Program description

Content


Core qualification

Module M0575: Procedural Programming

Courses
Title Typ Hrs/wk CP
Procedural Programming (L0197) Lecture 1 2
Procedural Programming (L0201) Recitation Section (large) 1 1
Procedural Programming (L0202) Practical Course 2 3
Module Responsible Prof. Siegfried Rump
Admission Requirements None
Recommended Previous Knowledge

Elementary PC handling skills

Elementary mathematical skills

Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

The students acquire the following knowledge:

  • They know basic elements of the programming language C. They know the basic data types and know how to use them.

  • They have an understanding of elementary compiler tasks, of the preprocessor and programming environment and know how those interact.

  • They know how to bind programs and how to include external libraries to enhance software packages.

  • They know how to use header files and how to declare function interfaces to create larger programming projects.

  • The acquire some knowledge how the program interacts with the operating system. This allows them to develop programs interacting with the programming environment as well.

  • They learnt several possibilities how to model and implement frequently occurring standard algorithms.

Skills
  • The students know how to judge the complexity of an algorithms and how to program algorithms efficiently.

  • The students are able to model and implement algorithms for a number of standard functionalities. Moreover, they are able to adapt a given API.

Personal Competence
Social Competence

The students acquire the following skills:

  • They are able to work in small teams to solve given weekly tasks, to identify and analyze programming errors and to present their results.

  • They are able to explain simple phenomena to each other directly at the PC.

  • They are able to plan and to work out a project in small teams.

  • They communicate final results and present programs to their tutor.

Autonomy
  • The students take individual examinations as well as a final written examn to prove their programming skills and ability to solve new tasks.

  • The students have many possibilities to check their abilities when solving several given programming exercises.

  • In order to solve the given tasks efficiently, the students have to split those appropriately within their group, where every student solves his or her part individually.

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 minutes
Assignment for the Following Curricula Computer Science: Core qualification: Compulsory
Data Science: Core qualification: Compulsory
Electrical Engineering: Core qualification: Compulsory
Computational Science and Engineering: Core qualification: Compulsory
Logistics and Mobility: Specialisation Engineering Science: Elective Compulsory
Mechatronics: Core qualification: Compulsory
Orientierungsstudium: Core qualification: Elective Compulsory
Technomathematics: Core qualification: Compulsory
Course L0197: Procedural Programming
Typ Lecture
Hrs/wk 1
CP 2
Workload in Hours Independent Study Time 46, Study Time in Lecture 14
Lecturer Prof. Siegfried Rump
Language DE
Cycle WiSe
Content
  • basic data types (integers, floating point format, ASCII-characters) and their dependencies on the CPU architecture
  • advanced data types (pointers, arrays, strings, structs, lists)

  • operators (arithmetical operations, logical operations, bit operations)

  • control flow (choice, loops, jumps)

  • preprocessor directives (macros, conditional compilation, modular design)

  • functions (function definitions/interface, recursive functions, "call by value" versus "call by reference", function pointers)

  • essential standard libraries and functions (stdio.h, stdlib.h, math.h, string.h, time.h)

  • file concept, streams

  • basic algorithms (sorting functions, series expansion, uniformly distributed permutation)

  • exercise programs to deepen the programming skills



Literature

Kernighan, Brian W (Ritchie, Dennis M.;)
The C programming language
ISBN: 9780131103702
Upper Saddle River, NJ [u.a.] : Prentice Hall PTR, 2009

Sedgewick, Robert 
Algorithms in C
ISBN: 0201316633
Reading, Mass. [u.a.] : Addison-Wesley, 2007 

Kaiser, Ulrich (Kecher, Christoph.;)
C/C++: Von den Grundlagen zur professionellen Programmierung
ISBN: 9783898428392
Bonn : Galileo Press, 2010

Wolf, Jürgen 
C von A bis Z : das umfassende Handbuch
ISBN: 3836214113
Bonn : Galileo Press, 2009

Course L0201: Procedural Programming
Typ Recitation Section (large)
Hrs/wk 1
CP 1
Workload in Hours Independent Study Time 16, Study Time in Lecture 14
Lecturer Prof. Siegfried Rump
Language DE
Cycle WiSe
Content See interlocking course
Literature See interlocking course
Course L0202: Procedural Programming
Typ Practical Course
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Siegfried Rump
Language DE
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M0736: Linear Algebra

Courses
Title Typ Hrs/wk CP
Linear Algebra (L0642) Lecture 4 4
Linear Algebra (L0643) Recitation Section (large) 2 2
Linear Algebra (L0645) Recitation Section (small) 2 2
Module Responsible Prof. Daniel Ruprecht
Admission Requirements None
Recommended Previous Knowledge None
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge
  • Students can name the basic concepts in linear algebra. 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 linear 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 (e.g. on their regular home work) in heterogeneously composed teams (i.e., teams from different study programs and background knowledge)  and to present their results appropriately (e.g. during exercise class).

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 128, Study Time in Lecture 112
Credit points 8
Course achievement None
Examination Written exam
Examination duration and scale 120
Assignment for the Following Curricula Computer Science: Core qualification: Compulsory
Data Science: Core qualification: Compulsory
Engineering Science: Core qualification: Compulsory
General Engineering Science (English program, 7 semester): Core qualification: Compulsory
Course L0642: Linear Algebra
Typ Lecture
Hrs/wk 4
CP 4
Workload in Hours Independent Study Time 64, Study Time in Lecture 56
Lecturer Dr. Julian Großmann
Language EN
Cycle WiSe
Content

Preliminaries

Vector spaces

Matrices and linear systems of equations

Scalar products and orthogonality

Basis transformation

Determinants

Eigen values


Literature

Strang: Linear Algebra

Beutelsbacher: Lineare Algebra

Course L0643: Linear Algebra
Typ Recitation Section (large)
Hrs/wk 2
CP 2
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Lecturer Dr. Julian Großmann, Jan Meichsner
Language EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course
Course L0645: Linear Algebra
Typ Recitation Section (small)
Hrs/wk 2
CP 2
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Lecturer Dr. Julian Großmann
Language EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M0561: Discrete Algebraic Structures

Courses
Title Typ Hrs/wk CP
Discrete Algebraic Structures (L0164) Lecture 2 3
Discrete Algebraic Structures (L0165) Recitation Section (small) 2 3
Module Responsible Prof. Karl-Heinz Zimmermann
Admission Requirements None
Recommended Previous Knowledge Mathematics from High School.
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

The students know the important basics of discrete algebraic structures including elementary combinatorial structures, monoids, groups, rings, fields, finite fields, and vector spaces. They also know specific structures like sub-. sum-, and quotient structures and homomorphisms. 

Skills

Students are able to formalize and analyze basic discrete algebraic structures.

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 Written exam
Examination duration and scale 120 min
Assignment for the Following Curricula General Engineering Science (German program, 7 semester): Specialisation Computer Science: Compulsory
Computer Science: Core qualification: Compulsory
Data Science: Core qualification: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Computer Science: Compulsory
Computational Science and Engineering: Core qualification: Compulsory
Orientierungsstudium: Core qualification: Elective Compulsory
Course L0164: Discrete Algebraic Structures
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Karl-Heinz Zimmermann
Language DE/EN
Cycle WiSe
Content
Literature
Course L0165: Discrete Algebraic Structures
Typ Recitation Section (small)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Karl-Heinz Zimmermann
Language DE/EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M0577: Non-technical Courses for Bachelors

Module Responsible Dagmar Richter
Admission Requirements None
Recommended Previous Knowledge None
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

The Non-technical Academic Programms (NTA)

imparts skills that, in view of the TUHH’s training profile, professional engineering studies require but are not able to cover fully. Self-reliance, self-management, collaboration and professional and personnel management competences. The department implements these training objectives in its teaching architecture, in its teaching and learning arrangements, in teaching areas and by means of teaching offerings in which students can qualify by opting for specific competences and a competence level at the Bachelor’s or Master’s level. The teaching offerings are pooled in two different catalogues for nontechnical complementary courses.

The Learning Architecture

consists of a cross-disciplinarily study offering. The centrally designed teaching offering ensures that courses in the nontechnical academic programms follow the specific profiling of TUHH degree courses.

The learning architecture demands and trains independent educational planning as regards the individual development of competences. It also provides orientation knowledge in the form of “profiles”

The subjects that can be studied in parallel throughout the student’s entire study program - if need be, it can be studied in one to two semesters. In view of the adaptation problems that individuals commonly face in their first semesters after making the transition from school to university and in order to encourage individually planned semesters abroad, there is no obligation to study these subjects in one or two specific semesters during the course of studies.

Teaching and Learning Arrangements

provide for students, separated into B.Sc. and M.Sc., to learn with and from each other across semesters. The challenge of dealing with interdisciplinarity and a variety of stages of learning in courses are part of the learning architecture and are deliberately encouraged in specific courses.

Fields of Teaching

are based on research findings from the academic disciplines cultural studies, social studies, arts, historical studies, migration studies, communication studies and sustainability research, and from engineering didactics. In addition, from the winter semester 2014/15 students on all Bachelor’s courses will have the opportunity to learn about business management and start-ups in a goal-oriented way.

The fields of teaching are augmented by soft skills offers and a foreign language offer. Here, the focus is on encouraging goal-oriented communication skills, e.g. the skills required by outgoing engineers in international and intercultural situations.

The Competence Level

of the courses offered in this area is different as regards the basic training objective in the Bachelor’s and Master’s fields. These differences are reflected in the practical examples used, in content topics that refer to different professional application contexts, and in the higher scientific and theoretical level of abstraction in the B.Sc.

This is also reflected in the different quality of soft skills, which relate to the different team positions and different group leadership functions of Bachelor’s and Master’s graduates in their future working life.

Specialized Competence (Knowledge)

Students can

  • locate selected specialized areas with the relevant non-technical mother discipline,
  • outline basic theories, categories, terminology, models, concepts or artistic techniques in the disciplines represented in the learning area,
  • different specialist disciplines relate to their own discipline and differentiate it as well as make connections, 
  • sketch the basic outlines of how scientific disciplines, paradigms, models, instruments, methods and forms of representation in the specialized sciences are subject to individual and socio-cultural interpretation and historicity,
  • Can communicate in a foreign language in a manner appropriate to the subject.
Skills

Professional Competence (Skills)

In selected sub-areas students can

  • apply basic methods of the said scientific disciplines,
  • auestion a specific technical phenomena, models, theories from the viewpoint of another, aforementioned specialist discipline,
  • to handle simple questions in aforementioned scientific disciplines in a sucsessful manner,
  • justify their decisions on forms of organization and application in practical questions in contexts that go beyond the technical relationship to the subject.
Personal Competence
Social Competence

Personal Competences (Social Skills)

Students will be able

  • to learn to collaborate in different manner,
  • to present and analyze problems in the abovementioned fields in a partner or group situation in a manner appropriate to the addressees,
  • to express themselves competently, in a culturally appropriate and gender-sensitive manner in the language of the country (as far as this study-focus would be chosen), 
  • to explain nontechnical items to auditorium with technical background knowledge.


Autonomy

Personal Competences (Self-reliance)

Students are able in selected areas

  • to reflect on their own profession and professionalism in the context of real-life fields of application
  • to organize themselves and their own learning processes      
  • to reflect and decide questions in front of a broad education background
  • to communicate a nontechnical item in a competent way in writen form or verbaly
  • to organize themselves as an entrepreneurial subject country (as far as this study-focus would be chosen)      
Workload in Hours Depends on choice of courses
Credit points 6
Courses
Information regarding lectures and courses can be found in the corresponding module handbook published separately.

