid dynamic fields, in particular their realms and limitations.
Skills	The students are able choose and apply appropriate numerical procedures that integrate the governing thermofluid dynamic PDEs in space and time. They can apply/optimise numerical analysis concepts to/for fluid dynamic applications. They can code computational algorithms in a structured way, apply these codes for parameter investigations and supplement interfaces to extract simulation data for an engineering analysis.
Personal Competence
Social Competence	The students are able to discuss problems, present the results of their own analysis, and jointly develop, implement and report on solution strategies that address given technical reference problems.
Autonomy	The students can independently analyse numerical methods to solving fluid engineering problems. They are able to critically analyse own results as well as external data with regards to the plausibility and reliability.
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Credit points	6
Course achievement	None
Examination	Written exam
Examination duration and scale	2h
Assignment for the Following Curricula	General Engineering Science (German program, 7 semester): Specialisation Mechanical Engineering, Focus Aircraft Systems Engineering: Elective Compulsory General Engineering Science (German program, 7 semester): Specialisation Naval Architecture: Compulsory General Engineering Science (German program, 7 semester): Specialisation Mechanical Engineering, Focus Energy Systems: Elective Compulsory Energy Systems: Technical Complementary Course Core Studies: Elective Compulsory Green Technologies: Energy, Water, Climate: Specialisation Energy Technology: Elective Compulsory Green Technologies: Energy, Water, Climate: Specialisation Maritime Technologies: Elective Compulsory Mechanical Engineering: Specialisation Energy Systems: Elective Compulsory Naval Architecture: Core Qualification: Compulsory Technomathematics: Specialisation III. Engineering Science: Elective Compulsory

Course L0235: Computational Fluid Dynamics I

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Thomas Rung
Language	DE
Cycle	WiSe
Content	Fundamentals of computational modelling of thermofluid dynamic problems. Development of numerical algorithms. Partial differential equations Foundations of finite numerical approximations Computation of potential flows Introduction of finite-differences Approximation of convective, diffusive and transient transport processes Formulation of boundary conditions and initial conditions Assembly and solution of algebraic equation systems Facets of weighted -residual approaches Finite volume methods Basics of grid generation
Literature	Ferziger and Peric: Computational Methods for Fluid Dynamics, Springer

Course L0419: Computational Fluid Dynamics I

Typ	Recitation Section (large)
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Thomas Rung
Language	DE
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Module M1804: Engineering Mechanics III (Dynamics)

Courses

Title	Typ	Hrs/wk	CP
Engineering Mechanics III (Dynamics) (L1134)	Lecture	3	3
Engineering Mechanics III (Dynamics) (L1136)	Recitation Section (large)	1	1
Engineering Mechanics III (Dynamics) (L1135)	Recitation Section (small)	2	2

Module Responsible

Prof. Robert Seifried

Admission Requirements

None

Recommended Previous Knowledge

Mathematics I, II, Engineering Mechanics I (Statics). Parallel to Engineering Mechanik III the module Mathematics III should be attended.

Educational Objectives

After taking part successfully, students have reached the following learning results

Professional Competence

Knowledge

The students can

describe the axiomatic procedure used in mechanical contexts;
explain important steps in model design;
present technical knowledge in kinematics, kinetics and vibrations.

Skills

The students can

explain the important elements of mathematical / mechanical analysis and model formation, and apply it to the context of their own problems;
apply basic kinematic, kinetic and vibraton methods to engineering problems;
estimate the reach and boundaries of kinematic, kinetic and vibraton methods and extend them to be applicable to wider problem sets.

Personal Competence

Social Competence

The students can work in groups and support each other to overcome difficulties.

Autonomy

Students are capable of determining their own strengths and weaknesses and to organize their time and learning based on those.

Workload in Hours

Independent Study Time 96, Study Time in Lecture 84

Credit points

Course achievement

Compulsory	Bonus	Form	Description
No	20 %	Midterm	Midterm

Examination

Written exam

Examination duration and scale

120 min

Assignment for the Following Curricula

General Engineering Science (German program, 7 semester): Core Qualification: Compulsory
Green Technologies: Energy, Water, Climate: Specialisation Maritime Technologies: Elective Compulsory
Mechanical Engineering: Core Qualification: Compulsory
Mechatronics: Specialisation Naval Engineering: Compulsory
Mechatronics: Specialisation Robot- and Machine-Systems: Compulsory
Mechatronics: Specialisation Medical Engineering: Compulsory
Mechatronics: Specialisation Dynamic Systems and AI: Compulsory
Naval Architecture: Core Qualification: Compulsory
Technomathematics: Specialisation III. Engineering Science: Elective Compulsory

Course L1134: Engineering Mechanics III (Dynamics)

Typ	Lecture
Hrs/wk	3
CP	3
Workload in Hours	Independent Study Time 48, Study Time in Lecture 42
Lecturer	Prof. Robert Seifried
Language	DE
Cycle	WiSe
Content	Kinematics 1.1 Motion of a particle 1.2 Planar motion of a rigid body 1.3 Spatial motion of a rigid body 1.4 Spatial relative Kinematics 2 Kinetics 2.1 Linear momentum and change of linear momentum 2.2 Angular momentum and change of angular momentum 2.3 Kinetics of rigid bodies 2.4 Energy and balance of energy 3 Vibrations 3.1 Classification of Vibrations 3.2 Free undamped vibration 3.3 Free damped vibration 3.4 Forced vibration 4. Impact problems 5 Kinetics of gyroscopes 5.1 Free gyroscopic motion 5.2 Forced gyroscopic motion
Literature	K. Magnus, H.H. Müller-Slany: Grundlagen der Technischen Mechanik. 7. Auflage, Teubner (2009). D. Gross, W. Hauger, J. Schröder, W. Wall: Technische Mechanik 3 und 4. 11. Auflage, Springer (2011).

Course L1136: Engineering Mechanics III (Dynamics)

Typ	Recitation Section (large)
Hrs/wk	1
CP	1
Workload in Hours	Independent Study Time 16, Study Time in Lecture 14
Lecturer	Prof. Robert Seifried
Language	DE
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Course L1135: Engineering Mechanics III (Dynamics)

Typ	Recitation Section (small)
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Robert Seifried
Language	DE
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Module M0833: Introduction to Control Systems

Courses

Title	Typ	Hrs/wk	CP
Introduction to Control Systems (L0654)	Lecture	2	4
Introduction to Control Systems (L0655)	Recitation Section (small)	2	2

Module Responsible	Prof. Timm Faulwasser
Admission Requirements	None
Recommended Previous Knowledge	Representation of signals and systems in time and frequency domain, Laplace transform
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students can represent dynamic system behavior in time and frequency domain, and can in particular explain properties of first and second order systems They can explain the dynamics of simple control loops and interpret dynamic properties in terms of frequency response and root locus They can explain the Nyquist stability criterion and the stability margins derived from it. They can explain the role of the phase margin in analysis and synthesis of control loops They can explain the way a PID controller affects a control loop in terms of its frequency response They can explain issues arising when controllers designed in continuous time domain are implemented digitally
Skills	Students can transform models of linear dynamic systems from time to frequency domain and vice versa They can simulate and assess the behavior of systems and control loops They can design PID controllers with the help of heuristic (Ziegler-Nichols) tuning rules They can analyze and synthesize simple control loops with the help of root locus and frequency response techniques They can calculate discrete-time approximations of controllers designed in continuous-time and use it for digital implementation They can use standard software tools (Matlab Control Toolbox, Simulink) for carrying out these tasks
Personal Competence
Social Competence	Students can work in small groups to jointly solve technical problems, and experimentally validate their controller designs
Autonomy	Students can obtain information from provided sources (lecture notes, software documentation, experiment guides) and use it when solving given problems. They can assess their knowledge in weekly on-line tests and thereby control their learning progress.
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Credit points	6
Course achievement	None
Examination	Written exam
Examination duration and scale	120 min
Assignment for the Following Curricula	General Engineering Science (German program, 7 semester): Core Qualification: Compulsory Bioprocess Engineering: Core Qualification: Compulsory Chemical and Bioprocess Engineering: Core Qualification: Compulsory Data Science: Specialisation II. Application: Elective Compulsory Electrical Engineering: Core Qualification: Compulsory Green Technologies: Energy, Water, Climate: Core Qualification: Compulsory Computer Science in Engineering: Core Qualification: Compulsory Logistics and Mobility: Specialisation Information Technology: Elective Compulsory Logistics and Mobility: Specialisation Traffic Planning and Systems: Elective Compulsory Logistics and Mobility: Specialisation Production Management and Processes: Elective Compulsory Mechanical Engineering: Core Qualification: Compulsory Mechatronics: Core Qualification: Compulsory Technomathematics: Specialisation III. Engineering Science: Elective Compulsory Theoretical Mechanical Engineering: Technical Complementary Course Core Studies: Elective Compulsory Process Engineering: Core Qualification: Compulsory Engineering and Management - Major in Logistics and Mobility: Specialisation II. Information Technology: Elective Compulsory Engineering and Management - Major in Logistics and Mobility: Specialisation II. Traffic Planning and Systems: Elective Compulsory Engineering and Management - Major in Logistics and Mobility: Specialisation II. Production Management and Processes: Elective Compulsory

Course L0654: Introduction to Control Systems

Typ	Lecture
Hrs/wk	2
CP	4
Workload in Hours	Independent Study Time 92, Study Time in Lecture 28
Lecturer	Prof. Timm Faulwasser
Language	DE
Cycle	WiSe
Content	Signals and systems Linear systems, differential equations and transfer functions First and second order systems, poles and zeros, impulse and step response Stability Feedback systems Principle of feedback, open-loop versus closed-loop control Reference tracking and disturbance rejection Types of feedback, PID control System type and steady-state error, error constants Internal model principle Root locus techniques Root locus plots Root locus design of PID controllers Frequency response techniques Bode diagram Minimum and non-minimum phase systems Nyquist plot, Nyquist stability criterion, phase and gain margin Loop shaping, lead lag compensation Frequency response interpretation of PID control Time delay systems Root locus and frequency response of time delay systems Smith predictor Digital control Sampled-data systems, difference equations Tustin approximation, digital implementation of PID controllers Software tools Introduction to Matlab, Simulink, Control toolbox Computer-based exercises throughout the course
Literature	Werner, H., Lecture Notes „Introduction to Control Systems“ G.F. Franklin, J.D. Powell and A. Emami-Naeini "Feedback Control of Dynamic Systems", Addison Wesley, Reading, MA, 2009 K. Ogata "Modern Control Engineering", Fourth Edition, Prentice Hall, Upper Saddle River, NJ, 2010 R.C. Dorf and R.H. Bishop, "Modern Control Systems", Addison Wesley, Reading, MA 2010

Course L0655: Introduction to Control Systems

Typ	Recitation Section (small)
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Timm Faulwasser
Language	DE
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Module M0708: Electrical Engineering III: Circuit Theory and Transients

Courses

Title	Typ	Hrs/wk	CP
Circuit Theory (L0566)	Lecture	3	4
Circuit Theory (L0567)	Recitation Section (small)	2	2

Module Responsible

Prof. Alexander Kölpin

Admission Requirements

None

Recommended Previous Knowledge

Electrical Engineering I and II, Mathematics I and II

Educational Objectives

After taking part successfully, students have reached the following learning results

Professional Competence

Knowledge

Students are able to explain the basic methods for calculating electrical circuits. They know the Fourier series analysis of linear networks driven by periodic signals. They know the methods for transient analysis of linear networks in time and in frequency domain, and they are able to explain the frequency behaviour and the synthesis of passive two-terminal-circuits.

Skills

The students are able to calculate currents and voltages in linear networks by means of basic methods, also when driven by periodic signals. They are able to calculate transients in electrical circuits in time and frequency domain and are able to explain the respective transient behaviour. They are able to analyse and to synthesize the frequency behaviour of passive two-terminal-circuits.

Personal Competence

Social Competence

Students work on exercise tasks in small guided groups. They are encouraged to present and discuss their results within the group.

Autonomy

The students are able to find out the required methods for solving the given practice problems. Possibilities are given to test their knowledge during the lectures continuously by means of short-time tests. This allows them to control independently their educational objectives. They can link their gained knowledge to other courses like Electrical Engineering I and Mathematics I.

Workload in Hours

Independent Study Time 110, Study Time in Lecture 70

Credit points

Course achievement

Compulsory	Bonus	Form	Description
No	10 %	Attestation	Freiwillige semesterbegleitende Quiz-Aufgaben im Rahmen der Vorlesung zur Erlangung von maximal 10% Bonuspunkten

Examination

Written exam

Examination duration and scale

150 min

Assignment for the Following Curricula

General Engineering Science (German program, 7 semester): Specialisation Mechanical Engineering, Focus Mechatronics: Compulsory
General Engineering Science (German program, 7 semester): Specialisation Electrical Engineering: Compulsory
Electrical Engineering: Core Qualification: Compulsory
Engineering Science: Specialisation Electrical Engineering: Compulsory
Computer Science in Engineering: Specialisation II. Mathematics & Engineering Science: Elective Compulsory
Mechatronics: Specialisation Electrical Systems: Compulsory
Mechatronics: Specialisation Dynamic Systems and AI: Compulsory
Mechatronics: Specialisation Robot- and Machine-Systems: Compulsory
Technomathematics: Specialisation III. Engineering Science: Elective Compulsory

Course L0566: Circuit Theory

Typ	Lecture
Hrs/wk	3
CP	4
Workload in Hours	Independent Study Time 78, Study Time in Lecture 42
Lecturer	Prof. Alexander Kölpin
Language	DE
Cycle	WiSe
Content	- Circuit theorems - N-port circuits - Periodic excitation of linear circuits - Transient analysis in time domain - Transient analysis in frequency domain; Laplace Transform - Frequency behaviour of passive one-ports
Literature	- M. Albach, "Grundlagen der Elektrotechnik 1", Pearson Studium (2011) - M. Albach, "Grundlagen der Elektrotechnik 2", Pearson Studium (2011) - L. P. Schmidt, G. Schaller, S. Martius, "Grundlagen der Elektrotechnik 3", Pearson Studium (2011) - T. Harriehausen, D. Schwarzenau, "Moeller Grundlagen der Elektrotechnik", Springer (2013) - A. Hambley, "Electrical Engineering: Principles and Applications", Pearson (2008) - R. C. Dorf, J. A. Svoboda, "Introduction to electrical circuits", Wiley (2006) - L. Moura, I. Darwazeh, "Introduction to Linear Circuit Analysis and Modeling", Amsterdam Newnes (2005)

Course L0567: Circuit Theory

Typ	Recitation Section (small)
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Alexander Kölpin
Language	DE
Cycle	WiSe
Content	see interlocking course
Literature	siehe korrespondierende Lehrveranstaltung

Module M1764: Bioprocess Technology I

Courses

Title	Typ	Hrs/wk	CP
Bioprocess Technology I (L2906)	Lecture	2	3
Bioprocess Technology I (L2907)	Recitation Section (large)	2	1
Bioprocess Technology I - Fundamental Practical Course (L2908)	Practical Course	2	2

Module Responsible

Prof. Andreas Liese

Admission Requirements

None

Recommended Previous Knowledge

Content of module "Biological and Biochemical Fundamentals"
Content of module “Organic Chemistry”

Educational Objectives

After taking part successfully, students have reached the following learning results

Professional Competence

Knowledge

Upon completion of the module, students will be able to:

to describe basic processes of bioprocess engineering,
to assign different types of kinetics to enzymes and microorganisms and to distinguish inhibition types,
to name and describe the parameters of stoichiometry and rheology,
to explain the mass transport processes in bioreactors fundamentally,
to understand and describe the basics of bioprocess management (batch and continuously operated reactor types, calculation of the batch reaction time,...) in great detail,
to explain methods for the retention of enzymes and microorganisms by immobilization in bioreactors.

Skills

After successful completion of this module, students should be able to

using various kinetic approaches, to determine substrate turnover by enzymes as well as their kinetic parameters,
describe the growth of whole cells with the help of different kinetic approaches as well as to determine their kinetic parameters,
qualitatively predict the effects of enzyme inhibition on the behavior of enzymes and on the overall process,
analyze and determine bioprocesses based on the stoichiometry of the reaction system,
differentiate the various basic reactor types in biotechnological processes and select them specifically for the respective application,
set up and solve mass balance and differential equations for the mathematical description of fermentation processes,
apply various methods for determining mass transfer parameters for gases in solution and calculate the corresponding mass transfer coefficients

Personal Competence

Social Competence

After completing the module, students are able to discuss scientific questions among themselves and with industry representatives in mixed teams, to represent their views on them and to work together on given engineering and scientific tasks.

Autonomy

After completion of this module participants are able to acquire new sources of knowledge and apply their knowledge to previously unknown issues and to present these.

Workload in Hours

Independent Study Time 96, Study Time in Lecture 84

Credit points

Course achievement

Compulsory	Bonus	Form	Description
Yes	5 %	Subject theoretical and practical work

Examination

Written exam

Examination duration and scale

90 min

Assignment for the Following Curricula

General Engineering Science (German program, 7 semester): Specialisation Chemical and Bioengineering: Compulsory
Chemical and Bioprocess Engineering: Core Qualification: Compulsory
Engineering Science: Specialisation Chemical and Bioprocess Engineering: Compulsory
Green Technologies: Energy, Water, Climate: Specialisation Biotechnologies: Elective Compulsory
Biomedical Engineering: Specialisation Implants and Endoprostheses: Elective Compulsory
Biomedical Engineering: Specialisation Management and Business Administration: Elective Compulsory
Biomedical Engineering: Specialisation Medical Technology and Control Theory: Elective Compulsory
Biomedical Engineering: Specialisation Artificial Organs and Regenerative Medicine: Compulsory
Technomathematics: Specialisation III. Engineering Science: Elective Compulsory

Course L2906: Bioprocess Technology I

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Andreas Liese
Language	DE
Cycle	WiSe
Content	Introduction to enzyme kinetics Immobilisation of enzymes and whole cells Stoichiometry of cell growth and product formation Microbial growth kinetics and growth models Maintenance metabolism Basic bioprocess reactor types Batch, fed-batch, chemostate and turbidostate fermentation Calculation of main parameters of fermentative processes Rheology and mechanical energy input Gassing of bioprocesses (aerobic and microaerobic) Discussion with bioprocess engineers of large and small companies, proportionally alumni of TUHH Repetitorium
Literature	A. Liese, K. Seelbach, C. Wandrey: Industrial Biotransformations, Wiley-VCH,2nd ed. 2006 H.W. Blanch, D. Clark: Biochemical Engineering, Taylor & Francis, 1997 P. M. Doran: Bioprocess Engineering Principles, 2nd. edition, Academic Press, 2013 H. Chmiel, R. Takors, D. Weuster-Botz (Herausgeber): Bioprozeßtechnik, Springer Spektrum, 2018 K.-E. Jaeger, A. Liese, C. Syldatk: Einführung in die Enzymtechnologie, Springer, 2018

Course L2907: Bioprocess Technology I

Typ	Recitation Section (large)
Hrs/wk	2
CP	1
Workload in Hours	Independent Study Time 2, Study Time in Lecture 28
Lecturer	Prof. Andreas Liese
Language	DE
Cycle	WiSe
Content	See interlocking course
Literature	See interlocking course

Course L2908: Bioprocess Technology I - Fundamental Practical Course

Typ	Practical Course
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Andreas Liese
Language	DE
Cycle	WiSe
Content	In this course fermentation and downstream technologies on the example of the production of an enzyme by means of a recombinant microorganism is learned. Detailed characterization and simulation of enzyme kinetics as well as application of the enzyme in a bioreactor is carried out. The students document their experiments and results in a protocol.
Literature	· Praktikumsskript bereitgestellt über StudIP · Bioprozesstechnik-Vorlesung & -Vorlesungsskript · Jaeger, K.-E., Liese, A., Syldatk, C. (2018). Einführung in die Enzymtechnologie. Springer Spektrum. · Hilterhaus, L., Liese, A., Kettling, U., Antranikian, G. (2016). Applied Biocatalysis. Wiley-VCH. · Hass, V. C., Pörtner, R. (2011). Praxis der Bioprozesstechnik mit virtuellem Praktikum. Spektrum Akademischer Verlag. · Chmiel, H. (2018). Bioprozesstechnik. Springer Spektrum. · Liese, A., Seelbach, K., Wandrey, C. (2006). Industrial Biotransformations. Wiley-VCH. · Bommarius, S., Riebel, B. (2004). Biocatalysis: Fundamentals and Applications. Wiley-Blackwell. · Schmid, R. D. (2003). Pocket Guide to Biotechnology and Genetic Engineering. Wiley-Blackwell.