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. Tobias Knopp
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
Autonomy
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 Computer Science: Elective Compulsory
General Engineering Science (German program, 7 semester): Specialisation Computer Science: Compulsory
Computer Science: Core qualification: Compulsory
Data Science: Core qualification: Compulsory
Engineering Science: Specialisation Mechatronics: Elective Compulsory
General Engineering Science (English program, 7 semester): Specialisation Computer Science: Elective Compulsory
General Engineering Science (English program, 7 semester): Specialisation Mechatronics: Elective Compulsory
Computational Science and Engineering: Core qualification: Compulsory
Orientierungsstudium: 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. Tobias Knopp
Language EN
Cycle SoSe
Content
  1. Propositional logic, Boolean algebra, propositional resolution, SAT-2KNF
  2. Predicate logic, unification, predicate logic resolution
  3. Temporal Logics (LTL, CTL)
  4. Deterministic finite automata, definition and construction
  5. Regular languages, closure properties, word problem, string matching
  6. Nondeterministic automata: 
    Rabin-Scott transformation of nondeterministic into deterministic automata
  7. Epsilon automata, minimization of automata,
    elimination of e-edges, uniqueness of the minimal automaton (modulo renaming of states)
  8. Myhill-Nerode Theorem: 
    Correctness of the minimization procedure, equivalence classes of strings induced by automata
  9. 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
  10. Regular expressions vs. finite automata:
    Equivalence of formalisms, systematic transformation of representations, reductions
  11. 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)
  12. Chomsky normal form
  13. CYK algorithm for deciding the word problem for context-free grammrs
  14. Deterministic pushdown automata
  15. Deterministic vs. nondeterministic pushdown automata:
    Application for parsing, LL(k) or LR(k) grammars and parsers vs. deterministic pushdown automata, compiler compiler
  16. Regular grammars
  17. Outlook: Turing machines and linear bounded automata vs general and context-sensitive grammars
  18. Chomsky hierarchy
  19. Mealy- and Moore automata:
    Automata with output (w/o accepting states), infinite state sequences, automata networks
  20. Omega automata: Automata for infinite input words, Büchi automata, representation of state transition systems, verification w.r.t. temporal logic specifications (in particular LTL)
  21. LTL safety conditions and model checking with Büchi automata, relationships between automata and logic
  22. Fixed points, propositional mu-calculus
  23. Characterization of regular languages by monadic second-order logic (MSO)
Literature
  1. Logik für Informatiker Uwe Schöning, Spektrum, 5. Aufl.
  2. Logik für Informatiker Martin Kreuzer, Stefan Kühling, Pearson Studium, 2006
  3. Grundkurs Theoretische Informatik, Gottfried Vossen, Kurt-Ulrich Witt, Vieweg-Verlag, 2010.
  4. 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. Tobias Knopp
Language EN
Cycle SoSe
Content See interlocking course
Literature See interlocking course

Module M0737: Mathematical Analysis

Courses
Title Typ Hrs/wk CP
Mathematical Analysis (L0647) Lecture 4 4
Mathematical Analysis (L0648) Recitation Section (large) 2 2
Mathematical Analysis (L0649) Recitation Section (small) 2 2
Module Responsible Prof. Daniel Ruprecht
Admission Requirements None
Recommended Previous Knowledge None
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge
  • Students can name the basic concepts in analysis. 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 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 (e.g. on their regular home work) in heterogeneously composed teams (i.e., teams from different study programs and background knowledge)  and to present their results appropriately (e.g. during exercise class).

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 128, Study Time in Lecture 112
Credit points 8
Course achievement None
Examination Written exam
Examination duration and scale 120 minutes
Assignment for the Following Curricula Computer Science: Core qualification: Compulsory
Data Science: Core qualification: Compulsory
Engineering Science: Core qualification: Compulsory
General Engineering Science (English program, 7 semester): Core qualification: Compulsory
Course L0647: Mathematical Analysis
Typ Lecture
Hrs/wk 4
CP 4
Workload in Hours Independent Study Time 64, Study Time in Lecture 56
Lecturer Dr. Julian Großmann
Language EN
Cycle SoSe
Content

Convergence, sequences, and series

Continuity

Elementary functions

Differential calculus

Integral calculus

Sequences of functions

Literature

Königsberger: Analysis

Forster: Analysis


Course L0648: Mathematical Analysis
Typ Recitation Section (large)
Hrs/wk 2
CP 2
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Lecturer Dr. Julian Großmann
Language EN
Cycle SoSe
Content See interlocking course
Literature See interlocking course
Course L0649: Mathematical Analysis
Typ Recitation Section (small)
Hrs/wk 2
CP 2
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Lecturer Dr. Julian Großmann
Language EN
Cycle SoSe
Content See interlocking course
Literature See interlocking course

Module M0727: Stochastics

Courses
Title Typ Hrs/wk CP
Stochastics (L0777) Lecture 2 4
Stochastics (L0778) Recitation Section (small) 2 2
Module Responsible Prof. Marko Lindner
Admission Requirements None
Recommended Previous Knowledge
  • Calculus
  • Discrete algebraic structures (combinatorics)
  • Propositional logic
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge Students can explain the main definitions of probability, and they can give basic definitions of modeling elements (random variables, events, dependence, independence assumptions) used in discrete and continuous settings (joint and marginal distributions, density functions). Students can describe characteristic notions such as expected values, variance, standard deviation, and moments. Students can define decision problems and explain algorithms for solving these problems (based on the chain rule or Bayesian networks). Algorithms, or estimators as they are caller, can be analyzed in terms of notions such as bias of an estimator, etc. Student can describe the main ideas of stochastic processes and explain algorithms for solving decision and computation problem for stochastic processes. Students can also explain basic statistical detection and estimation techniques.
Skills

Students can apply algorithms for solving decision problems, and they can justify whether approximation techniques are good enough in various application contexts, i.e., students can derive estimators and judge whether they are applicable or reliable.

Personal Competence
Social Competence

- Students are able to work together (e.g. on their regular home work) in heterogeneously composed teams (i.e., teams from different study programs and background knowledge)  and to present their results appropriately (e.g. during exercise class).

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 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
Computer Science: Core qualification: Compulsory
Data Science: Core qualification: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Computer Science: Compulsory
Computational Science and Engineering: Core qualification: Compulsory
Computational Science and Engineering: Core qualification: Compulsory
Logistics and Mobility: Specialisation Engineering Science: Elective Compulsory
Theoretical Mechanical Engineering: Core qualification: Elective Compulsory
Course L0777: Stochastics
Typ Lecture
Hrs/wk 2
CP 4
Workload in Hours Independent Study Time 92, Study Time in Lecture 28
Lecturer Dr. Christian Seifert
Language DE/EN
Cycle SoSe
Content

Foundations of probability theory

  • Definitions of probability, conditional probability
  • Random variables, dependencies, independence assumptions, 
  • Marginal and joint probabilities
  • Distributions and density functions
  • Characteristics: expected values, variance, standard deviation, moments

Practical representations for joint probabilities

  • Bayessche Netzwerke
  • Semantik, Entscheidungsprobleme, exakte und approximative Algorithmen

Stochastic processes

  • Stationarity, ergodicity
  • Correlations
  • Dynamic Bayesian networks, Hidden Markov networks, Kalman filters, queues

Detection & estimation

  • Detectors
  • Estimation rules and procedures
  • Hypothesis and distribution tests
  • Stochastic regression
Literature
  1. Methoden der statistischen Inferenz, Likelihood und Bayes, Held, L., Spektrum 2008
  2. Stochastik für Informatiker, Dümbgen, L., Springer 2003
  3. Statistik: Der Weg zur Datenanalyse, Fahrmeir, L., Künstler R., Pigeot, I, Tutz, G., Springer 2010
  4. Stochastik, Georgii, H.-O., deGruyter, 2009
  5. Probability and Random Processes, Grimmett, G., Stirzaker, D., Oxford University Press, 2001
  6. Programmieren mit R, Ligges, U., Springer 2008
Course L0778: Stochastics
Typ Recitation Section (small)
Hrs/wk 2
CP 2
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Lecturer Dr. Christian Seifert
Language DE/EN
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
Computational Science and Engineering: 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 M1592: Advanced Stochastics

Courses
Title Typ Hrs/wk CP
Advanced Stochastics (L2430) Lecture 2 4
Advanced Stochastics (L2431) Recitation Section (small) 2 2
Module Responsible Prof. Marko Lindner
Admission Requirements None
Recommended Previous Knowledge Stochastics
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge Students can explain the main concepts and definitions of descriptive statistics, statistical testing, robust estimation and time series analysis.
Skills Students can apply algorithms, in particular statistical standard software, to the above mentioned problems.
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).
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 6
Course achievement None
Examination Written exam
Examination duration and scale 90 min
Assignment for the Following Curricula Data Science: Core qualification: Compulsory
Course L2430: Advanced Stochastics
Typ Lecture
Hrs/wk 2
CP 4
Workload in Hours Independent Study Time 92, Study Time in Lecture 28
Lecturer Prof. Marko Lindner
Language DE/EN
Cycle WiSe
Content
  • descriptive statistics
  • statistical software
  • estimation and testing
  • nonparametric statistics
  • robust estimation
  • time series analysis
Literature
Course L2431: Advanced Stochastics
Typ Recitation Section (small)
Hrs/wk 2
CP 2
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Lecturer Prof. Marko Lindner
Language DE/EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M0853: Mathematics III

Courses
Title Typ Hrs/wk CP
Analysis III (L1028) Lecture 2 2
Analysis III (L1029) Recitation Section (small) 1 1
Analysis III (L1030) Recitation Section (large) 1 1
Differential Equations 1 (Ordinary Differential Equations) (L1031) Lecture 2 2
Differential Equations 1 (Ordinary Differential Equations) (L1032) Recitation Section (small) 1 1
Differential Equations 1 (Ordinary Differential Equations) (L1033) Recitation Section (large) 1 1
Module Responsible Prof. Anusch Taraz
Admission Requirements None
Recommended Previous Knowledge Mathematics I + II
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge
  • Students can name the basic concepts in the area of analysis and differential equations. 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 the area of analysis and differential equations 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 128, Study Time in Lecture 112
Credit points 8
Course achievement None
Examination Written exam
Examination duration and scale 60 min (Analysis III) + 60 min (Differential Equations 1)
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
Computer Science: Core qualification: Compulsory
Data Science: Core qualification: Compulsory
Digital Mechanical Engineering: Core qualification: Compulsory
Electrical Engineering: Core qualification: Compulsory
Energy and Environmental Engineering: Core qualification: Compulsory
Engineering Science: Core qualification: Compulsory
General Engineering Science (English program, 7 semester): Core qualification: Compulsory
Computational Science and Engineering: Core qualification: Compulsory
Mechanical Engineering: Core qualification: Compulsory
Mechatronics: Core qualification: Compulsory
Naval Architecture: Core qualification: Compulsory
Process Engineering: Core qualification: Compulsory
Course L1028: Analysis III
Typ Lecture
Hrs/wk 2
CP 2
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Lecturer Dozenten des Fachbereiches Mathematik der UHH
Language DE
Cycle WiSe
Content

Main features of differential and integrational calculus of several variables 

  • Differential calculus for several variables
  • Mean value theorems and Taylor's theorem
  • Maximum and minimum values
  • Implicit functions
  • Minimization under equality constraints
  • Newton's method for multiple variables
  • Double integrals over general regions
  • Line and surface integrals
  • Theorems of Gauß and Stokes
Literature
  • http://www.math.uni-hamburg.de/teaching/export/tuhh/index.html


Course L1029: Analysis III
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 WiSe
Content See interlocking course
Literature See interlocking course
Course L1030: Analysis III
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 WiSe
Content See interlocking course
Literature See interlocking course
Course L1031: Differential Equations 1 (Ordinary Differential Equations)
Typ Lecture
Hrs/wk 2
CP 2
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Lecturer Dozenten des Fachbereiches Mathematik der UHH
Language DE
Cycle WiSe
Content

Main features of the theory and numerical treatment of ordinary differential equations 

  • Introduction and elementary methods
  • Exsitence and uniqueness of initial value problems
  • Linear differential equations
  • Stability and qualitative behaviour of the solution
  • Boundary value problems and basic concepts of calculus of variations
  • Eigenvalue problems
  • Numerical methods for the integration of initial and boundary value problems
  • Classification of partial differential equations

Literature
  • http://www.math.uni-hamburg.de/teaching/export/tuhh/index.html


Course L1032: Differential Equations 1 (Ordinary Differential Equations)
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 WiSe
Content See interlocking course
Literature See interlocking course
Course L1033: Differential Equations 1 (Ordinary Differential Equations)
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 WiSe
Content See interlocking course
Literature See interlocking course

Module M0662: Numerical Mathematics I

Courses
Title Typ Hrs/wk CP
Numerical Mathematics I (L0417) Lecture 2 3
Numerical Mathematics I (L0418) Recitation Section (small) 2 3
Module Responsible Prof. Sabine Le Borne
Admission Requirements None
Recommended Previous Knowledge
  • Mathematik I + II for Engineering Students (german or english) or Analysis & Linear Algebra I + II for Technomathematicians
  • basic MATLAB knowledge
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students are able to

  • name numerical methods for interpolation, integration, least squares problems, eigenvalue problems, nonlinear root finding problems and to explain their core ideas,
  • repeat convergence statements for the numerical methods,
  • explain aspects for the practical execution of numerical methods with respect to computational and storage complexitx.