Module M1761: Biological and Biochemical Fundamentals

Courses

Title	Typ	Hrs/wk	CP
Biological and Biochemical Fundamentals (L2900)	Lecture	2	2
Fundamental Biological and Biochemical Practical Course (L2901)	Practical Course	3	3
Introduction to the Biological and Biochemical Practical Course (L2902)	Lecture	1	1

Module Responsible

Prof. Johannes Gescher

Admission Requirements

None

Recommended Previous Knowledge

The module is divided into two parts. In the winter semester, a lecture with 2 semester hours per week is offered. No previous knowledge is required for this lecture. In the following summer semester, the second part of the module is offered. This is divided into an internship and an introductory lecture. For these two parts of the module, attendance of the lecture in the winter semester is strongly recommended.

Educational Objectives

After taking part successfully, students have reached the following learning results

Professional Competence

Knowledge

The module aims to teach you the basic principles of biological systems and biocatalysts. You will learn how organisms are constructed and what basic characteristics can be used to distinguish organisms from the three kingdoms of life. You will learn about the ways in which biological systems can produce energy and you will apply the principles of biological thermodynamics. In addition, you will learn how enzymes are constructed and, using some classes of enzymes as examples, you will learn how enzymes exert their effect.

At the end of the module

- you will be able to describe basic principles of living systems and explain the metabolism of organisms by applying them.

- you will be able to assign organisms to the three kingdoms of life based on some basic characteristics

- you will be able to describe the tasks of enzymes generically on the basis of some example reactions

- you will be able to deduce from the basic characteristics of organisms and enzymes which biotechnological applications are possible with these systems.

- you can understand and use the technical vocabulary of biological systems and processes

- you will be able to perform simple bioinformatic operations to assign DNA sequences to a function

- you can confidently apply the basic principles of using primary literature

Skills

The students master the basic techniques of sterile work and molecular diagnostics. They can independently prepare media and maintain microorganisms in culture. In addition, they can isolate and characterize organisms from enrichment cultures and environmental samples.

Personal Competence

Social Competence

The students are able,

- to gather knowledge in groups of about 2 to 10 students

- to introduce their own knowledge and to argue their view in discussions in teams

- to divide a complex task into subtasks, solve these and to present the combined results

Autonomy

Students are able to independently structure their internship days and prioritize tasks. Furthermore, they are able to collect and process basic information on microorganisms via a literature search.

Workload in Hours

Independent Study Time 96, Study Time in Lecture 84

Credit points

Course achievement

Compulsory	Bonus	Form	Description
Yes	None	Presentation	Zusammenstellung der Ergebnisse des Praktikums

Examination

Written exam

Examination duration and scale

90 min

Assignment for the Following Curricula

Course L2900: Biological and Biochemical Fundamentals

Typ	Lecture
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Johannes Gescher
Language	DE
Cycle	WiSe
Content	In the lecture we will learn the basic characteristics of organisms of all kingdoms of life. This includes cell biology as well as cell physiology. We understand the energetic foundations of living systems and the variety of possible metabolic concepts of life. From these basic laws we will understand how and to what extent an application and genetic reprogramming of organisms for application can take place.
Literature	Fuchs: Allgemeine Mikrobiologie, 11. vollständig überarbeitete Auflage 2022; ISBN: 9783132434776 Brock: Biology of Microorganisms, ISBN-13: 9780134626109

Course L2901: Fundamental Biological and Biochemical Practical Course

Typ	Practical Course
Hrs/wk	3
CP	3
Workload in Hours	Independent Study Time 48, Study Time in Lecture 42
Lecturer	Prof. Johannes Gescher
Language	DE
Cycle	SoSe
Content	The aim of the practical course is to teach basic microbiological and molecular biological techniques on the basis of individual research assignments and control experiments. In doing so, organisms are to be isolated in this practical course, which will be further processed by students of the 4th and 6th semester in two independent modules.
Literature	Steinbüchel: Mikrobiologisches Praktikum, ISBN: 978-3-662-63234-5

Course L2902: Introduction to the Biological and Biochemical Practical Course

Typ	Lecture
Hrs/wk	1
CP	1
Workload in Hours	Independent Study Time 16, Study Time in Lecture 14
Lecturer	Prof. Johannes Gescher
Language	DE
Cycle	SoSe
Content	The aim of the introductory lecture is to explain different methods used and their range of application. In addition, we will clarify specific physiological characteristics of the microorganisms to be isolated.
Literature	Steinbüchel: Mikrobiologisches Praktikum, ISBN: 978-3-662-63234-5

Module M1280: MED II: Introduction to Physiology

Courses

Title	Typ	Hrs/wk	CP
Introduction to Physiology (L0385)	Lecture	2	3

Module Responsible	Prof. Michael Morlock
Admission Requirements	None
Recommended Previous Knowledge	None
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	The students can describe the basics of the energy metabolism; describe physiological relations in selected fields of muscle, heart/circulation, neuro- and sensory physiology.
Skills	The students can describe the effects of basic bodily functions (sensory, transmission and processing of information, development of forces and vital functions) and relate them to similar technical systems.
Personal Competence
Social Competence	The students can conduct discussions in research and medicine on a technical level. The students can find solutions to problems in the field of physiology, both analytical and metrological.
Autonomy	The students can derive answers to questions arising in the course and other physiological areas, using technical literature, by themselves.
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Credit points	3
Course achievement	None
Examination	Written exam
Examination duration and scale	60 minutes
Assignment for the Following Curricula	General Engineering Science (German program, 7 semester): Specialisation Biomedical Engineering: Compulsory General Engineering Science (German program, 7 semester): Specialisation Mechanical Engineering, Focus Biomechanics: Compulsory Electrical Engineering: Specialisation Medical Technology: Elective Compulsory Engineering Science: Specialisation Biomedical Engineering: Elective Compulsory General Engineering Science (English program, 7 semester): Specialisation Biomedical Engineering: Elective Compulsory Mechanical Engineering: Specialisation Biomechanics: Compulsory Mechatronics: Specialisation Medical Engineering: Compulsory Biomedical Engineering: Specialisation Medical Technology and Control Theory: Elective Compulsory Biomedical Engineering: Specialisation Management and Business Administration: Elective Compulsory Biomedical Engineering: Specialisation Artificial Organs and Regenerative Medicine: Elective Compulsory Biomedical Engineering: Specialisation Implants and Endoprostheses: Elective Compulsory Technomathematics: Specialisation III. Engineering Science: Elective Compulsory

Course L0385: Introduction to Physiology

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Dr. Gerhard Engler
Language	DE
Cycle	SoSe
Content
Literature	Taschenatlas der Physiologie, Silbernagl Despopoulos, ISBN 978-3-135-67707-1, Thieme Repetitorium Physiologie, Speckmann, ISBN 978-3-437-42321-5, Elsevier

Module M0805: Technical Acoustics I (Acoustic Waves, Noise Protection, Psycho Acoustics )

Courses

Title	Typ	Hrs/wk	CP
Technical Acoustics I (Acoustic Waves, Noise Protection, Psycho Acoustics ) (L0516)	Lecture	2	3
Technical Acoustics I (Acoustic Waves, Noise Protection, Psycho Acoustics ) (L0518)	Recitation Section (large)	2	3

Module Responsible	Prof. Benedikt Kriegesmann
Admission Requirements	None
Recommended Previous Knowledge	Mechanics I (Statics, Mechanics of Materials) and Mechanics II (Hydrostatics, Kinematics, Dynamics) Mathematics I, II, III (in particular differential equations)
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	The students possess an in-depth knowledge in acoustics regarding acoustic waves, noise protection, and psycho acoustics and are able to give an overview of the corresponding theoretical and methodical basis.
Skills	The students are capable to handle engineering problems in acoustics by theory-based application of the demanding methodologies and measurement procedures treated within the module.
Personal Competence
Social Competence	Students can work in small groups on specific problems to arrive at joint solutions.
Autonomy	The students are able to independently solve challenging acoustical problems in the areas treated within the module. Possible conflicting issues and limitations can be identified and the results are critically scrutinized.
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Credit points	6
Course achievement	None
Examination	Written exam
Examination duration and scale	90 min
Assignment for the Following Curricula	Aircraft Systems Engineering: Core Qualification: Elective Compulsory International Management and Engineering: Specialisation II. Aviation Systems: Elective Compulsory Aeronautics: Core Qualification: Elective Compulsory Mechatronics: Core Qualification: Elective Compulsory Product Development, Materials and Production: Core Qualification: Elective Compulsory Technomathematics: Specialisation III. Engineering Science: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Product Development and Production: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Simulation Technology: Elective Compulsory