Skills

Students are able to

  • implement, apply and compare numerical methods using MATLAB,
  • justify the convergence behaviour of numerical methods with respect to the problem and solution algorithm,
  • select and execute a suitable solution approach for a given problem.
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 Written exam
Examination duration and scale 90 minutes
Assignment for the Following Curricula General Engineering Science (German program, 7 semester): Specialisation Computer Science: Compulsory
General Engineering Science (German program, 7 semester): Specialisation Mechanical Engineering, Focus Materials in Engineering Sciences: Compulsory
General Engineering Science (German program, 7 semester): Specialisation Biomedical Engineering: Compulsory
General Engineering Science (German program, 7 semester): Specialisation Mechanical Engineering, Focus Biomechanics: Compulsory
General Engineering Science (German program, 7 semester): Specialisation Mechanical Engineering, Focus Theoretical Mechanical Engineering: Compulsory
Bioprocess Engineering: Specialisation A - General Bioprocess Engineering: Elective Compulsory
Computer Science: Specialisation Computational Mathematics: Elective Compulsory
Computer Science: Specialisation II. Mathematics and Engineering Science: Elective Compulsory
Data Science: Core qualification: Compulsory
Electrical Engineering: Core qualification: Elective Compulsory
Engineering Science: Core qualification: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Mechanical Engineering, Focus Theoretical Mechanical Engineering: Elective Compulsory
General Engineering Science (English program, 7 semester): Core qualification: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Computer Science: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Mechanical Engineering, Focus Biomechanics: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Mechanical Engineering, Focus Materials in Engineering Sciences: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Mechanical Engineering, Focus Theoretical Mechanical Engineering: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Biomedical Engineering: Compulsory
Computational Science and Engineering: Core qualification: Compulsory
Mechanical Engineering: Specialisation Theoretical Mechanical Engineering: Elective Compulsory
Mechanical Engineering: Specialisation Theoretical Mechanical Engineering: Compulsory
Mechanical Engineering: Specialisation Energy Systems: Elective Compulsory
Theoretical Mechanical Engineering: Technical Complementary Course Core Studies: Elective Compulsory
Process Engineering: Specialisation Process Engineering: Elective Compulsory
Course L0417: Numerical Mathematics I
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
  1. Error analysis: Number representation, error types, conditioning and stability
  2. Interpolation: polynomial and spline interpolation
  3. Numerical integration and differentiation: order, Newton-Cotes formula, error estimates, Gaussian quadrature, adaptive quadrature, difference formulas
  4. Linear systems: LU and Cholesky factorization, matrix norms, conditioning
  5. Linear least squares problems: normal equations, Gram.Schmidt and Householder orthogonalization, singular value decomposition, regularization
  6. Eigenvalue problems: power iteration, inverse iteration, QR algorithm
  7. Nonlinear systems of equations: Fixed point iteration, root-finding algorithms for real-valued functions, Newton and Quasi-Newton methods for systems
Literature
  • Stoer/Bulirsch: Numerische Mathematik 1, Springer
  • Dahmen, Reusken: Numerik für Ingenieure und Naturwissenschaftler, Springer


Course L0418: Numerical Mathematics I
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, Dr. Jens-Peter Zemke
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 6
Course achievement None
Examination Written exam
Examination duration and scale 60 min
Assignment for the Following Curricula Computer Science: Core qualification: Compulsory
Data Science: Core qualification: Compulsory
Computational Science and Engineering: Core qualification: 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 M0625: Databases

Courses
Title Typ Hrs/wk CP
Databases (L0337) Lecture 4 5
Databases (L1150) Project-/problem-based Learning 1 1
Module Responsible NN
Admission Requirements None
Recommended Previous Knowledge

Students should habe basic knowledge in the following areas:

  • Discrete Algebraic Structures
  • Procedural Programming
  • Logic, Automata, and Formal Languages
  • Object-Oriented Programming, Algorithms and Data Structures
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students can explain the general architecture of an application system that is based on a database. They describe the syntax and semantics of the Entity Relationship conceptual modeling languages, and they can enumerate basic decision problems and know which features of a domain model can be captured with ER and which features cannot be represented. Furthermore, students can summarize the features of the relational data model, and can describe how ER models can be systematically transformed into the relational data model. Student are able to discuss dependency theory using the operators of relational algebra, and they know how to use relational algebra as a query language. In addition, they can sketch the main modules of the architecture of a database system from an implementation point of view. Storage and index structures as well as query answering and optimization techniques can be explained. The role of transactions can be described in terms of ACID conditions and common recovery mechanisms can be characterized. The students can recall why recursion is important for query languages and describe how Datalog can be used and implemented.They demonstrate how Datalog can be used for information integration. For solving ER decision problems the students can explain description logics with their syntax and semantics, they describe description logic decision problems and explain how these problems can be mapped onto each other. They can sketch the idea of ontology-based data access and can name the main complexity measure in database theory. Last but not least, the students can describe the main features of XML and can explain XPath and XQuery as query languages.

Skills

Students can apply ER for describing domains for which they receive a textual description, and students can transform relational schemata with a given set of functional dependencies into third normal form or even Boyce-Codd normal form. They can also apply relational algebra, SQL, or Datalog to specify queries. Using specific datasets, they can explain how index structures work (e.g., B-trees) and how index structures change while data is added or deleted. They can rewrite queries for better performance of query evaluation. Students can analyse which query language expressivity is required for which application problem. Description logics can be applied for domain modeling, and students can transform ER diagrams into description logics in order to check for consistency and implicit subsumption relations.  They solve data integration problems using Datalog and LAV or GAV rules. Students can apply XPath and Xquery to retrieve certain patterns in XML data.

Personal Competence
Social Competence Students develop an understanding of social structures in a company used for developing real-world products. They know the responsibilities of data analysts, programmers, and managers in the overall production process.
Autonomy
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: Specialisation Computer and Software Engineering: Elective Compulsory
Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory
Data Science: Core qualification: Compulsory
Technomathematics: Specialisation II. Informatics: Elective Compulsory
Course L0337: Databases
Typ Lecture
Hrs/wk 4
CP 5
Workload in Hours Independent Study Time 94, Study Time in Lecture 56
Lecturer NN
Language EN
Cycle WiSe
Content
  • Architecture of database systems, conceptual data modeling with the Entity Relationship (ER) modeling language
  • Relational data model, referential integrity, keys, foreign keys, functional dependencies (FDs), canonical mapping of entity types and relationship into the relational data model, anomalies
  • Relational algebra as a simple query language
  • Dependency theory, FD closure, canonical cover of FD set, decomposition of relational schemata, multivalued dependencies, normalization, inclusion dependencies
  • Practical query languages and integrity constraints w/o considering a conceptual domain model: SQL 
  • Storage structures, database implementation architecture
  • Index structures
  • Query processing
  • Query optimization
  • Transactions and recovery
  • Query languages with recursion and consideration of a simple conceptual domain model: Datalog
  • Semi-naive evaluation strategy, magic sets transformation
  • Information integration, declarative schema transformation (LAV, GAV), distributed database systems
  • Description logics, syntax, semantics, decision problems, decision algorithms for Abox satisfiability
  • Ontology based data access (OBDA), DL-Lite for formalizing ER diagramms
  • Complexity measure: Data complexity
  • Semistructured databases and query languages: XML and XQuery
Literature
  1. A. Kemper, A. Eickler, Datenbanksysteme - n. Auflage, Oldenbourg, 2010
  2. S. Abiteboul, R. Hull, V. Vianu, Foundations of Databases, Addison-Wesley, 1995
  3. Database Systems, An Application Oriented Approach, Pearson International Edition, 2005
  4. H. Garcia-Molina, J.D. Ullman, J. Widom, Database Systems: The Complete Book, Prentice Hall, 2002

Course L1150: Databases
Typ Project-/problem-based Learning
Hrs/wk 1
CP 1
Workload in Hours Independent Study Time 16, Study Time in Lecture 14
Lecturer NN
Language EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M1595: Machine Learning

Courses
Title Typ Hrs/wk CP
Machine Learning (L2432) Lecture 2 3
Machine Learning (L2433) Recitation Section (small) 2 3
Module Responsible Prof. Tobias Knopp
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 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 Data Science: Core qualification: Compulsory
Course L2432: Machine Learning
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer NN
Language DE/EN
Cycle SoSe
Content
  • supervised learning techniques (generative/descriptive learning, parametric/non-parametric learning, neural networks, support vector machines)
  • unsupervised learning techniques (clustering, dimension reduction, kernel methods)
  • fundamentals of statistical learning theory
  • advanced techniques such as transfer learning, reinforcement learning, generative adversarial networks and adaptive control
Literature
  • An Introduction to Statistical Learning, James, Witten, Hastie, Tibshirani
  • Pattern Recognition and Machine Learning, Bishop
Course L2433: Machine Learning
Typ Recitation Section (small)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer NN
Language DE/EN
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.
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: Core qualification: Compulsory
Data Science: Core qualification: Compulsory
Electrical Engineering: Core qualification: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Electrical Engineering: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Bioprocess Engineering: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Computer Science: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Mechanical Engineering, Focus Biomechanics: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Mechanical Engineering, Focus Energy Systems: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Mechanical Engineering, Focus Aircraft Systems Engineering: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Mechanical Engineering, Focus Materials in Engineering Sciences: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Mechanical Engineering, Focus Mechatronics: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Mechanical Engineering, Focus Theoretical Mechanical Engineering: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Process Engineering: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Biomedical Engineering: Compulsory
Computational Science and Engineering: Core qualification: 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 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
Computer Science: Core qualification: Compulsory
Computer Science: Core qualification: Compulsory
Data Science: Core qualification: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Computer Science: Compulsory
Logistics and Mobility: Specialisation Engineering Science: Elective Compulsory
Technomathematics: Specialisation I. Mathematics: 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 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. Christoph Ihl
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: 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
Computer Science: Core qualification: Compulsory
Data Science: Core qualification: Compulsory
Electrical Engineering: Core qualification: Compulsory
Energy and Environmental Engineering: Core qualification: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Electrical Engineering: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Civil Engineering: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Bioprocess Engineering: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Energy and Enviromental Engineering: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Computer Science: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Mechanical Engineering, Focus Biomechanics: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Mechanical Engineering, Focus Energy Systems: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Mechanical Engineering, Focus Aircraft Systems Engineering: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Mechanical Engineering, Focus Materials in Engineering Sciences: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Mechanical Engineering, Focus Mechatronics: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Mechanical Engineering, Focus Product Development and Production: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Mechanical Engineering, Focus Theoretical Mechanical Engineering: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Naval Architecture: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Process Engineering: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Biomedical Engineering: Compulsory
Computational Science and Engineering: Core qualification: Compulsory
Logistics and Mobility: Core qualification: Compulsory
Mechanical Engineering: Core qualification: Compulsory
Mechatronics: Core qualification: Compulsory
Orientierungsstudium: Core qualification: Elective Compulsory
Naval Architecture: Core qualification: Compulsory
Technomathematics: Core qualification: Compulsory
Process Engineering: 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. Christoph Ihl, Katharina Roedelius, Tobias Vlcek
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. Christoph Ihl, Prof. Thorsten Blecker, Prof. Christian Lüthje, Prof. Christian Ringle, Prof. Kathrin Fischer, Prof. Cornelius Herstatt, Prof. Wolfgang Kersten, Prof. Matthias Meyer, Prof. Thomas Wrona
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 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 Written exam
Examination duration and scale 90 min
Assignment for the Following Curricula Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory
Data Science: Core qualification: 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
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
Cycle SoSe
Content See interlocking course
Literature See interlocking course

Module M0953: Introduction to Information Security

Courses
Title Typ Hrs/wk CP
Introduction to Information Security (L1114) Lecture 3 3
Introduction to Information Security (L1115) Recitation Section (small) 2 3
Module Responsible Prof. Dieter Gollmann
Admission Requirements None
Recommended Previous Knowledge Basics of Computer Science
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students can 

  • name the main security risks when using Information and Communication Systems and name the fundamental security mechanisms, 
  • describe commonly used methods for risk and security analysis,  
  • name the fundamental principles of data protection.
Skills

Students can

  • evaluate the strenghts and weaknesses of the fundamental security mechanisms and of the commonly used methods for risk and security analysis, 

  • apply the fundamental principles of data protection to concrete cases.

Personal Competence
Social Competence Students are capable of appreciating the impact of security problems on  those affected and of the potential responsibilities for their resolution. 
Autonomy None
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
Assignment for the Following Curricula Computer Science: Core qualification: Compulsory
Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory
Data Science: Core qualification: Compulsory
Computational Science and Engineering: Specialisation Computer Science: Elective Compulsory
Course L1114: Introduction to Information Security
Typ Lecture
Hrs/wk 3
CP 3
Workload in Hours Independent Study Time 48, Study Time in Lecture 42
Lecturer Prof. Dieter Gollmann
Language EN
Cycle WiSe
Content
  • Fundamental concepts
  • Passwords & biometrics
  • Introduction to cryptography
  • Sessions, SSL/TLS
  • Certificates, electronic signatures
  • Public key infrastructures
  • Side-channel analysis
  • Access control
  • Privacy
  • Software security basics
  • Security management & risk analysis
  • Security evaluation: Common Criteria




Literature

D. Gollmann: Computer Security, Wiley & Sons, third edition, 2011

Ross Anderson: Security Engineering, Wiley & Sons, second edition, 2008


Course L1115: Introduction to Information Security
Typ Recitation Section (small)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Dieter Gollmann
Language EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M1594: Practical Course Data Science

Courses
Title Typ Hrs/wk CP
Practical Course Data Science (L2436) Practical Course 8 6
Module Responsible Prof. Tobias Knopp
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 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 Data Science: Core qualification: Compulsory
Course L2436: Practical Course Data Science
Typ Practical Course
Hrs/wk 8
CP 6
Workload in Hours Independent Study Time 68, Study Time in Lecture 112
Lecturer NN
Language DE/EN
Cycle WiSe
Content

During the internship, a project from the entire field of data science will be worked on. The concrete task is determined by the respective lecturer. The participating students work on the solution in a team. A typical project sequence as it will occur in the later professional practice is run through. This includes requirements analysis, implementation and testing of a data-oriented software system. Depending on the project, the data to be used must first be collected and prepared so that it can be used in a machine learning process. The project planning and task sharing is done by the students. During the project the common design tools are used to support planning, administration and realization.

Literature

Wird durch die jeweiligen DozentInnen zur Verfügung gestellt.

Supplied by the respective lecturer.

Module M1593: Data Mining

Courses
Title Typ Hrs/wk CP
Data Mining (L2434) Lecture 2 3
Data Mining (L2435) Recitation Section (small) 2 3
Module Responsible Prof. Tobias Knopp
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

The students know

  • various forms of knowledge representation
  • different methods for cluster analysis and classification
  • methods for data preprocessing
  • tools for the visualization of large amounts of data
  • methods for the evaluation of data
  • text, web and stream mining; time series analysis

Skills

Students are able to analyze large, heterogeneous data using clustering and classification methods. They can preprocess the data so that a data-driven model  can be trained with homogeneous data. The students are able to visualize large amounts of data and their internal structures and to evaluate the 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 6
Course achievement None
Examination Written exam
Examination duration and scale 90 min
Assignment for the Following Curricula Data Science: Core qualification: 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 NN
Language DE/EN
Cycle WiSe
Content
  • knowledge representation
  • clustering
  • classification
  • prepro­cess­ing (fea­ture sub­set se­lec­tion, discretization, sam­pling, data clean­ing)
  • text, web and stream mining; time series analysis
  • association rules
  • visualization 
  • data evaluation
Literature

Data Mining and Analysis: Fundamental Concepts and Algorithms, Mohammed J. Zaki  and Wagner Meira Jr

Course L2435: Data Mining
Typ Recitation Section (small)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer NN
Language DE/EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M1598: Image Processing

Courses
Title Typ Hrs/wk CP
Image Processing (L2443) Lecture 2 4
Image Processing (L2444) Recitation Section (small) 2 2
Module Responsible Prof. Tobias Knopp
Admission Requirements None
Recommended Previous Knowledge Signal and Systems
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

The students know about

  • visual perception
  • multidimensional signal processing
  • sampling and sampling theorem
  • filtering
  • image enhancement
  • edge detection
  • multi-resolution procedures: Gauss and Laplace pyramid, wavelets
  • image compression
  • image segmentation
  • morphological image processing
Skills

The students can

  • analyze, process, and improve multidimensional image data
  • implement simple compression algorithms
  • design custom filters for specific applications
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 Data Science: Core qualification: Elective Compulsory
Course L2443: Image Processing
Typ Lecture
Hrs/wk 2
CP 4
Workload in Hours Independent Study Time 92, Study Time in Lecture 28
Lecturer Prof. Tobias Knopp
Language DE/EN
Cycle WiSe
Content
  • Visual perception
  • Multidimensional signal processing
  • Sampling and sampling theorem
  • Filtering
  • Image enhancement
  • Edge detection
  • Multi-resolution procedures: Gauss and Laplace pyramid, wavelets
  • Image Compression
  • Segmentation
  • Morphological image processing
Literature

Bredies/Lorenz, Mathematische Bildverarbeitung, Vieweg, 2011
Pratt, Digital Image Processing, Wiley, 2001
Bernd Jähne: Digitale Bildverarbeitung - Springer, Berlin 2005

Course L2444: Image Processing
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 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 6
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: Core qualification: Elective Compulsory
Data Science: Technical Complementary Course: Elective Compulsory
Engineering Science: Specialisation Mechatronics: Elective Compulsory
General Engineering Science (English program, 7 semester): Specialisation Computer Science: Elective Compulsory
General Engineering Science (English program, 7 semester): Specialisation Mechatronics: Elective Compulsory
Computational Science and Engineering: Specialisation I. Computer Science: Elective Compulsory
Computational Science and Engineering: Specialisation 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.

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 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 Computer and Software Engineering: Elective Compulsory
Computer Science: Specialisation Computational Mathematics: Elective Compulsory
Computer Science: Specialisation II. Mathematics and Engineering Science: Elective Compulsory
Data Science: Core qualification: Elective Compulsory
Computational Science and Engineering: Specialisation II. Mathematics & Engineering Science: Elective Compulsory
Computational Science and Engineering: Specialisation Computer 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
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 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 6
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 Bioprocess Engineering: Compulsory
General Engineering Science (German program, 7 semester): Specialisation Naval Architecture: Compulsory
General Engineering Science (German program, 7 semester): Specialisation Electrical Engineering: Compulsory
General Engineering Science (German program, 7 semester): Specialisation Biomedical Engineering: Compulsory
General Engineering Science (German program, 7 semester): Specialisation Energy and Enviromental Engineering: Compulsory
General Engineering Science (German program, 7 semester): Specialisation Process Engineering: 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 Biomechanics: Compulsory
General Engineering Science (German program, 7 semester): Specialisation Mechanical Engineering, Focus Aircraft Systems Engineering: Compulsory
General Engineering Science (German program, 7 semester): Specialisation Mechanical Engineering, Focus Materials in Engineering Sciences: Compulsory
General Engineering Science (German program, 7 semester): Specialisation Mechanical Engineering, Focus Theoretical Mechanical Engineering: Compulsory
General Engineering Science (German program, 7 semester): Specialisation Mechanical Engineering, Focus Product Development and Production: Compulsory
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 Energy Systems: Compulsory
General Engineering Science (German program, 7 semester): Specialisation Civil Engineering: Compulsory
Computer Science: Core qualification: Compulsory
Data Science: Core qualification: Elective Compulsory
Electrical Engineering: Core qualification: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Electrical Engineering: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Civil Engineering: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Bioprocess Engineering: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Energy and Enviromental Engineering: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Computer Science: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Mechanical Engineering, Focus Biomechanics: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Mechanical Engineering, Focus Energy Systems: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Mechanical Engineering, Focus Aircraft Systems Engineering: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Mechanical Engineering, Focus Materials in Engineering Sciences: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Mechanical Engineering, Focus Mechatronics: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Mechanical Engineering, Focus Product Development and Production: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Mechanical Engineering, Focus Theoretical Mechanical Engineering: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Naval Architecture: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Process Engineering: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Biomedical Engineering: Compulsory
Computational Science and Engineering: Core qualification: Compulsory
Mechatronics: Core qualification: 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 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

Basics of Computer Science

Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students are able to explain important and common Internet protocols in detail and classify them, in order to be able to analyse and develop networked systems in further studies and job.

Skills

Students are able to analyse common Internet protocols and evaluate the use of them in different domains.

Personal Competence
Social Competence


Autonomy

Students can select relevant parts out of 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: Core qualification: Elective Compulsory
Electrical Engineering: Core qualification: Elective Compulsory
Engineering Science: Specialisation Mechatronics: Elective Compulsory
General Engineering Science (English program, 7 semester): Specialisation Computer Science: Elective Compulsory
General Engineering Science (English program, 7 semester): Specialisation Mechatronics: Elective Compulsory
Computational Science and 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 Prof. Andreas Timm-Giel, Prof. Dieter Gollmann
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 (virtual) labs.

In the second part of the lecture an introduction to Internet security is given.

This class comprises:

  • Application layer protocols (HTTP, FTP, 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
  • Multimedia applications in the Internet
  • Network management
  • Internet security: IPSec
  • Internet security: Firewalls
Literature


  • Kurose, Ross, Computer Networking - A Top-Down Approach, 6th Edition, Addison-Wesley
  • Kurose, Ross, Computernetzwerke - Der Top-Down-Ansatz, Pearson Studium; Auflage: 6. Auflage
  • W. Stallings: Cryptography and Network Security: Principles and Practice, 6th 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 Prof. Andreas Timm-Giel, Prof. Dieter Gollmann
Language EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M1235: Electrical Power Systems I: Introduction to Electrical Power Systems

Courses
Title Typ Hrs/wk CP
Electrical Power Systems I: Introduction to Electrical Power Systems (L1670) Lecture 3 4
Electrical Power Systems I: Introduction to Electrical Power Systems (L1671) Recitation Section (large) 2 2
Module Responsible Prof. Christian Becker
Admission Requirements None
Recommended Previous Knowledge

Fundamentals of Electrical Engineering

Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students are able to give an overview of conventional and modern electric power systems.  They can explain in detail and critically evaluate technologies of electric power generation, transmission, storage, and distribution as well as integration of equipment into electric power systems.

Skills

With completion of this module the students are able to apply the acquired skills in applications of the design, integration, development of electric power systems and to assess the results.

Personal Competence
Social Competence

The students can participate in specialized and interdisciplinary discussions, advance ideas and represent their own work results in front of others.

Autonomy

Students can independently tap knowledge of the emphasis of the lectures. 

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: Elective Compulsory
Data Science: Core qualification: Elective Compulsory
Electrical Engineering: Core qualification: Elective Compulsory
Energy and Environmental Engineering: Specialisation Energy Engineering: Elective Compulsory
Energy Systems: Specialisation Energy Systems: Elective Compulsory
General Engineering Science (English program, 7 semester): Specialisation Electrical Engineering: Elective Compulsory
Computational Science and Engineering: Specialisation II. Mathematics & Engineering Science: Elective Compulsory
Computational Science and Engineering: Specialisation Engineering Sciences: Elective Compulsory
Renewable Energies: Core qualification: Compulsory
Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Energy Systems: Elective Compulsory
Course L1670: Electrical Power Systems I: Introduction to Electrical Power Systems
Typ Lecture
Hrs/wk 3
CP 4
Workload in Hours Independent Study Time 78, Study Time in Lecture 42
Lecturer Prof. Christian Becker
Language DE
Cycle WiSe
Content
  • fundamentals and current development trends in electric power engineering 
  • tasks and history of electric power systems
  • symmetric three-phase systems
  • fundamentals and modelling of eletric power systems 
    • lines
    • transformers
    • synchronous machines
    • induction machines
    • loads and compensation
    • grid structures and substations 
  • fundamentals of energy conversion
    • electro-mechanical energy conversion
    • thermodynamics
    • power station technology
    • renewable energy conversion systems
  • steady-state network calculation
    • network modelling
    • load flow calculation
    • (n-1)-criterion
  • symmetric failure calculations, short-circuit power
  • control in networks and power stations
  • grid protection
  • grid planning
  • power economy fundamentals
Literature

K. Heuck, K.-D. Dettmann, D. Schulz: "Elektrische Energieversorgung", Vieweg + Teubner, 9. Auflage, 2013

A. J. Schwab: "Elektroenergiesysteme", Springer, 5. Auflage, 2017

R. Flosdorff: "Elektrische Energieverteilung" Vieweg + Teubner, 9. Auflage, 2008

Course L1671: Electrical Power Systems I: Introduction to Electrical Power Systems
Typ Recitation Section (large)
Hrs/wk 2
CP 2
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Lecturer Prof. Christian Becker
Language DE
Cycle WiSe
Content
  • fundamentals and current development trends in electric power engineering 
  • tasks and history of electric power systems
  • symmetric three-phase systems
  • fundamentals and modelling of eletric power systems 
    • lines
    • transformers
    • synchronous machines
    • induction machines
    • loads and compensation
    • grid structures and substations 
  • fundamentals of energy conversion
    • electro-mechanical energy conversion
    • thermodynamics
    • power station technology
    • renewable energy conversion systems
  • steady-state network calculation
    • network modelling
    • load flow calculation
    • (n-1)-criterion
  • symmetric failure calculations, short-circuit power
  • control in networks and power stations
  • grid protection
  • grid planning
  • power economy fundamentals
Literature

K. Heuck, K.-D. Dettmann, D. Schulz: "Elektrische Energieversorgung", Vieweg + Teubner, 9. Auflage, 2013

A. J. Schwab: "Elektroenergiesysteme", Springer, 5. Auflage, 2017

R. Flosdorff: "Elektrische Energieverteilung" Vieweg + Teubner, 9. Auflage, 2008

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. 
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
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory
Computer Science: Specialisation Computational Mathematics: Elective Compulsory
Data Science: Core qualification: Elective Compulsory
Electrical Engineering: Core qualification: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Electrical Engineering: Compulsory
Computational Science and Engineering: Core qualification: Compulsory
Computational Science and Engineering: Specialisation Engineering Sciences: Elective 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
  • Fundamentals of random processes

  • Introduction to communications engineering

  • Quadrature amplitude modulation

  • Description of radio frequency transmission in the equivalent complex baseband

  • Transmission channels, channel models

  • Analog digital conversion: Sampling, quantization, pulsecode modulation (PCM)

  • Fundamentals of information theory, source coding, channel coding

  • Digital baseband transmission: Pulse shaping, eye diagramm, 1. and 2. Nyquist condition, matched filter, detection, error probability

  • Fundamentals of digital modulation

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 M1615: Introduction to Data Acquisition and Processing

Courses
Title Typ Hrs/wk CP
Data Acquisition and Data Processing (L2445) Project Seminar 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

sound programming skills

basic principles of electrical engineering / physics

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. Data processing from acquisition to regression and classification can be described in context.