Course L0516: Technical Acoustics I (Acoustic Waves, Noise Protection, Psycho Acoustics )

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Dr. Sören Keuchel
Language	EN
Cycle	SoSe
Content	- Introduction and Motivation - Acoustic quantities - Acoustic waves - Sound sources, sound radiation - Sound engergy and intensity - Sound propagation - Signal processing - Psycho acoustics - Noise - Measurements in acoustics
Literature	Cremer, L.; Heckl, M. (1996): Körperschall. Springer Verlag, Berlin Veit, I. (1988): Technische Akustik. Vogel-Buchverlag, Würzburg Veit, I. (1988): Flüssigkeitsschall. Vogel-Buchverlag, Würzburg

Course L0518: Technical Acoustics I (Acoustic Waves, Noise Protection, Psycho Acoustics )

Typ	Recitation Section (large)
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Dr. Sören Keuchel
Language	EN
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Module M0734: Electrical Engineering Project Laboratory

Courses

Title	Typ	Hrs/wk	CP
Electrical Engineering Project Laboratory (L0640)	Project-/problem-based Learning	8	6

Module Responsible	Prof. Christian Becker
Admission Requirements	None
Recommended Previous Knowledge	Electrical Engineering I, Electrical Engineering II
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students are able to give a summary of the technical details of projects in the area of electrical engineering and illustrate respective relationships. They are capable of describing and communicating relevant problems and questions using appropriate technical language. They can explain the typical process of solving practical problems and present related results.
Skills	The students can transfer their fundamental knowledge on electrical engineering to the process of solving practical problems. They identify and overcome typical problems during the realization of projects in the context of electrical engineering. Students are able to develop, compare, and choose conceptual solutions for non-standardized problems.
Personal Competence
Social Competence	Students are able to cooperate in small, mixed-subject groups in order to independently derive solutions to given problems in the context of electrical engineering. They are able to effectively present and explain their results alone or in groups in front of a qualified audience. Students have the ability to develop alternative approaches to an electrical engineering problem independently or in groups and discuss advantages as well as drawbacks.
Autonomy	Students are capable of independently solving electrical engineering problems using provided literature. They are able to fill gaps in as well as extent their knowledge using the literature and other sources provided by the supervisor. Furthermore, they can meaningfully extend given problems and pragmatically solve them by means of corresponding solutions and concepts.
Workload in Hours	Independent Study Time 68, Study Time in Lecture 112
Credit points	6
Course achievement	None
Examination	Subject theoretical and practical work
Examination duration and scale	based on task + presentation
Assignment for the Following Curricula	General Engineering Science (German program, 7 semester): Specialisation Electrical Engineering: Compulsory Electrical Engineering: Core Qualification: Compulsory Engineering Science: Specialisation Electrical Engineering: Elective Compulsory Engineering Science: Specialisation Electrical Engineering: Elective Compulsory Technomathematics: Specialisation III. Engineering Science: Elective Compulsory

Course L0640: Electrical Engineering Project Laboratory

Typ	Project-/problem-based Learning
Hrs/wk	8
CP	6
Workload in Hours	Independent Study Time 68, Study Time in Lecture 112
Lecturer	Prof. Christian Becker, Dozenten des SD E
Language	DE
Cycle	SoSe
Content	Topics and projects cover the entire field of applications of electrical engineering. Typically, the students will prototype functional units and self-contained systems, such as radar devices, networks of sensors, amateur radio transceiver, power electronics based inverters, discrete computers, or atomic force microscopes. Different projects are devised on a yearly basis.
Literature	Alle zur Durchführung der Projekte sinnvollen Quellen (Skripte, Fachbücher, Manuals, Datenblätter, Internetseiten). / All sources that are useful for completion of the projects (lecture notes, textbooks, manuals, data sheets, internet pages).

Module M1005: Enhanced Fundamentals of Materials Science

Courses

Title	Typ	Hrs/wk	CP
Advanced Ceramics and Polymers (EN) (L2983)	Lecture	2	2
Advanced Ceramics and Polymers (EN) (L2984)	Recitation Section (large)	1	1
Materials for Energy Storage and Conversion (DE) (L1086)	Lecture	2	3

Module Responsible	Prof. Gerold Schneider
Admission Requirements	None
Recommended Previous Knowledge	Module "Fundamentals of Materials Science" Module "Materials Science Laboratory" Module "Advanced Materials"
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	The students are able to give an enhanced overview over the following topics in metals, polymers and ceramics: Atomic bonds, crystal and amorphous structures, defects , electrical and mass transport, microstructure and phase diagrams. They are capable to explain the corresponding technical terms.
Skills	The students are able to apply the appropriate physical and chemical methods for the above mentioned subjects.
Personal Competence
Social Competence
Autonomy	The students are capable to understand independently the structure and propeties of ceramics, metals and polymers. They should be able to critally evaluate the profoundness of their knowledge.
Workload in Hours	Independent Study Time 110, Study Time in Lecture 70
Credit points	6
Course achievement	None
Examination	Written exam
Examination duration and scale	180 min
Assignment for the Following Curricula	Mechanical Engineering: Specialisation Materials in Engineering Sciences: Compulsory Technomathematics: Specialisation III. Engineering Science: Elective Compulsory

Course L2983: Advanced Ceramics and Polymers (EN)

Typ	Lecture
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Kaline Pagnan Furlan, Prof. Robert Meißner
Language	EN
Cycle	SoSe
Content	After the lecture you should be able to (lecture objectives): Identify the main characteristics of polymeric and ceramic materials Understand how to process polymers and ceramics and their applications Evaluate and select polymers and ceramics according to a prospected application, linking the expected properties and design to an appropriate manufacturing method Understand about fiber-reinforced composites fabrication, processing, and properties Polymeric materials Polymers in engineering A brief history of plastics; Why plastics?; Plastics industry; Lightweight construction using plastics. Structure of the macromolecule Constitution; chain configuration; chain conformation; potentials; bonds. Synthesis, rheology Polymerization; polyaddition; polycondensation; molecular weight and distribution; crosslinking; application temperatures and processing; test methods DSC /DMTA. Plastics processing Relationships of viscosity and processing of plastics; The main manufacturing technologies and processing parameters: Extrusion, injection molding, calendering, blown films, blow molding, stretch blow molding; Which products can be manufactured with which manufacturing method. Composite materials Short fiber reinforced and injection molding; fiber types and strength; elastic properties of FRP and anisotropy. Mechanical properties Understand the material behavior of polymers under mechanical load; know that plastics have a strongly time-dependent deformation behavior and know the reasons; measurement methods to determine the load behavior (tensile test, creep or relaxation test). Plastics and the environment Understand the advantages and disadvantages of polymers in terms of environmental aspects; know that plastics can be recycled in different ways; know innovative approaches to improve the life cycle assessment. Ceramic materials Ceramics in engineering Brief history of ceramic materials; why are ceramic materials used?; relevance of ceramic materials in engineering; overview of common applications. Ceramic shaping methods Slip casting, tape casting, dip coating, filter pressing, extrusion, injection molding, die and isostatic pressing, robocasting (3D printing). Sintering Driving force and mechanism of sintering; effect of curved surfaces and diffusion paths; solid state sintering, liquid phase sintering and reaction bonding sintering; sintering stages. Colloidal science Stability of particles within a solvent; DLVO theory; zeta potential; iso-eletric point; multi-material mixes. Effect of processing on properties Understand how the different properties of ceramics are affected by the processing parameters during common processing steps. Ceramic-matrix composites Advantages of ceramic composites; influence of a second phase during sintering; continuous and discontinuous matrix; influence of second phase shape on the mechanical properties; fiber-matrix interfaces. Functional properties of ceramics and their applications Structural applications; high-temperature applications; electrical applications; filters and membranes; fuel cells; catalysis; magnetic ceramics; sensors.
Literature	Polymeric materials Polymeric Materials: Structure, Properties, Applications; G. W. Ehrenstein, Hanser Verlag, ISBN 978-3-446-21461-3 , https://katalog.tub.tuhh.de/Record/319998959 Polymer Rheology: Fundamentals and Applications; T. A. Osswald and N. Rudolph, Hanser Verlag, ISBN 978-1-56990-517-3 , https://katalog.tub.tuhh.de/Record/793882745 Rheology of filled polymer systems, A. V. Shenoy, Springer Dodrecht, ISBN 978-0-412-83100-3 , https://katalog.tub.tuhh.de/Record/244182205 Rheology of Polymeric Systems: Principles and Applications; P. J. Carreau, D. C.R. De Kee and R. P. Chhabra, Hanser Verlag, ISBN 978-1-56990-722-1 , https://doi.org/10.1016/C2018-0-01790-9 Polymer Testing; W. Grellmann and S. Seidler; Hanser Verlag, ISBN 978-1-56990-549-4 , https://katalog.tub.tuhh.de/Record/527841358 Ceramic materials D.W. Richerson, Modern ceramic engineering : properties, processing, and use in design, Dekker New York, 1992 https://katalog.tub.tuhh.de/Record/02717039X or https://katalog.tub.tuhh.de/Record/486225119 A.R. Boccaccini and N.P.Bansal, Ceramics and composites processing methods, John Wiley & Sons 2012 https://katalog.tub.tuhh.de/Record/1679605283 (Chapters 1, 4, 8 and 13) R. Riedel and I. Chen, Ceramics Science and Technology, Wiley-VCH, 2011 https://doi.org/10.1002/9783527631957 (Chapters 6, 12 and 16) R. Riedel and I. Chen, Ceramics Science and Technology - Volume 4: Applications, Wiley-VCH, 2013 https://doi.org/10.1002/9783527631971

Course L2984: Advanced Ceramics and Polymers (EN)

Typ	Recitation Section (large)
Hrs/wk	1
CP	1
Workload in Hours	Independent Study Time 16, Study Time in Lecture 14
Lecturer	Prof. Kaline Pagnan Furlan, Prof. Robert Meißner
Language	EN
Cycle	SoSe
Content
Literature

Course L1086: Materials for Energy Storage and Conversion (DE)

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Jörg Weißmüller
Language	DE/EN
Cycle	SoSe
Content	Advanced understanding of metals: • Physical materials properties o Materials behaviour - elastic, thermal, electrical o Superelasticity and shape memory effect o Fundamentals of electrical conductivity in metals and semiconductors o Superconductivity • Chemical (or "dry") corrosion o Driving forces and mechanisms o Passivation o Growth laws • Introduction to electrochemistry o Electrolytes o Ions o Solvatation o Dissolution and deposition of metals o Galvanic cells and cell voltage o Galvanic series o Nernst equation o Polarizable electrodes o Electrochemical double layer o Capacitive and pseudocapacitive processes o Capacitive currents and Faraday currents • Electrochemical (or "wet") corrosion and corrosion protection o Basic observations o Galvanic corrosion o Protection against galvanic corrosion o Stainless steel o sacrificial anodes o Passivation and Pourbaix diagrams o Corrosion through gas reduction o Crevice corrosion o Stress corrosion cracking o Alloy corrosion and nanoporous metals • Electrochemical energy storage o How a battery works o Lead accumulators o Alkaline batteries o Nickel-metal hydride accumulators o Flux batteries o Lithium-ion accumulators o Electrolytic and super capacitors o Fuel cells • Materials for hydrogen storage o Storage strategies o Requirements for storage materials o State of the art • Magnetism and magnetic materials o Phenomenology: magnetic field and magnetization o Para-, ferro-, antiferromagnets; Curie transition o Magnetism at the atomic scale; exchange coupling o Magnetization isotherms, domains o Measurement methods o Magnetocrystalline anisotropy and domain walls o Hard magnetic materials and their applications o Soft magnetic materials and their applications
Literature	- Vorlesungsskript - W.D. Callister, „Materialwissenschaften und Werkstofftechnik “, Wiley-VCH 2012 - Carl H. Hamann, Wolf Vielstich, "Elektrochemie", Wiley-VCH; 4. Auflage 2005 - Kurzweil, Dietlmeier, "Elektrochemische Speicher" Springer Vieweg (2015) (eBook: https://link.springer.com/book/10.1007/978-3-658-10900-4 ) - B. D. Cullity, C.D. Graham, "Introduction to magnetic materials", John Wiley & Sons, 2011 - D. Jiles, "Introduction to magnetism and magnetic materials", CRC press, 2015

Module M0606: Numerical Algorithms in Structural Mechanics

Courses

Title	Typ	Hrs/wk	CP
Numerical Algorithms in Structural Mechanics (L0284)	Lecture	2	3
Numerical Algorithms in Structural Mechanics (L0285)	Recitation Section (small)	2	3

Module Responsible	Prof. Alexander Düster
Admission Requirements	None
Recommended Previous Knowledge	Knowledge of partial differential equations is recommended.
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students are able to + give an overview of the standard algorithms that are used in finite element programs. + explain the structure and algorithm of finite element programs. + specify problems of numerical algorithms, to identify them in a given situation and to explain their mathematical and computer science background.
Skills	Students are able to + construct algorithms for given numerical methods. + select for a given problem of structural mechanics a suitable algorithm. + apply numerical algorithms to solve problems of structural mechanics. + implement algorithms in a high-level programming languate (here C++). + critically judge and verfiy numerical algorithms.
Personal Competence
Social Competence	Students are able to + solve problems in heterogeneous groups. + present and discuss their results in front of others. + give and accept professional constructive criticism.
Autonomy	Students are able to + assess their knowledge by means of exercises and E-Learning. + acquaint themselves with the necessary knowledge to solve research oriented tasks. + to transform the acquired knowledge to similar problems.
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Credit points	6
Course achievement	None
Examination	Written exam
Examination duration and scale	2h
Assignment for the Following Curricula	Civil Engineering: Specialisation Computational Engineering: Elective Compulsory Materials Science: Specialisation Modeling: Elective Compulsory Naval Architecture and Ocean Engineering: Core Qualification: Elective Compulsory Technomathematics: Specialisation III. Engineering Science: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Simulation Technology: Elective Compulsory

Course L0284: Numerical Algorithms in Structural Mechanics

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Alexander Düster
Language	EN
Cycle	SoSe
Content	1. Motivation 2. Basics of C++ 3. Numerical integration 4. Solution of nonlinear problems 5. Solution of linear equation systems 6. Verification of numerical algorithms 7. Selected algorithms and data structures of a finite element code
Literature	[1] D. Yang, C++ and object-oriented numeric computing, Springer, 2001. [2] K.-J. Bathe, Finite-Elemente-Methoden, Springer, 2002.

Course L0285: Numerical Algorithms in Structural Mechanics

Typ	Recitation Section (small)
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Alexander Düster
Language	EN
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Module M0594: Fundamentals of Mechanical Engineering Design

Courses

Title	Typ	Hrs/wk	CP
Fundamentals of Mechanical Engineering Design (L0258)	Lecture	2	3
Fundamentals of Mechanical Engineering Design (L0259)	Recitation Section (large)	2	3

Module Responsible	Prof. Dieter Krause
Admission Requirements	None
Recommended Previous Knowledge	Basic knowledge about mechanics and production engineering Internship (Stage I Practical)
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	After passing the module, students are able to: explain basic working principles and functions of machine elements, explain requirements, selection criteria, application scenarios and practical examples of basic machine elements, indicate the background of dimensioning calculations.
Skills	After passing the module, students are able to: accomplish dimensioning calculations of covered machine elements, transfer knowledge learned in the module to new requirements and tasks (problem solving skills), recognize the content of technical drawings and schematic sketches, technically evaluate basic designs.
Personal Competence
Social Competence	Students are able to discuss technical information in the lecture supported by activating methods.
Autonomy	Students are able to independently deepen their acquired knowledge in exercises. Students are able to acquire additional knowledge and to recapitulate poorly understood content e.g. by using the video recordings of the lectures.
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Credit points	6
Course achievement	None
Examination	Written exam
Examination duration and scale	120 min
Assignment for the Following Curricula	General Engineering Science (German program, 7 semester): Core Qualification: Compulsory Engineering Science: Specialisation Mechanical Engineering: Compulsory Engineering Science: Specialisation Biomedical Engineering: Compulsory Green Technologies: Energy, Water, Climate: Specialisation Energy Technology: Elective Compulsory Green Technologies: Energy, Water, Climate: Specialisation Maritime Technologies: Elective Compulsory Mechanical Engineering: Core Qualification: Compulsory Mechatronics: Core Qualification: Compulsory Orientation Studies: Core Qualification: Elective Compulsory Naval Architecture: Core Qualification: Compulsory Technomathematics: Specialisation III. Engineering Science: Elective Compulsory Engineering and Management - Major in Logistics and Mobility: Specialisation II. Information Technology: Elective Compulsory Engineering and Management - Major in Logistics and Mobility: Specialisation II. Production Management and Processes: Elective Compulsory

Course L0258: Fundamentals of Mechanical Engineering Design

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Dieter Krause, Prof. Nikola Bursac, Prof. Sören Ehlers
Language	DE
Cycle	SoSe
Content	Lecture Introduction to design Introduction to the following machine elements Screws Shaft-hub joints Rolling contact bearings Welding / adhesive / solder joints Springs Axes & shafts Presentation of technical objects (technical drawing) Exercise Calculation methods for dimensioning the following machine elements: Screws Shaft-hub joints Rolling contact bearings Welding / adhesive / solder joints Springs Axis & shafts
Literature	Dubbel, Taschenbuch für den Maschinenbau; Grote, K.-H., Feldhusen, J.(Hrsg.); Springer-Verlag, aktuelle Auflage. Maschinenelemente, Band I-III; Niemann, G., Springer-Verlag, aktuelle Auflage. Maschinen- und Konstruktionselemente; Steinhilper, W., Röper, R., Springer Verlag, aktuelle Auflage. Einführung in die DIN-Normen; Klein, M., Teubner-Verlag. Konstruktionslehre, Pahl, G.; Beitz, W., Springer-Verlag, aktuelle Auflage. Maschinenelemente 1-2; Schlecht, B., Pearson Verlag, aktuelle Auflage. Maschinenelemente - Gestaltung, Berechnung, Anwendung; Haberhauer, H., Bodenstein, F., Springer-Verlag, aktuelle Auflage. Roloff/Matek Maschinenelemente; Wittel, H., Muhs, D., Jannasch, D., Voßiek, J., Springer Vieweg, aktuelle Auflage. Sowie weitere Bücher zu speziellen Themen

Course L0259: Fundamentals of Mechanical Engineering Design

Typ	Recitation Section (large)
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Dieter Krause, Prof. Nikola Bursac, Prof. Sören Ehlers
Language	DE
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Module M0777: Semiconductor Circuit Design

Courses

Title	Typ	Hrs/wk	CP
Semiconductor Circuit Design (L0763)	Lecture	3	4
Semiconductor Circuit Design (L0864)	Recitation Section (small)	1	2