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. An actual problem including data acquisition and data processing is solved in 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 6
Course achievement
Compulsory Bonus Form Description
Yes None Presentation
Yes 10 % Excercises
Examination Written exam
Examination duration and scale 90 min
Assignment for the Following Curricula Data Science: Core qualification: Elective Compulsory
Course L2445: Data Acquisition and Data Processing
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 WiSe
Content

Within an actual project setting, relevant tasks in data acquisition and data processing willbe discussed, including

- data acquisition (e.g., image data, sensor data)

- data pre-processing (e.g., filtering)

- data analysis (e.g., solving regressing and classification tasks using machine learning methods)

- evaluation and interpretation of the results

Literature

Wird in der Veranstaltung bekannt gegeben.

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, measuring stochastic signals, describing measurements, acquisition of analog signals, applied metrology

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 M0959: Mechanics III (Dynamics)

Courses
Title Typ Hrs/wk CP
Mechanics III (Dynamics) (L1134) Lecture 3 3
Mechanics III (Dynamics) (L1135) Recitation Section (small) 2 2
Mechanics III (Dynamics) (L1136) Recitation Section (large) 1 1
Module Responsible Prof. Robert Seifried
Admission Requirements None
Recommended Previous Knowledge

Mathematics I, II, Mechanics I (Statics)

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 stereostatics.
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 hydrostatical, kinematic and kinetic methods to engineering problems;
  • estimate the reach and boundaries of statical 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 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
Data Science: Core qualification: Elective Compulsory
Digital Mechanical Engineering: Core qualification: Compulsory
Mechanical Engineering: Core qualification: Compulsory
Mechatronics: Core qualification: Compulsory
Naval Architecture: Core qualification: Compulsory
Technomathematics: Specialisation III. Engineering Science: Elective Compulsory
Course L1134: 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

  • Kinematics of points and relative motion
  • Planar and spatial motion of point systems and rigid bodies 

Dynamics

  • Terms
  • Fundamental equations
  • Motion of the rigid body in 3D-space
  • Dynamics of gyroscopes, rotors
  • Realtive kinetics
  • Systems with non-constant mass


Vibrations


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 L1135: 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
Course L1136: 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

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 NN
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 124, Study Time in Lecture 56
Credit points 6
Course achievement None
Examination Subject theoretical and practical work
Examination duration and scale -
Assignment for the Following Curricula Data Science: Core qualification: 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 NN
Language DE/EN
Cycle WiSe
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 NN
Language DE/EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M1070: Simulation of Transport and Handling Systems

Courses
Title Typ Hrs/wk CP
Simulation of Transport and Handling Systems (L1352) Lecture 1 2
Simulation of Transport and Handling Systems (L1818) Recitation Section (small) 3 4
Module Responsible Prof. Carlos Jahn
Admission Requirements None
Recommended Previous Knowledge

Must have attended (and passed) the lecture on Transport- and Handling-Technology

Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students can…

  • Explain the structure and workings of standard external logistics systems.
  • Outline the benefits of using simulation software subject to the starting situation.
  • Present different simulation programs and kinds of simulation that are in widespread use and explain their characteristics.


Skills

Students are able to…

  • Recognize, analyze, and assemble into a model the elementary building blocks of a logistics system.
  • Map complex external logistics process using the Plant Simulation® simulation software.
  • Draw inferences from the results of the simulation, transfer them to the reality, and deduce action recommendations from them.


Personal Competence
Social Competence

Students are capable of…

  • Solving complex tasks in a team and to document assignments accordingly.
  • Playing different roles in the teamwork and giving each other appropriate feedback in the team.
  • Presenting the relevant results of their project to specialists and representing them.


Autonomy

Students are able…

  • To acquaint themselves independently with software with which they are not familiar and to use it to solve complex tasks.
  • To define work steps independently and to acquire the knowledge required to do so.


Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Course achievement
Compulsory Bonus Form Description
No 20 % Subject theoretical and practical work
Examination Subject theoretical and practical work
Examination duration and scale Simulation study and report with approximately 15 pages per person
Assignment for the Following Curricula Data Science: Core qualification: Elective Compulsory
Logistics and Mobility: Specialisation Logistics and Mobility: Elective Compulsory
Course L1352: Simulation of Transport and Handling Systems
Typ Lecture
Hrs/wk 1
CP 2
Workload in Hours Independent Study Time 46, Study Time in Lecture 14
Lecturer Prof. Carlos Jahn
Language DE
Cycle WiSe
Content

The lecture deals with the simulation of external logistics systems. The focus is therefore on the consideration of logistical processes between companies or on transhipment systems, such as ports or individual terminals.

In the first part of the lecture, students will first acquire basic knowledge of external logistics systems and the advantages of using simulations to present them. Then an overview of existing simulation types and programs is given and examples for existing simulation models of logistic systems in science and practice are shown. Some simulation models will be demonstrated.

In the second part of the lecture the students learn the basic handling of the simulation software Plant Simulation®. They receive theoretical explanations of the general functionality of the simulation tool, which are further deepened through the use of online tutorials. At the same time, three exercises, which build on each other, offer students the opportunity to implement the course content they have learnt in small groups. The exercises can be completed during the supervised lecture periods as well as at other times.

The acquired knowledge is to be applied in the third part in the course of group work. The students will be divided into groups, each of which will then work on a relevant problem from the field of (external) logistic systems by means of simulation. The students are given a defined period of time for their work. During this time at least one person is always available for questions and suggestions. The results of the group work are to be documented in a simulation report and handed in at the end of the processing time. Finally, the individual groups present the problems they have worked on and their results in a presentation.

Literature

Bangsow, Steffen (2011): Praxishandbuch Plant Simulation und SimTalk. Anwendung und Programmierung in über 150 Beispiel-Modellen. München: Hanser Verlag.

Eley, Michael (2012): Simulation in der Logistik. Einführung in die Erstellung ereignisdiskreter Modelle unter Verwendung des Werkzeuges "Plant Simulation". Berlin, Heidelberg: Springer.

Engelhardt-Nowitzki, Corinna; Nowitzki, Olaf; Krenn, Barbara (2008): Management komplexer Materialflüsse mittels Simulation. State-of-the-Art und innovative Konzepte. Wiesbaden: Deutscher Universitäts-Verlag / GWV Fachverlage GmbH, Wiesbaden.

Rabe, Markus; Spieckermann, Sven; Wenzel, Sigrid (2008): Verifikation und Validierung für die Simulation in Produktion und Logistik. Vorgehensmodelle und Techniken. Berlin, Heidelberg: Springer.

Sargent, Robert G. (2010): Verification and Validation of Simulation Models. In: B. Johansson, S. Jain, J. Montoya-Torres, J. Hugan, and E. Yücesan, eds.: Proceedings of the 2010 Winter Simulation Conference.

VDI‐Richlinie: VDI 3633. Simulation von Logistik‐, Materialfluß‐und Produktionssystemen

Wenzel, Sigrid; Rabe, Markus; Spieckermann, Sven (2006): Verifikation und Validierung für die Simulation in Produktion und Logistik. Vorgehensmodelle und Techniken. 1. Aufl. Berlin: Springer Berlin.

Course L1818: Simulation of Transport and Handling Systems
Typ Recitation Section (small)
Hrs/wk 3
CP 4
Workload in Hours Independent Study Time 78, Study Time in Lecture 42
Lecturer Prof. Carlos Jahn
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. Herbert Werner
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
Computer Science: Specialisation Computational Mathematics: Elective Compulsory
Data Science: Core qualification: Elective Compulsory
Electrical Engineering: Core qualification: Compulsory
Energy and Environmental Engineering: Core qualification: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Electrical Engineering: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Civil Engineering: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Bioprocess Engineering: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Energy and Enviromental Engineering: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Computer Science: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Mechanical Engineering, Focus Biomechanics: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Mechanical Engineering, Focus Energy Systems: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Mechanical Engineering, Focus Aircraft Systems Engineering: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Mechanical Engineering, Focus Materials in Engineering Sciences: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Mechanical Engineering, Focus Mechatronics: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Mechanical Engineering, Focus Product Development and Production: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Mechanical Engineering, Focus Theoretical Mechanical Engineering: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Naval Architecture: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Process Engineering: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Biomedical Engineering: Compulsory
Computational Science and Engineering: Core qualification: Compulsory
Logistics and Mobility: Specialisation Engineering Science: 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
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. Herbert Werner
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. Herbert Werner
Language DE
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M1597: Seminars Data Science

Courses
Title Typ Hrs/wk CP
Seminar Data Science I (L2441) Seminar 2 3
Seminar Data Science II (L2442) Seminar 2 3
Module Responsible Prof. Tobias Knopp
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 124, Study Time in Lecture 56
Credit points 6
Course achievement None
Examination Presentation
Examination duration and scale Presentation 20 min and discussion 5 min
Assignment for the Following Curricula Data Science: Core qualification: Compulsory
Course L2441: Seminar Data Science I
Typ Seminar
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Tobias Knopp
Language DE/EN
Cycle WiSe/SoSe
Content
Literature
Course L2442: Seminar Data Science II
Typ Seminar
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Tobias Knopp
Language DE/EN
Cycle WiSe/SoSe
Content
Literature

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 iteration methods,
  • explain aspects regarding the efficient implementation of iteration methods.
Skills

Students are able to

  • 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 Computational Mathematics: Elective Compulsory
Computer Science: Specialisation II. Mathematics and Engineering Science: Elective Compulsory
Data Science: Core qualification: Elective Compulsory
Computational Science and Engineering: Specialisation II. Mathematics & Engineering Science: Elective Compulsory
Computational Science and Engineering: Specialisation Computer 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 DE/EN
Cycle SoSe
Content
  1. Sparse systems: Orderings and storage formats, direct solvers
  2. Classical methods: basic notions, convergence
  3. Projection methods
  4. Krylov space methods
  5. Preconditioning (e.g. ILU)
  6. Multigrid methods
Literature
  1. Y. Saad, Iterative methods for sparse linear systems
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 DE/EN
Cycle SoSe
Content See interlocking course
Literature See interlocking course

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.

Autonomy

The students can reflect their knowledge and document the results of their work. They can present the results in an appropriate manner.

Workload in Hours Independent Study Time 110, Study Time in Lecture 70
Credit points 6
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 Computer and Software Engineering: Elective Compulsory
Computer Science: Specialisation II. Mathematics and Engineering Science: 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
Computational Science and Engineering: Specialisation II. Mathematics & Engineering Science: Elective Compulsory
Computational Science and Engineering: Specialisation Computer Science: Elective Compulsory
Computational Science and Engineering: Specialisation Engineering Sciences: Elective 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

Wird in der Veranstaltung bekannt gegeben.

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

- 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

Wird in der Veranstaltung bekannt gegeben.

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 Prof. Matthias Kuhl
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 Electrical Engineering: Compulsory
General Engineering Science (German program, 7 semester): Specialisation Mechanical Engineering, Focus Mechatronics: Compulsory
Data Science: Core qualification: Elective Compulsory
Electrical Engineering: Core qualification: Compulsory
Engineering Science: Specialisation Electrical Engineering: Compulsory
Engineering Science: Specialisation Mechatronics: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Electrical Engineering: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Mechanical Engineering, Focus Mechatronics: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Mechatronics: Compulsory
Computational Science and Engineering: Specialisation II. Mathematics & Engineering Science: Elective Compulsory
Mechanical Engineering: Specialisation Mechatronics: Compulsory
Mechatronics: Core qualification: 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 Prof. Matthias Kuhl
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 Prof. Matthias Kuhl, 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 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. Karl-Heinz Zimmermann
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

The students known the important machine models of computability, the class of partial recursive functions, universal computability, Gödel numbering of computations, the theorems of Kleene, Rice, and Rice-Shapiro, the concept of decidable and undecidable sets, the word problems for semi-Thue systems, Thue systems, semi-groups, and Post correspondence systems, Hilbert's 10-th problem, and the basic concepts of complexity theory.