Module Responsible	NN
Admission Requirements	None
Recommended Previous Knowledge	Fundamentals of electrical engineering Basics of physics, especially semiconductor physics
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students are able to explain the functionality of different MOS devices in electronic circuits. Students are able to explain how analog circuits functions and where they are applied. Students are able to explain the functionality of fundamental operational amplifiers and their specifications. Students know the fundamental digital logic circuits and can discuss their advantages and disadvantages. Students have knowledge about memory circuits and can explain their functionality and specifications. Students know the appropriate fields for the use of bipolar transistors.
Skills	Students can calculate the specifications of different MOS devices and can define the parameters of electronic circuits. Students are able to develop different logic circuits and can design different types of logic circuits. Students can use MOS devices, operational amplifiers and bipolar transistors for specific applications.
Personal Competence
Social Competence	Students are able work efficiently in heterogeneous teams. Students working together in small groups can solve problems and answer professional questions.
Autonomy	Students are able to assess their level of knowledge.
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Credit points	6
Course achievement	None
Examination	Written exam
Examination duration and scale	120 min
Assignment for the Following Curricula	General Engineering Science (German program, 7 semester): Specialisation Mechanical Engineering, Focus Mechatronics: Elective Compulsory General Engineering Science (German program, 7 semester): Specialisation Electrical Engineering: Compulsory Electrical Engineering: Core Qualification: Compulsory Engineering Science: Specialisation Electrical Engineering: Compulsory Engineering Science: Specialisation Mechatronics: Compulsory Engineering Science: Specialisation Mechatronics: Elective Compulsory General Engineering Science (English program, 7 semester): Specialisation Electrical Engineering: Compulsory General Engineering Science (English program, 7 semester): Specialisation Mechatronics: Compulsory Computer Science in Engineering: Specialisation II. Mathematics & Engineering Science: Elective Compulsory Mechanical Engineering: Specialisation Mechatronics: Compulsory Mechatronics: Specialisation Electrical Systems: Compulsory Mechatronics: Core Qualification: Compulsory Mechatronics: Specialisation Robot- and Machine-Systems: Elective Compulsory Technomathematics: Specialisation III. Engineering Science: Elective Compulsory

Course L0763: Semiconductor Circuit Design

Typ	Lecture
Hrs/wk	3
CP	4
Workload in Hours	Independent Study Time 78, Study Time in Lecture 42
Lecturer	NN
Language	DE
Cycle	SoSe
Content	Repetition Semiconductorphysics and Diodes Functionality and characteristic curve of bipolar transistors Basic circuits with bipolar transistors Functionality and characteristic curve of MOS transistors Basic circuits with MOS transistors for amplifiers Operational amplifiers and their applications Typical applications for analog and digital circuits Realization of logical functions Basic circuits with MOS transistors for combinational logic Memory circuits Basic circuits with MOS transistors for sequential logic Basic concepts of analog-to-digital and digital-to-analog-converters
Literature	U. Tietze und Ch. Schenk, E. Gamm, Halbleiterschaltungstechnik, Springer Verlag, 14. Auflage, 2012, ISBN 3540428496 R. J. Baker, CMOS - Circuit Design, Layout and Simulation, J. Wiley & Sons Inc., 3. Auflage, 2011, ISBN: 047170055S H. Göbel, Einführung in die Halbleiter-Schaltungstechnik, Berlin, Heidelberg Springer-Verlag Berlin Heidelberg, 2011, ISBN: 9783642208874 ISBN: 9783642208867 URL: http://site.ebrary.com/lib/alltitles/docDetail.action?docID=10499499 URL: http://dx.doi.org/10.1007/978-3-642-20887-4 URL: http://ebooks.ciando.com/book/index.cfm/bok_id/319955 URL: http://www.ciando.com/img/bo

Course L0864: Semiconductor Circuit Design

Typ	Recitation Section (small)
Hrs/wk	1
CP	2
Workload in Hours	Independent Study Time 46, Study Time in Lecture 14
Lecturer	NN, Weitere Mitarbeiter
Language	DE
Cycle	SoSe
Content	Basic circuits and characteristic curves of bipolar transistors Basic circuits and characteristic curves of MOS transistors for amplifiers Realization and dimensioning of operational amplifiers Realization of logic functions Basic circuits with MOS transistors for combinational and sequential logic Memory circuits Circuits for analog-to-digital and digital-to-analog converters Design of exemplary circuits
Literature	U. Tietze und Ch. Schenk, E. Gamm, Halbleiterschaltungstechnik, Springer Verlag, 14. Auflage, 2012, ISBN 3540428496 R. J. Baker, CMOS - Circuit Design, Layout and Simulation, J. Wiley & Sons Inc., 3. Auflage, 2011, ISBN: 047170055S H. Göbel, Einführung in die Halbleiter-Schaltungstechnik, Berlin, Heidelberg Springer-Verlag Berlin Heidelberg, 2011, ISBN: 9783642208874 ISBN: 9783642208867 URL: http://site.ebrary.com/lib/alltitles/docDetail.action?docID=10499499 URL: http://dx.doi.org/10.1007/978-3-642-20887-4 URL: http://ebooks.ciando.com/book/index.cfm/bok_id/319955 URL: http://www.ciando.com/img/bo

Module M0604: High-Order FEM

Courses

Title	Typ	Hrs/wk	CP
High-Order FEM (L0280)	Lecture	3	4
High-Order FEM (L0281)	Recitation Section (large)	1	2

Module Responsible

Prof. Alexander Düster

Admission Requirements

None

Recommended Previous Knowledge

Knowledge of partial differential equations is recommended.

Educational Objectives

After taking part successfully, students have reached the following learning results

Professional Competence

Knowledge

Students are able to
+ give an overview of the different (h, p, hp) finite element procedures.
+ explain high-order finite element procedures.
+ specify problems of finite element procedures, to identify them in a given situation and to explain their mathematical and mechanical background.

Skills

Students are able to
+ apply high-order finite elements to problems of structural mechanics.
+ select for a given problem of structural mechanics a suitable finite element procedure.
+ critically judge results of high-order finite elements.
+ transfer their knowledge of high-order finite elements to new problems.

Personal Competence

Social Competence

Students are able to
+ solve problems in heterogeneous groups.
+ present and discuss their results in front of others.
+ give and accept professional constructive criticism.

Autonomy

Students are able to
+ assess their knowledge by means of exercises and E-Learning.
+ acquaint themselves with the necessary knowledge to solve research oriented tasks.
+ to transform the acquired knowledge to similar problems.

Workload in Hours

Independent Study Time 124, Study Time in Lecture 56

Credit points

Course achievement

Compulsory	Bonus	Form	Description
No	10 %	Presentation	Forschendes Lernen

Examination

Written exam

Examination duration and scale

120 min

Assignment for the Following Curricula

Civil Engineering: Specialisation Computational Engineering: Elective Compulsory
International Management and Engineering: Specialisation II. Product Development and Production: Elective Compulsory
Materials Science: Specialisation Modeling: Elective Compulsory
Mechanical Engineering and Management: Specialisation Product Development and Production: Elective Compulsory
Mechatronics: Technical Complementary Course: Elective Compulsory
Product Development, Materials and Production: Core Qualification: Elective Compulsory
Naval Architecture and Ocean Engineering: Core Qualification: Elective Compulsory
Technomathematics: Specialisation III. Engineering Science: Elective Compulsory
Theoretical Mechanical Engineering: Core Qualification: Elective Compulsory

Course L0280: High-Order FEM

Typ	Lecture
Hrs/wk	3
CP	4
Workload in Hours	Independent Study Time 78, Study Time in Lecture 42
Lecturer	Prof. Alexander Düster
Language	EN
Cycle	SoSe
Content	1. Introduction 2. Motivation 3. Hierarchic shape functions 4. Mapping functions 5. Computation of element matrices, assembly, constraint enforcement and solution 6. Convergence characteristics 7. Mechanical models and finite elements for thin-walled structures 8. Computation of thin-walled structures 9. Error estimation and hp-adaptivity 10. High-order fictitious domain methods
Literature	[1] Alexander Düster, High-Order FEM, Lecture Notes, Technische Universität Hamburg-Harburg, 164 pages, 2014 [2] Barna Szabo, Ivo Babuska, Introduction to Finite Element Analysis – Formulation, Verification and Validation, John Wiley & Sons, 2011

Course L0281: High-Order FEM

Typ	Recitation Section (large)
Hrs/wk	1
CP	2
Workload in Hours	Independent Study Time 46, Study Time in Lecture 14
Lecturer	Prof. Alexander Düster
Language	EN
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Module M1805: Computational Mechanics

Courses

Title	Typ	Hrs/wk	CP
Computational Mechanics (Exercises) (L1138)	Recitation Section (small)	2	2
Computational Multibody Dynamics (L1137)	Integrated Lecture	2	2
Computational Stuctural Mechanics (L2475)	Integrated Lecture	2	2

Module Responsible

Prof. Robert Seifried

Admission Requirements

None

Recommended Previous Knowledge

Mathematics I-III and Engineering Mechanics I-III

Educational Objectives

After taking part successfully, students have reached the following learning results

Professional Competence

Knowledge

The students can

describe the axiomatic procedure used in mechanical contexts;
explain important steps in model design;
present technical knowledge.

Skills

The students can

explain the important elements of mathematical / mechanical analysis and model formation, and apply it to the context of their own problems;
apply basic methods from numerical mechanics to engineering problems;
estimate the reach and boundaries of the methods and extend them to be applicable to wider problem sets.

Personal Competence

Social Competence

The students can work in groups and support each other to overcome difficulties.

Autonomy

Students are capable of determining their own strengths and weaknesses and to organize their time and learning based on those.