Skills

Students are able to investigate the computability of sets and functions and to analyze the complexity of computable functions.

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 newer literature and to associate the acquired knowledge with other classes.

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 60 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: Core qualification: Elective Compulsory
General Engineering Science (English program, 7 semester): Specialisation Computer Science: Elective Compulsory
Computational Science and Engineering: Specialisation I. Computer Science: Elective Compulsory
Technomathematics: Specialisation II. Informatics: Elective Compulsory
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. Karl-Heinz Zimmermann
Language DE/EN
Cycle SoSe
Content
Literature
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. Karl-Heinz Zimmermann
Language DE/EN
Cycle SoSe
Content
Literature

Module M1005: Enhanced Fundamentals of Materials Science

Courses
Title Typ Hrs/wk CP
Enhanced Fundamentals: Ceramics and Polymers (L1233) Lecture 2 2
Enhanced Fundamentals: Ceramics and Polymers (L1234) Recitation Section (large) 1 1
Enhanced Fundamentals: Metals (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 General Engineering Science (German program, 7 semester): Specialisation Mechanical Engineering, Focus Materials in Engineering Sciences: Compulsory
General Engineering Science (German program, 7 semester): Specialisation Mechanical Engineering, Focus Product Development and Production: Compulsory
Data Science: Core qualification: Elective Compulsory
General Engineering Science (English program, 7 semester): Specialisation Mechanical Engineering, Focus Materials in Engineering Sciences: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Mechanical Engineering, Focus Product Development and Production: Compulsory
Mechanical Engineering: Specialisation Materials in Engineering Sciences: Compulsory
Technomathematics: Specialisation III. Engineering Science: Elective Compulsory
Course L1233: Enhanced Fundamentals: Ceramics and Polymers
Typ Lecture
Hrs/wk 2
CP 2
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Lecturer Prof. Gerold Schneider, Prof. Robert Meißner
Language DE/EN
Cycle SoSe
Content

1. Einführung

Natürliche „Keramiken“ - Steine
„Künstliche“ Keramik - vom Porzellan bis zur Hochleistungskeramik  Anwendungen von Hochleistungskeramik

2. Pulverherstellung

Einteilung der Pulversyntheseverfahren
Der Bayer-Prozess zur Al2O3-Herstellung
Der Acheson-Prozess zur SiC-Herstellung
Chemical Vapour Deposition

    Pulveraufbereitung

Mahltechnik
Sprühtrockner

3. Formgebung

Arten der Formgebung
Pressen (0 - 15 % Feuchte)
Gießen (> 25 % Feuchte)
Plastische Formgebung (15 - 25 % Feuchte)

4. Sintern

Triebkraft des Sinterns
Effekt von gekrümmten Oberflächen und Diffusionswegen
Sinterstadien des isothermen Festphasensinterns
Herring scaling laws
Heißisostatisches Pressen

5. Mechanische Eigenschaften von Keramiken

Elastisches und plastisches Materialverhalten
Bruchzähigkeit - Linear-elastische Bruchmechanik
Festigkeit - Festigkeitsstreuung

6. Elektrische Eigenschaften von Keramiken

Ferroelektische Keramiken

Piezo-, ferroelektrische Materialeigenschaften
Anwendungen

Keramische Ionenleiter

Ionische Leitfähigkeit
Dotiertes Zirkonoxid in der Brennstoffzelle und Lambdasonde

Literature

D R H Jones, Michael F. Ashby, Engineering Materials 1, An Introduction to Properties, Applications and Design, Elesevier

D.W. Richerson, Modern Ceramic Engineering, Marcel Decker, New York, 1992

W.D. Kingery, Introduction to Ceramics, John Wiley & Sons, New York, 1975

D.J. Green, An introduction to the mechanical properties of ceramics”, Cambridge University Press, 1998

D. Munz, T. Fett, Ceramics, Springer, 2001


Polymerwerkstoffe
Struktur und mechanische Eigenschaften G.W.Ehrenstein;
Hanser Verlag; ISBN 3-446-12478-0; ca. 20  €

Kunststoffphysik
W.Retting, H.M.Laun; Hanser Verlag; ISBN 3446162356; ca. 25 €

Werkstoffkunde Kunststoffe
G.Menges; Hanser Verlag; ISBN 3-446-15612-7; ca. 25 €

Kunststoff-Kompendium
A.Frank, K. Biederbick; Vogel Buchverlag; ISBN 3-8023-0135-8; ca.30 €

Course L1234: Enhanced Fundamentals: Ceramics and Polymers
Typ Recitation Section (large)
Hrs/wk 1
CP 1
Workload in Hours Independent Study Time 16, Study Time in Lecture 14
Lecturer Prof. Gerold Schneider, Prof. Robert Meißner
Language DE/EN
Cycle SoSe
Content See interlocking course
Literature See interlocking course
Course L1086: Enhanced Fundamentals: Metals
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Jörg Weißmüller, Prof. Patrick Huber
Language DE
Cycle SoSe
Content

Enhanced Fundamentals of Metals:

  • Introduction to phenomenological thermodynamics
  • Elasticity
  • Thermal materials behavior (heat capacity, thermal expansion)
  • Conductors, semiconductors, isolators: conduction mechanisms and band structure
  • Superconductors
  • Dry corrosion
  • Electrochemistry in the material sciences
  • Wet corrosion
  • Alloy corrosion
  • Corrosion protection
  • Stainless steel
  • Battery materials
  • Supercapacitors
  • Fuel cells
  • Materials for hydrogen storage
  • Magnetism: phenomenology, Magnetometers, atomistics, micromagnetism
  • Magnetic materials
  • Magnetic materials: applications
Literature

Vorlesungsskript

 


Specialization Electrical Engineering

Module M0743: Electrical Engineering I: Direct Current Networks and Electromagnetic Fields

Courses
Title Typ Hrs/wk CP
Electrical Engineering I: Direct Current Networks and Electromagnetic Fields (L0675) Lecture 3 5
Electrical Engineering I: Direct Current Networks and Electromagnetic Fields (L0676) Recitation Section (small) 2 1
Module Responsible Prof. Matthias Kuhl
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 110, Study Time in Lecture 70
Credit points 6
Course achievement
Compulsory Bonus Form Description
No 10 % Excercises
Examination Written exam
Examination duration and scale 120 Minutes
Assignment for the Following Curricula General Engineering Science (German program, 7 semester): Core qualification: Compulsory
Data Science: Specialisation Electrical Engineering: Compulsory
Electrical Engineering: Core qualification: Compulsory
Computational Science and Engineering: Core qualification: Compulsory
Mechatronics: Core qualification: Compulsory
Orientierungsstudium: Core qualification: Elective Compulsory
Course L0675: Electrical Engineering I: Direct Current Networks and Electromagnetic Fields
Typ Lecture
Hrs/wk 3
CP 5
Workload in Hours Independent Study Time 108, Study Time in Lecture 42
Lecturer Prof. Matthias Kuhl
Language DE
Cycle WiSe
Content
Literature
  1. M. Kasper, Skript zur Vorlesung Elektrotechnik 1, 2013
  2. M. Albach: Grundlagen der Elektrotechnik 1, Pearson Education, 2004
  3. F. Moeller, H. Frohne, K.H. Löcherer, H. Müller: Grundlagen der Elektrotechnik, Teubner, 2005
  4. A. R. Hambley: Electrical Engineering, Principles and Applications, Pearson Education, 2008
Course L0676: Electrical Engineering I: Direct Current Networks and Electromagnetic Fields
Typ Recitation Section (small)
Hrs/wk 2
CP 1
Workload in Hours Independent Study Time 2, Study Time in Lecture 28
Lecturer Prof. Matthias Kuhl
Language DE
Cycle WiSe
Content
Literature
  1. Übungsaufgaben zur Elektrotechnik 1, TUHH, 2013
  2. Ch. Kautz: Tutorien zur Elektrotechnik, Pearson Studium, 2010

Module M0547: Electrical Engineering II: Alternating Current Networks and Basic Devices

Courses
Title Typ Hrs/wk CP
Electrical Engineering II: Alternating Current Networks and Basic Devices (L0178) Lecture 3 5
Electrical Engineering II: Alternating Current Networks and Basic Devices (L0179) Recitation Section (small) 2 1
Module Responsible Prof. Christian Becker
Admission Requirements None
Recommended Previous Knowledge

Electrical Engineering I

Mathematics I

Direct current networks, complex numbers


Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students are able to reproduce and explain fundamental theories, principles, and methods related to the theory of alternating currents. They can describe networks of linear elements using a complex notation for voltages and currents. They can reproduce an overview of applications for the theory of alternating currents in the area of electrical engineering. Students are capable of explaining the behavior of fundamental passive and active devices as well as their impact on simple circuits.


Skills

Students are capable of calculating parameters within simple electrical networks at alternating currents by means of a complex notation for voltages and currents. They can appraise the fundamental effects that may occur within electrical networks at alternating currents. Students are able to analyze simple circuits such as oscillating circuits, filter, and matching networks quantitatively and dimension elements by means of a design. They can motivate and justify the fundamental elements of an electrical power supply (transformer, transmission line, compensation of reactive power, multiphase system) and are qualified to dimension their main features.


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.


Autonomy

Students are capable to gather necessary information from the references provided and relate that information to the context of the lecture. They are able to continually reflect their knowledge by means of activities that accompany the lecture, such as online-tests 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
Compulsory Bonus Form Description
No 10 % Midterm
Examination Written exam
Examination duration and scale 90 - 150 minutes
Assignment for the Following Curricula General Engineering Science (German program, 7 semester): Core qualification: Compulsory
Data Science: Specialisation Electrical Engineering: Compulsory
Electrical Engineering: Core qualification: Compulsory
Computational Science and Engineering: Core qualification: Compulsory
Mechatronics: Core qualification: Compulsory
Orientierungsstudium: Core qualification: Elective Compulsory
Course L0178: Electrical Engineering II: Alternating Current Networks and Basic Devices
Typ Lecture
Hrs/wk 3
CP 5
Workload in Hours Independent Study Time 108, Study Time in Lecture 42
Lecturer Prof. Christian Becker
Language DE
Cycle SoSe
Content

- General time-dependency of electrical networks

- Representation and properties of harmonic signals

- RLC-elements at alternating currents/voltages

- Complex notation for the representation of RLC-elements

- Power in electrical networks at alternating currents, compensation of reactive power

- Frequency response locus (Nyquist plot) and Bode-diagrams

- Measurement instrumentation for assessing alternating currents

- Oscillating circuits, filters, electrical transmission lines

- Transformers, three-phase current, energy converters

- Simple non-linear and active electrical devices


Literature

- M. Albach, "Elektrotechnik", Pearson Studium (2011)

- T. Harriehausen, D. Schwarzenau, "Moeller Grundlagen der Elektrotechnik", Springer (2013)  

- R. Kories, H. Schmidt-Walter, "Taschenbuch der Elektrotechnik", Harri Deutsch (2010)

- C. Kautz, "Tutorien zur Elektrotechnik", Pearson (2009)

- A. Hambley, "Electrical Engineering: Principles and Applications", Pearson (2013)

- R. Dorf, "The Electrical Engineering Handbook", CRC (2006)


Course L0179: Electrical Engineering II: Alternating Current Networks and Basic Devices
Typ Recitation Section (small)
Hrs/wk 2
CP 1
Workload in Hours Independent Study Time 2, Study Time in Lecture 28
Lecturer Prof. Christian Becker
Language DE
Cycle SoSe
Content

- General time-dependency of electrical networks

- Representation and properties of harmonic signals

- RLC-elements at alternating currents/voltages

- Complex notation for the representation of RLC-elements

- Power in electrical networks at alternating currents, compensation of reactive power

- Frequency response locus (Nyquist plot) and Bode-diagrams

- Measurement instrumentation for assessing alternating currents

- Oscillating circuits, filters, electrical transmission lines

- Transformers, three-phase current, energy converters

- Simple non-linear and active electrical devices


Literature

- M. Albach, "Elektrotechnik", Pearson Studium (2011)

- T. Harriehausen, D. Schwarzenau, "Moeller Grundlagen der Elektrotechnik", Springer (2013)  

- R. Kories, H. Schmidt-Walter, "Taschenbuch der Elektrotechnik", Harri Deutsch (2010)

- C. Kautz, "Tutorien zur Elektrotechnik", Pearson (2009)

- A. Hambley, "Electrical Engineering: Principles and Applications", Pearson (2013)

- R. Dorf, "The Electrical Engineering Handbook", CRC (2006)


Specialization Logistics

Module M1013: Transport- and Handling-Technology

Courses
Title Typ Hrs/wk CP
Transport- and Handling-Technology (L0715) Lecture 2 3
Transport- and Handling-Technology (L0718) Recitation Section (small) 2 3
Module Responsible Prof. Carlos Jahn
Admission Requirements None
Recommended Previous Knowledge none
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students are able to...

- reproduce and discuss the terminology of transport and handling technology according to guidelines and standards (e.g. differences between means of transport and means of conveyance or loading unit and means of transport).

- Determine, compare, select and assign suitable techniques based on the questions:

(1) By which means goods should be transported? (e.g. goods in transit, loading units)

(2) On what should it be transported? (e.g. truck, railway wagon, inland waterway vessel, ocean-going vessel, aircraft)

(3) Where is the cargo to be handled? (e.g., transshipment station, port, airport)

(4) By which means? (e.g. crane, forklift).

Skills

Students can...

- gain access to relevant guidelines and standards and use them (e.g. for unloading technologies in the rail transport of bulk goods),

- Differentiate and evaluate transport and transhipment technologies (e.g. by calculating individual CO2 balances, or transport times and costs for different modes of transport as well as point-to-point or hub-and-spoke freight transport in aviation).


Personal Competence
Social Competence

Students are able to...

- discuss and organize extensive research tasks in small groups (formation of short-term small groups during the lecture and exercise units and within the framework of an extensive written elaboration in the course of the semester),

- describe, differentiate and evaluate problems together (e.g. in the joint compilation of factual knowledge on topics such as slow steaming in container shipping or the development of different maritime supply chains (e.g. containers, RoRo, liquid bulk or project cargo).


Autonomy

Students are able to...

- research and select technical literature, in particular standards and guidelines,

- submit own parts in an extensive written paper in small groups in due time and to present them jointly within a fixed time frame,

- prepare for a field excursion and to interact with partners from the industry, 

- apply acquired knowledge to new questions.


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 minutes
Assignment for the Following Curricula Data Science: Specialisation Logistics: Compulsory
Logistics and Mobility: Core qualification: Compulsory
Course L0715: Transport- and Handling-Technology
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Carlos Jahn
Language DE
Cycle WiSe
Content

The aim of the course is to teach the basics, applications and usefulness of  various transport and handling technologies. Students should be enabled to select, evaluate and dimension suitable techniques for defined transport and handling tasks. In addition to the goods to be transported and the loading units, the various means of transport, transhipment terminals and the necessary equipment play a special role. In addition, it is possible to build up a basic knowledge of the relevant guidelines and standards. In addition, to the transport routes such as road, rail, water (inland navigation and maritime shipping), air, intermodal transport is also discussed.

Literature

Arnold (2008) Handbuch Logistik 3, Springer, Berlin

Buchholz (1998) Handbuch der Verkehrslogistik, Springer, Berlin

Clausen und Geiger (2013) Verkehrs- und Transportlogistik, 2. Auflage, Springer, Berlin (u.a.) DIN 250003, DIN 30781, DIN 30800, DIN 30801, DIN 30802, DIN CENTS 13853, DIN EN 15011, DIN EN 15056, DIN EN 15528, DIN EN 283, DIN EN 284, DIN EN 452, DIN EN ISO 6346, DIN EN ISO 6346A3, DIN ISO 1161, DIN ISO 668

Gleißner, Femerling (2008) Logistik, Gabler, Wiesbaden Kranke, Schmied, Schön (2011) CO2-Berechnung in der Logistik, Verlag Heinrich Vogel, München

Martin (2016) Transport- und Lagerlogistik: Systematik, Planung, Einsatz und Wirtschaftlichkeit, Springer, Berlin 

(u.a.) VDI 2360, VDI 2518, VDI 3302, VDI 3586

Course L0718: Transport- and Handling-Technology
Typ Recitation Section (small)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Carlos Jahn
Language DE
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M1004: Logistics Management

Courses
Title Typ Hrs/wk CP
Introduction into Production Logistics (L1222) Lecture 2 2
Logistics Economics (L1221) Project-/problem-based Learning 2 4
Module Responsible Prof. Wolfgang Kersten
Admission Requirements None
Recommended Previous Knowledge

Introduction to Business and Management


Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

 Students will be able

  • to differentiate between production logistics and logistics services,
  • to describe internal and external areas of production and logistics management,
  • understand the difference between the different roles in a supply chain,
  • to describe and explain the actual challenges of production and Logistics management


Skills

Based on the acquired knowledge students are capable of

  • Analysing logistics problems and influence factors in companies,
  • Selecting appropriate methods for solving practical problems,
  • Applying methods and tools of logistics management for standardized problems.


Personal Competence
Social Competence

Students can

  • actively participate in discussions and team sessions,
  • arrive at work results in groups and document them,
  • develop joint solutions in mixed teams and present them to others.


Autonomy

Students are able to
- perform work steps for solving problems of business logistics independently with the aid of pointers

- assess their own state of learning in specific terms and to define further work steps on this basis guided by teachers.

Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Course achievement
Compulsory Bonus Form Description
No 20 % Subject theoretical and practical work
Examination Written exam
Examination duration and scale 120 min
Assignment for the Following Curricula Data Science: Specialisation Logistics: Compulsory
Logistics and Mobility: Core qualification: Compulsory
Orientierungsstudium: Core qualification: Elective Compulsory
Course L1222: Introduction into Production Logistics
Typ Lecture
Hrs/wk 2
CP 2
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Lecturer Dr. Yong Lee
Language DE
Cycle SoSe
Content In the era of time-competition production and logistics need to be considered as a combined strategic competitive advantage.

"Introduction in to production logistics" gives an overview over the different disciplinces of production logistics:

- Development from cost-, quality to time-competitiion,
- fundamentals of production and logistics,
- phase-oriented and functional subsystems of production logistics,
- planning and steering,
- analysis and optimization (focus: Lean Management),
- production logistics controlling and supply-chain management in production network

Theory is complented by case studies and guest presentations.
Literature
  • Der Vorlesung zugrunde liegende Literatur (Auswahl):

    - Beer, Stafford (1988): Diagnosing the system for organizations. John Wiley & Sons. Chichester, New York, Brisbane, Toronto 1988.
    - Ferdows, Kasra; De Meyer, Arnoud (1990): Lasting Improvements in Manufacturing Performance   In Search of a New Theory. In: Journal of Operations Management, Vol. 9 (2), 1990, S. 365-384.
    - Gudehus, Timm (2010): Logistik. Grundlagen - Strategien - Anwendungen. 4. aktual. Aufl. Springer Verlag. Heidelberg/Berlin 2010.
    - Günther, Hans-Otto/Tempelmeier, Horst (2012): Produktion und Logistik. 9., akt. u. erw. Aufl. Springer Verlag. Berlin/Heidelberg 2012.
    - Hayes, Robert H.; Schmenner, Roger (1978): How Should You Organize Ma-nufacturing?. In: Harvard Business Review, Vol. 56 (1), 1978, S. 105-118.
    - Krafcik, John F. (1988): Triumph of the lean production system. In: Sloan Management Review, Vol. 30 (1), S. 41-52.
    - Maskell, Brian H. (1989a): Performance Measurement for World Class Manu­facturing. Part I. Manufacturing Systems, Vol. 7, 1989, S. 62-64.
    - Pawellek, Günther (2007): Produktionslogistik - Planung - Steuerung - Controlling. Carl Hanser Verlag. München 2007.
    - Nyhuis, Peter (2008): Beiträge zu einer Theorie der Logistik. Springer Verlag. Berlin/Heidelberg 2008.
    - Pfohl, Hans-Christian (2010): Logistiksysteme. Betriebswirtschaftliche Grundlagen. 8., neu bearb. u. aktual. Aufl. Springer Verlag. Berlin/Heidelberg 2010.
    - Schuh, Günther (1988): Gestaltung und Bewertung von Produktvarianten. Ein Beitrag zur systematischen Planung von Serienprodukten. Dissertation. RWTH Aachen 1988.
    - Takeda, Hitoshi (2012): Das synchrone Produktionssystem. Just-in-time für das ganze Unternehmen. 7. Aufl. Verlag Franz Vahlen. München 2012.
    - Ten Hompel, Michael/Sadowsky, Volker/Beck, Maria (2011): Kommissionierung. Materialflusssysteme 2 - Planung und Berechnung der Kommissionierung in der  Logistik. Springer Verlag. Berlin/Heidelberg 2011.
    - Wannenwetsch, Helmut (2007): Integrierte Materialwirtschaft und Logistik. Beschaffung, Logistik, Materialwirtschaft und Produktion.3., akt. Aufl. Springer Verlag. Berlin/Heidelberg 2007.
    - Wiendahl, Hans-Peter/Reichardt, Jürgen/Nyhuis, Peter (2014): Handbuch Fabrikplanung. Konzept, Gestaltung und Umsetzung wandlungsfähiger Produktionsstätten. 2., überarb. u. erw. Aufl. Carl Hanser Verlag. München/Wien 2014.
    - Wildemann, Horst (1997): Fertigungsstrategien - Reorganisation für eine schlanke Produktion und Zulieferung. 3. Aufl. TCW Transfer-Centrum-Verlag. München 1997.
    - Wildemann, Horst (2008): Produktionssysteme. Leitfaden zur methoden-gestützten Reorganisation der Produktion. 6. Aufl. 2008, TCW München.
    - Wildemann, Horst (2009): Logistik Prozeßmanagement. 4. Aufl. TCW Transfer-Centrum-Verlag. München 2009.
    - Zäpfel, Günther (2001): Grundzüge des Produktions- und Logistikmanagement. 2., unwesentlich veränd. Aufl. R. Oldenbourg Verlag. München/Wien 2001.
Course L1221: Logistics Economics
Typ Project-/problem-based Learning
Hrs/wk 2
CP 4
Workload in Hours Independent Study Time 92, Study Time in Lecture 28
Lecturer Dr. Meike Schröder, Dr. Meike Schröder
Language DE
Cycle SoSe
Content
  • Explanation of basic concepts of logistics and outline of the scope of the logistics business, identification of global logistics networks and relationships
  • Stakeholder: Introduction to the different kinds of logistics service providers, characterization of services of consulting firms for logistics companies
  • Strategy: Influence of the business strategies on business logistics
  • Outsourcing: Decision processes, possibilities and risks of outsourcing of logistics services
  • Market: Logistics in Germany, relevance of logistics for the city of Hamburg
  • Research: Outlook on current issues in academic research, as well as an outline of supplementary management methods for logistics


Literature
  • Arnold, D.; Isermann, H.; Kuhn, A.; Tempelmeier, H. (2008): Handbuch Logistik, Berlin: Springer, 2008, ISBN: 3-540-72928-3
  • Ballou, R. H. (2004): Business logistics, supply chain management: planning, organizing, and controlling the supply chain, 5. ed., internat. ed., Upper Saddle River, NJ: Pearson Prentice Hall, 2004, ISBN: 0-13-123010-7
  • Bretzke, W.-R. (2008): Logistische Netzwerke, Springer, Berlin, 2008
  • Gleißner, H.; Femerling, C. (2008): Logistik - Grundlagen, Übungen, Fallbeispiele, Wiesbaden: Gabler, 2008, ISBN: 978-3-8349-0296-2
  • Kersten, W.; Hohrath, P.; Koch, J. (2007): Innovative logistics services : Advantage and Disadvantages of Outsourcing Complex Service Bundles, in: Key Factors for Successful Logistics, Berlin: Erich Schmidt Verlag GmbH & Co. KG, 2007
  • Kersten, W.; Koch, J. (2007): Motive für das Outsourcing komplexer Logistikdienstleistungen, in: Handbuch Kontraktlogistik : Management komplexer Logistikdienstleistungen, Weinheim
  • Schulte, C. (2009): Logistik: Wege zur Optimierung der Supply Chain, 5. überarb. und erw. Aufl., München: Vahlen, 2009, ISBN: 3-8006-3516-X
  • Wildemann, H. (1997): Logistik Prozessmanagement - Organisation und Methoden, München: TCW Transfer‐Centrum Verlag, 1997, ISBN: 3‐931511‐17‐0


Specialization Materials Science

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 Energy and Enviromental Engineering: Compulsory
General Engineering Science (German program, 7 semester): Specialisation Naval Architecture: Compulsory
General Engineering Science (German program, 7 semester): Specialisation Naval Architecture: Compulsory
Data Science: Specialisation Materials Science: Compulsory
Digital Mechanical Engineering: Core qualification: Compulsory
Energy and Environmental Engineering: Core qualification: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Energy and Enviromental Engineering: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Mechanical Engineering: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Naval Architecture: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Biomedical Engineering: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Naval Architecture: Compulsory
Logistics and Mobility: Specialisation Engineering Science: Elective Compulsory
Mechanical Engineering: Core qualification: Compulsory
Mechatronics: Core qualification: Compulsory
Naval Architecture: Core qualification: Compulsory
Technomathematics: Specialisation III. Engineering Science: 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 SoSe
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 Prof. Stefan Müller
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 M0934: Advanced Materials

Courses
Title Typ Hrs/wk CP
Advanced Materials Characterization (L1087) Lecture 2 2
Advanced Materials Design (L1091) Lecture 2 2
Advanced Materials Design (L1092) Recitation Section (large) 2 2
Module Responsible Prof. Patrick Huber
Admission Requirements None
Recommended Previous Knowledge Fundamentals of Materials Science (I and II)
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

The students will be able to explain the properties of advanced materials along with their applications in technology, in particular metallic, ceramic, polymeric, semiconductor, modern composite materials (biomaterials) and nanomaterials.