Workload in Hours

Independent Study Time 96, Study Time in Lecture 84

Credit points

Course achievement

Compulsory	Bonus	Form	Description
No	15 %	Midterm	Midterm Mehrkörpersysteme
No	5 %	Excercises	Hausaufgaben

Examination

Written exam

Examination duration and scale

120 min

Assignment for the Following Curricula

General Engineering Science (German program, 7 semester): Specialisation Mechanical Engineering: Compulsory
General Engineering Science (German program, 7 semester): Specialisation Biomedical Engineering: Compulsory
General Engineering Science (German program, 7 semester): Specialisation Naval Architecture: Compulsory
Energy Systems: Technical Complementary Course Core Studies: Elective Compulsory
Mechanical Engineering: Core Qualification: Compulsory
Mechatronics: Specialisation Robot- and Machine-Systems: Compulsory
Mechatronics: Specialisation Medical Engineering: Elective Compulsory
Naval Architecture: Core Qualification: Compulsory
Technomathematics: Specialisation III. Engineering Science: Elective Compulsory
Theoretical Mechanical Engineering: Technical Complementary Course Core Studies: Elective Compulsory

Course L1138: Computational Mechanics (Exercises)

Typ	Recitation Section (small)
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Robert Seifried, Prof. Christian Cyron
Language	DE
Cycle	SoSe
Content
Literature	K. Magnus, H.H. Müller-Slany: Grundlagen der Technischen Mechanik. 7. Auflage, Teubner (2009). D. Gross, W. Hauger, J. Schröder, W. Wall: Technische Mechanik 1-4. 11. Auflage, Springer (2011).

Course L1137: Computational Multibody Dynamics

Typ	Integrated Lecture
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Robert Seifried
Language	DE
Cycle	SoSe
Content	Modelling of mechanical systems Linear versus nonlinear vibration Numerical methods for time integration Vibrations with multiple degrees of freedom: free, damped, forced, modal transformation Concepts from analytical mechanics Spatial multibody systems Linearization of multibody systems Introduction to Matlab
Literature	K. Magnus, H.H. Müller-Slany: Grundlagen der Technischen Mechanik. 7. Auflage, Teubner (2009). D. Gross, W. Hauger, J. Schröder, W. Wall: Technische Mechanik 1-4. 11. Auflage, Springer (2011). W. Schiehlen, P. Eberhard: Technische Dynamik, Springer (2012).

Course L2475: Computational Stuctural Mechanics

Typ	Integrated Lecture
Hrs/wk	2
CP	2
Workload in Hours	Independent Study Time 32, Study Time in Lecture 28
Lecturer	Prof. Christian Cyron, Dr. Kevin Linka
Language	DE
Cycle	SoSe
Content	The lecture Computational Structural Mechanics extends the content of the lecture Engineering Mechanic II. It bridges the gap between the manual calculation of mechanical stress and deformation in systems with a particularly simple geometry and the efficent computer-based computation of general mechanical systems: Basics of linear continuum mechanics Planar structures: plate, membrane, slab Linientragwerke: beam, cable, truss Weak form and Galerkin's method Finite element method: theory and application Principles of mechanics: principle of virtual work, virtual displacements, virtual forces
Literature	Gross, Hauger, Wriggers, "Technische Mechanik 4", Springer

Module M2027: Modeling, Simulation and Optimization (EN)

Courses

Title	Typ	Hrs/wk	CP
Modeling, Simulation and Optimization (EN) (L2446)	Integrated Lecture	4	6

Module Responsible	Prof. Benedikt Kriegesmann
Admission Requirements	None
Recommended Previous Knowledge	Sound knowledge of engineering mathematics, engineering mechanics and fluid mechanics
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Students will have an overview of various technical problems and the differential equations, which describe them. Students will gave an overview of different solution approaches and for which kind of problems they can be used for.
Skills	Students are able to solve different technical problems with the introduced discretization methods.
Personal Competence
Social Competence	The students are able to discuss problems and jointly develop solution strategies.
Autonomy	The students are able to develop solution strategies for complex problems self-consistent and critically analyse results.
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Credit points	6
Course achievement	None
Examination	Written exam
Examination duration and scale	90 min
Assignment for the Following Curricula	General Engineering Science (German program, 7 semester): Specialisation Mechanical Engineering, Focus Theoretical Mechanical Engineering: Compulsory General Engineering Science (German program, 7 semester): Specialisation Advanced Materials: Compulsory General Engineering Science (German program, 7 semester): Specialisation Mechanical Engineering, Focus Aircraft Systems Engineering: Elective Compulsory General Engineering Science (German program, 7 semester): Specialisation Mechanical Engineering, Focus Mechatronics: Elective Compulsory Engineering Science: Core Qualification: Compulsory Engineering Science: Specialisation Advanced Materials: Compulsory Engineering Science: Specialisation Biomedical Engineering: Compulsory Engineering Science: Specialisation Mechanical Engineering and Management: Compulsory Engineering Science: Specialisation Mechatronics: Elective Compulsory Engineering Science: Specialisation Mechanical Engineering: Compulsory Mechanical Engineering: Specialisation Theoretical Mechanical Engineering: Compulsory Mechanical Engineering: Specialisation Mechatronics: Elective Compulsory Mechanical Engineering: Specialisation Aircraft Systems Engineering: Compulsory Technomathematics: Specialisation III. Engineering Science: Elective Compulsory

Course L2446: Modeling, Simulation and Optimization (EN)

Typ	Integrated Lecture
Hrs/wk	4
CP	6
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Lecturer	Prof. Benedikt Kriegesmann, Prof. Alexander Düster, Prof. Robert Seifried, Prof. Thomas Rung
Language	EN
Cycle	SoSe
Content	Partial Differential Equations in technical problems Overview of modelling approaches Finite Approximation Methods - Finite Differences / Elements / Volumes Introduction to the Discrete Element Method Numerical methods for time dependent problems Gradient-based optimization
Literature	Michael Schäfer, Computational Engineering - Introduction to Numerical Methods, Springer.

Module M1332: BIO I: Experimental Methods in Biomechanics

Courses

Title	Typ	Hrs/wk	CP
Experimental Methods in Biomechanics (L0377)	Lecture	2	3

Module Responsible	Dr. Gerd Huber
Admission Requirements	None
Recommended Previous Knowledge	It is recommended to participate in "Implantate und Frakturheilung" before attending "Experimentelle Methoden".
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	The course deals with common experimental methods used in biomechanics. For each topic an overview and some basic practical knowledge is provided. 1. Tribology 2. Optical Methods 3. Motion Analysis 4. Pressure Distribution 5. Strain Gauges 6. Pre-clinical testing 7. Specimen Preparation and Storage The students can describe the different ways how bones heal, and the requirements for their existence. The students can name different treatments for the spine and hollow bones under given fracture morphologies. The students can describe different measurement techniques for forces and movements, and choose the adequate technique for a given task.
Skills	The students can describe the basic handling of several experimental techniques used in biomechanics.
Personal Competence
Social Competence	Students are able to organize themselves as a group to solve simple experimental tasks together. On the one hand, the division of tasks must be organized during the experiment as well as during the short written elaboration, but on the other hand, the knowledge acquired must be available to all participants of the group afterwards. The challenge here is that the topics change quickly because fundamentally different measurement principles are taught. In addition, a strict time management is expected.
Autonomy	Students perform simple experimental tasks in small groups or create simple sensors (e.g. strain gauges). The preceding lecture serves as a basis for these experiments. As preparation or follow-up, the theoretical knowledge has to be worked up and related to the experimental result. In particular, independent transfer performance is necessary to clarify why experimental observations can show deviations from the theoretical values and how these deviations can be compensated.
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Credit points	3
Course achievement	None
Examination	Written exam
Examination duration and scale	90 min
Assignment for the Following Curricula	General Engineering Science (German program, 7 semester): Specialisation Mechanical Engineering, Focus Biomechanics: Compulsory General Engineering Science (German program, 7 semester): Specialisation Biomedical Engineering: Compulsory Engineering Science: Specialisation Biomedical Engineering: Elective Compulsory General Engineering Science (English program, 7 semester): Specialisation Biomedical Engineering: Elective Compulsory Mechanical Engineering: Specialisation Biomechanics: Compulsory Mechatronics: Specialisation Medical Engineering: Elective Compulsory Technomathematics: Specialisation III. Engineering Science: Elective Compulsory

Course L0377: Experimental Methods in Biomechanics

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Dr. Gerd Huber, Prof. Michael Morlock
Language	DE
Cycle	SoSe
Content	The course deals with common experimental methods used in biomechanics. For each topic an overview and some basic practical knowledge is provided. 1. Tribology 2. Optical Methods 3. Motion Analysis 4. Pressure Distribution 5. Strain Gauges 6. Pre-clinical testing 7. Specimen Preparation and Storage
Literature	Hoffmann K., Eine Einführung in die Technik des Messens mit Dehnmessstreifen White A.A., Panjabi M.M.: Clinical biomechanics of the spine Nigg, B.: Biomechanics of the musculo-skeletal system Online Hilfe von Mathworks: https://de.mathworks.com/help/matlab/

Module M1709: Applied optimization in energy and process engineering

Courses

Title	Typ	Hrs/wk	CP
Applied optimization in energy and process engineering (L2693)	Integrated Lecture	2	3
Applied optimization in energy and process engineering (L2695)	Recitation Section (small)	3	3

Module Responsible

Prof. Mirko Skiborowski

Admission Requirements

None

Recommended Previous Knowledge

Fundamentals in the field of mathematical modeling and numerical mathematics, as well as a basic understanding of process engineering processes.

In particular the contents of the module Process and Plant Engineering II

Educational Objectives

After taking part successfully, students have reached the following learning results

Professional Competence

Knowledge

The module provides a general introduction to the basics of applied mathematical optimization and deals with application areas on different scales from the identification of kinetic models, to the optimal design of unit operations and the optimization of entire (sub)processes, as well as production planning. In addition to the basic classification and formulation of optimization problems, different solution approaches are discussed and tested during the exercises. Besides deterministic gradient-based methods, metaheuristics such as evolutionary and genetic algorithms and their application are discussed as well.

• Introduction to Applied Optimization

• Formulation of optimization problems

• Linear Optimization

• Nonlinear Optimization

• Mixed-integer (non)linear optimization

• Multi-objective optimization

• Global optimization

Skills

After successful participation in the module "Applied Optimization in Energy and Process Engineering", students are able to formulate the different types of optimization problems and to select appropriate solution methods in suitable software such as Matlab and GAMS and to develop improved solution strategies. Furthermore, students will be able to interpret and critically examine the results accordingly.