Skills

The students will be able to select material configurations according to the technical needs and, if necessary, to design new materials considering architectural principles from the micro- to the macroscale. The students will also gain an overview on modern materials science, which enables them to select optimum materials combinations depending on the technical applications.

Personal Competence
Social Competence

The students are able to present solutions to specialists and to develop ideas further.


Autonomy

The students are able to ...

  • assess their own strengths and weaknesses.
  • define tasks independently.


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): Specialisation Mechanical Engineering: Elective Compulsory
General Engineering Science (German program, 7 semester): Specialisation Mechanical Engineering, Focus Biomechanics: Compulsory
General Engineering Science (German program, 7 semester): Specialisation Mechanical Engineering, Focus Materials in Engineering Sciences: Compulsory
Data Science: Specialisation Materials Science: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Mechanical Engineering: Elective Compulsory
Mechanical Engineering: Core qualification: Elective Compulsory
Course L1087: Advanced Materials Characterization
Typ Lecture
Hrs/wk 2
CP 2
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Lecturer Prof. Patrick Huber
Language DE
Cycle SoSe
Content
Literature

William D. Callister und David G. Rethwisch, Materialwissenschaften und Werkstofftechnik, Wiley&Sons, Asia (2011).

William D. Callister, Materials Science and Technology, Wiley& Sons, Inc. (2007).



Course L1091: Advanced Materials Design
Typ Lecture
Hrs/wk 2
CP 2
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Lecturer Prof. Bodo Fiedler, Prof. Stefan Müller, Prof. Patrick Huber, Prof. Gerold Schneider, Prof. Jörg Weißmüller
Language DE/EN
Cycle SoSe
Content


Literature Vorlesungsunterlagen
Course L1092: Advanced Materials Design
Typ Recitation Section (large)
Hrs/wk 2
CP 2
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Lecturer Prof. Bodo Fiedler, Prof. Stefan Müller, Prof. Patrick Huber, Prof. Gerold Schneider, Prof. Jörg Weißmüller
Language DE/EN
Cycle SoSe
Content See interlocking course
Literature See interlocking course

Specialization Mechanics

Module M0889: Mechanics I (Statics)

Courses
Title Typ Hrs/wk CP
Mechanics I (Statics) (L1001) Lecture 2 3
Mechanics I (Statics) (L1002) Recitation Section (small) 2 2
Mechanics I (Statics) (L1003) Recitation Section (large) 1 1
Module Responsible Prof. Robert Seifried
Admission Requirements None
Recommended Previous Knowledge

Solid school knowledge in mathematics and physics.

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 stereostatics.
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 statical methods to engineering problems;
  • estimate the reach and boundaries of statical 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 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
Civil- and Environmental Engineering: Core qualification: Compulsory
Data Science: Specialisation Mechanics: Compulsory
Digital Mechanical Engineering: Core qualification: Compulsory
Logistics and Mobility: Core qualification: Compulsory
Mechanical Engineering: Core qualification: Compulsory
Mechatronics: Core qualification: Compulsory
Orientierungsstudium: Core qualification: Elective Compulsory
Naval Architecture: Core qualification: Compulsory
Course L1001: Mechanics I (Statics)
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Robert Seifried
Language DE
Cycle WiSe
Content
  • Tasks in Mechanics
  • Modelling and model elements
  • Vector calculus for forces and torques
  • Forces and equilibrium in space
  • Constraints and reactions, characterization of constraint systems
  • Planar and spatial truss structures
  • Internal forces and moments for beams and frames
  • Center of mass, volumn, area and line
  • Computation of center of mass by intergals, joint bodies
  • Friction (sliding and sticking)
  • Friction of ropes
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. 11. Auflage, Springer (2011).
Course L1002: Mechanics I (Statics)
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 Forces and equilibrium
Constraints and reactions
Frames
Center of mass
Friction
Internal forces and moments for beams
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. 11. Auflage, Springer (2011).
Course L1003: Mechanics I (Statics)
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 Forces and equilibrium
Constraints and reactions
Frames
Center of mass
Friction
Internal forces and moments for beams
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. 11. Auflage, Springer (2011).

Module M0696: Mechanics II: Mechanics of Materials

Courses
Title Typ Hrs/wk CP
Mechanics II (L0493) Lecture 2 2
Mechanics II (L0494) Recitation Section (small) 2 2
Mechanics II (L1691) Recitation Section (large) 2 2
Module Responsible Prof. Christian Cyron
Admission Requirements None
Recommended Previous Knowledge Mechanics I
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge The students name the fundamental concepts and laws of statics such as stresses, strains, Hooke's linear law.
Skills

The students apply the mathematical/mechanical analysis and modeling.

The students apply the fundamental methods of elasto statics to simply engineering problems.

The students estimate the validity and limitations of the introduced methods.


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 90 min
Assignment for the Following Curricula General Engineering Science (German program, 7 semester): Core qualification: Compulsory
Civil- and Environmental Engineering: Core qualification: Compulsory
Data Science: Specialisation Mechanics: Compulsory
Digital Mechanical Engineering: Core qualification: Compulsory
Logistics and Mobility: Core qualification: Compulsory
Mechanical Engineering: Core qualification: Compulsory
Mechatronics: Core qualification: Compulsory
Orientierungsstudium: Core qualification: Elective Compulsory
Naval Architecture: Core qualification: Compulsory
Course L0493: Mechanics II
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

stresses and strains
Hooke's law
tension and compression
torsion
bending
stability
buckling
energy methods

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 L0494: Mechanics II
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
Course L1691: Mechanics II
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, Dr. Konrad Schneider
Language DE
Cycle SoSe
Content See interlocking course
Literature See interlocking course

Specialization Medicine

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.

Autonomy

The students can develop understanding of topics from the course, 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
Data Science: Specialisation Medicine: 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
General Engineering Science (English program, 7 semester): Specialisation Mechanical Engineering, Focus Biomechanics: Compulsory
Mechanical Engineering: Specialisation Biomechanics: 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 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 None
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge The students can describe basal structures and functions of internal organs and the musculoskeletal system.

The students can describe the basic macroscopy and microscopy of those systems.

Skills

The students can recognize the relationship between given anatomical facts and the development of some common diseases; they can explain the relevance of structures and their functions in the context of widespread diseases.

Personal Competence
Social Competence

The students can participate in current discussions in biomedical research and medicine on a professional level.

Autonomy

The students are able to access anatomical knowledge by themselves, can participate 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 Medicine: Compulsory
Electrical Engineering: Specialisation Medical Technology: Elective Compulsory
Engineering Science: Specialisation Biomedical Engineering: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Mechanical Engineering, Focus Biomechanics: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Biomedical Engineering: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Biomedical Engineering: Compulsory
Mechanical Engineering: Specialisation Biomechanics: 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 Prof. Tobias Lange
Language DE
Cycle SoSe
Content

General Anatomy

1st week:             The Eucaryote Cell

2nd week:             The Tissues

3rd week:             Cell Cycle, Basics in Development

4th week:             Musculoskeletal System

5th week:             Cardiovascular System

6th week:             Respiratory System   

7th week:             Genito-urinary System

8th week:             Immune system

9th week:             Digestive System I

10th week:           Digestive System II

11th week:           Endocrine System

12th week:           Nervous System

13th week:           Exam



Literature

Adolf Faller/Michael Schünke, Der Körper des Menschen, 17. Auflage, Thieme Verlag Stuttgart, 2016

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 Medicine: Compulsory
Electrical Engineering: Specialisation Medical Technology: Elective Compulsory
Engineering Science: Specialisation Biomedical Engineering: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Mechanical Engineering, Focus Biomechanics: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Biomedical Engineering: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Biomedical Engineering: Compulsory
Mechanical Engineering: Specialisation Biomechanics: 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 Prof. Ulrich Carl, Prof. Thomas Vestring
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 M1280: MED II: Introduction to Physiology

Courses
Title Typ Hrs/wk CP
Introduction to Physiology (L0385) Lecture 2 3
Module Responsible Dr. Roger Zimmermann
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
Data Science: Specialisation Medicine: Compulsory
Electrical Engineering: Specialisation Medical Technology: Elective Compulsory
Engineering Science: Specialisation Biomedical Engineering: Elective Compulsory
General Engineering Science (English program, 7 semester): Specialisation Mechanical Engineering, Focus Biomechanics: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Biomedical Engineering: Compulsory
General Engineering Science (English program, 7 semester): Specialisation Biomedical Engineering: Elective Compulsory
Mechanical Engineering: Specialisation Biomechanics: 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, 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

Supplement Modules

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 6
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: Core qualification: Elective Compulsory
Data Science: Technical Complementary Course: Elective Compulsory
Engineering Science: Specialisation Mechatronics: Elective Compulsory
General Engineering Science (English program, 7 semester): Specialisation Computer Science: Elective Compulsory
General Engineering Science (English program, 7 semester): Specialisation Mechatronics: Elective Compulsory
Computational Science and Engineering: Specialisation I. Computer Science: Elective Compulsory
Computational Science and Engineering: Specialisation 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.

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.

Thesis

Module M-001: Bachelor Thesis

Courses
Title Typ Hrs/wk CP
Module Responsible Professoren der TUHH
Admission Requirements
  • According to General Regulations §21 (1):

    At least 126 ECTS credit points have to be achieved in study programme. The examinations board decides on exceptions.

Recommended Previous Knowledge
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge
  • The students can select, outline and, if need be, critically discuss the most important scientific fundamentals of their course of study (facts, theories, and methods).
  • On the basis of their fundamental knowledge of their subject the students are capable in relation to a specific issue of opening up and establishing links with extended specialized expertise.
  • The students are able to outline the state of research on a selected issue in their subject area.
Skills
  • The students can make targeted use of the basic knowledge of their subject that they have acquired in their studies to solve subject-related problems.
  • With the aid of the methods they have learnt during their studies the students can analyze problems, make decisions on technical issues, and develop solutions.
  • The students can take up a critical position on the findings of their own research work from a specialized perspective.


Personal Competence
Social Competence
  • Both in writing and orally the students can outline a scientific issue for an expert audience accurately, understandably and in a structured way.
  • The students can deal with issues in an expert discussion and answer them in a manner that is appropriate to the addressees. In doing so they can uphold their own assessments and viewpoints convincingly.


Autonomy
  • The students are capable of structuring an extensive work process in terms of time and of dealing with an issue within a specified time frame.
  • The students are able to identify, open up, and connect knowledge and material necessary for working on a scientific problem.
  • The students can apply the essential techniques of scientific work to research of their own.
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
Bioprocess Engineering: Thesis: Compulsory
Computer Science: Thesis: Compulsory
Data Science: Thesis: Compulsory
Digital Mechanical Engineering: Thesis: Compulsory
Electrical Engineering: Thesis: Compulsory
Energy and Environmental Engineering: Thesis: Compulsory
Engineering Science: Thesis: Compulsory
General Engineering Science (English program, 7 semester): Thesis: Compulsory
Computational Science and Engineering: Thesis: Compulsory
Logistics and Mobility: Thesis: Compulsory
Mechanical Engineering: Thesis: Compulsory
Mechatronics: Thesis: Compulsory
Naval Architecture: Thesis: Compulsory
Technomathematics: Thesis: Compulsory
Teilstudiengang Lehramt Elektrotechnik-Informationstechnik: Thesis: Compulsory
Teilstudiengang Lehramt Metalltechnik: Thesis: Compulsory
Process Engineering: Thesis: Compulsory