Personal Competence

Social Competence

Students are capable of:

•develop solutions in heterogeneous small groups

Autonomy

Students are capable of:

•taping new knowledge on a special subject by literature research

Workload in Hours

Independent Study Time 110, Study Time in Lecture 70

Credit points

Course achievement

Compulsory	Bonus	Form	Description
No	10 %	Midterm	Bonuspunkte

Examination

Oral exam

Examination duration and scale

35 min

Assignment for the Following Curricula

Bioprocess Engineering: Specialisation A - General Bioprocess Engineering: Elective Compulsory
Chemical and Bioprocess Engineering: Specialisation Bioprocess Engineering: Elective Compulsory
Chemical and Bioprocess Engineering: Specialisation Chemical Process Engineering: Elective Compulsory
Chemical and Bioprocess Engineering: Specialisation General Process Engineering: Elective Compulsory
Chemical and Bioprocess Engineering: Specialisation Chemical and Bio process Engineering: Elective Compulsory
Energy Systems: Specialisation Energy Systems: Elective Compulsory
Environmental Engineering: Specialisation Energy and Resources: Elective Compulsory
Renewable Energies: Specialisation Bioenergy Systems: Elective Compulsory
Renewable Energies: Specialisation Wind Energy Systems: Elective Compulsory
Technomathematics: Specialisation III. Engineering Science: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Energy Systems: Elective Compulsory
Process Engineering: Specialisation Chemical Process Engineering: Elective Compulsory
Process Engineering: Specialisation Process Engineering: Elective Compulsory

Course L2693: Applied optimization in energy and process engineering

Typ	Integrated Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Mirko Skiborowski
Language	EN
Cycle	SoSe
Content	The lecture offers a general introduction to the basics and possibilities of applied mathematical optimization and deals with application areas on different scales from kinetics identification, optimal design of unit operations to the optimization of entire (sub)processes, and production planning. In addition to the basic classification and formulation of optimization problems, different solution approaches are discussed. Besides deterministic gradient-based methods, metaheuristics such as evolutionary and genetic algorithms and their application are discussed as well. - Introduction to Applied Optimization - Formulation of optimization problems - Linear Optimization - Nonlinear Optimization - Mixed-integer (non)linear optimization - Multi-objective optimization - Global optimization
Literature	Weicker, K., Evolutionäre Algortihmen, Springer, 2015 Edgar, T. F., Himmelblau D. M., Lasdon, L. S., Optimization of Chemical Processes, McGraw Hill, 2001 Biegler, L. Nonlinear Programming - Concepts, Algorithms, and Applications to Chemical Processes, 2010 Kallrath, J. Gemischt-ganzzahlige Optimierung: Modellierung in der Praxis, Vieweg, 2002

Course L2695: Applied optimization in energy and process engineering

Typ	Recitation Section (small)
Hrs/wk	3
CP	3
Workload in Hours	Independent Study Time 48, Study Time in Lecture 42
Lecturer	Prof. Mirko Skiborowski
Language	EN
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Specialization IV. Subject Specific Focus

Module M1321: Technical Complementary Course I for Technomathematics (according to Subject Specific Regulations)

Courses

Title

Typ

Hrs/wk

Module Responsible	Prof. Anusch Taraz
Admission Requirements	None
Recommended Previous Knowledge	see selected module according to FSPO
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	see selected module according to FSPO
Skills	see selected module according to FSPO
Personal Competence
Social Competence	see selected module according to FSPO
Autonomy	see selected module according to FSPO
Workload in Hours	Depends on choice of courses
Credit points	6
Assignment for the Following Curricula	Technomathematics: Specialisation IV. Subject Specific Focus: Elective Compulsory

Module M1322: Technical Complementary Course II for Technomathematics (according to Subject Specific Regulations)

Courses

Title

Typ

Hrs/wk

Module Responsible	Prof. Anusch Taraz
Admission Requirements	None
Recommended Previous Knowledge	see selected module accoording to FSPO
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	see selected module accoording to FSPO
Skills	see selected module accoording to FSPO
Personal Competence
Social Competence	see selected module accoording to FSPO
Autonomy	see selected module accoording to FSPO
Workload in Hours	Depends on choice of courses
Credit points	6
Assignment for the Following Curricula	Technomathematics: Specialisation IV. Subject Specific Focus: Elective Compulsory

Module M1957: Transferring Mathematics

Courses

Title

Typ

Hrs/wk

Module Responsible	Dozenten der Mathematik
Admission Requirements	None
Recommended Previous Knowledge
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge
Skills
Personal Competence
Social Competence
Autonomy
Workload in Hours	Independent Study Time 180, Study Time in Lecture 0
Credit points	6
Course achievement	None
Examination	Written elaboration
Examination duration and scale	Report, 5-10 pages
Assignment for the Following Curricula	Technomathematics: Specialisation IV. Subject Specific Focus: Elective Compulsory

Module M1620: Ethics in Information Technology

Courses

Title	Typ	Hrs/wk	CP
Ethics in Information Technology (L2450)	Lecture	2	3
Ethics in Information Technology (L2451)	Seminar	2	3

Module Responsible	Prof. Maximilian Kiener
Admission Requirements	None
Recommended Previous Knowledge
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	The students are familiar with: Ethical fundamental positions Meanings of the concept of information and its historical evolution Ethical fundamental issues of information technology (autonomy of algorithmic decision-making and artificial intelligence; power through access and utilization of data, etc.) Implications of increasing data collection and analysis on individuals and modern societies Data protection policies in general and in specific application areas (e.g., medical data) Effects of errors in software systems The ethical guidelines of the German Society for Computer Science (Gesellschaft für Informatik) and the recommendations for Good Scientific Practice of the DFG (German Research Foundation)
Skills	The students can: Apply ethical fundamental positions in the analysis of examples from the history and present of computer science and data science. Recognize and describe ethical conflicts regarding the collection and processing of data. Reflect on their own actions in the collection, processing, and analysis of data, as well as their consequences. Consider data protection policies and evaluate the compliance of software systems with data protection policies. Assess the impact of software errors in a specific application domain and implement appropriate measures to minimize errors.
Personal Competence
Social Competence	After completing the module, the students are able to work on subject-specific tasks independently or in groups and present them effectively.
Autonomy	After completing the module, the students are able to independently explore subfields of the subject area using specialized literature, summarize the acquired knowledge, present it, and integrate it with the content of other courses.
Workload in Hours	Independent Study Time 124, Study Time in Lecture 56
Credit points	6
Course achievement	None
Examination	Presentation
Examination duration and scale	-
Assignment for the Following Curricula	General Engineering Science (German program, 7 semester): Specialisation Data Science: Elective Compulsory Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory Data Science: Core Qualification: Compulsory Engineering Science: Specialisation Data Science: Elective Compulsory Technomathematics: Specialisation IV. Subject Specific Focus: Elective Compulsory

Course L2450: Ethics in Information Technology

Typ	Lecture
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Maximilian Kiener
Language	DE/EN
Cycle	SoSe
Content
Literature	Wird zu Beginn der Lehrveranstaltung bekannt gegeben.

Course L2451: Ethics in Information Technology

Typ	Seminar
Hrs/wk	2
CP	3
Workload in Hours	Independent Study Time 62, Study Time in Lecture 28
Lecturer	Prof. Maximilian Kiener
Language	DE/EN
Cycle	SoSe
Content	See interlocking course
Literature	See interlocking course

Thesis

Module M1800: Bachelor thesis (dual study program)

Courses

Title

Typ

Hrs/wk

Module Responsible	Professoren der TUHH
Admission Requirements	None
Recommended Previous Knowledge
Educational Objectives	After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge	Dual students… … choose central theoretical principles from their field of study (facts, theories, methods) in relation to problems and applications, present them and discuss them critically. … further develop their subject-related and practical knowledge as appropriate and link both areas of knowledge together. … present the current research available on a chosen topic or on a chosen operational issue linked to their subject.
Skills	Dual students… … evaluate both the basic knowledge linked to their field of study acquired at the university and professional knowledge gained through the company, then purposefully use it to solve technical and application-related problems. … analyse questions and problems using the methods learned throughout their studies (including practical phases), reach factually justifiable decisions and develop application-specific solutions. … critically analyse the results of their own research work from a subject-specific and professional perspective.
Personal Competence
Social Competence	Dual students… … present a professional problem in the form of an academic question for a specialist audience in a structured, comprehensible and factually correct manner, both orally and in writing. … respond to questions as part of a specialist discussion and answer them appropriately. In doing so, they argue their own evaluations and points of view convincingly.
Autonomy	Dual students… … structure a comprehensive, chronological workflow and work independently on a question to a high academic level within a given period of time. … identify, develop and link necessary knowledge and material to handle an academic and application-related problem. … apply the essential techniques of academic work when conducting their own research on an operational issue.
Workload in Hours	Independent Study Time 360, Study Time in Lecture 0
Credit points	12
Course achievement	None
Examination	Thesis
Examination duration and scale	According to General Regulations
Assignment for the Following Curricula	General Engineering Science (German program, 7 semester): Thesis: Compulsory Civil- and Environmental Engineering: Thesis: Compulsory Chemical and Bioprocess Engineering: Thesis: Compulsory Computer Science: Thesis: Compulsory Data Science: Thesis: Compulsory Electrical Engineering: Thesis: Compulsory Engineering Science: Thesis: Compulsory Green Technologies: Energy, Water, Climate: Thesis: Compulsory Computer Science in Engineering: Thesis: Compulsory Mechanical Engineering: Thesis: Compulsory Mechatronics: Thesis: Compulsory Naval Architecture: Thesis: Compulsory Technomathematics: Thesis: Compulsory Engineering and Management - Major in Logistics and Mobility: Thesis: Compulsory