Program description

Content

Core Qualification

Module M0523: Business & Management

Module Responsible Prof. Matthias Meyer
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 find their way around selected special areas of management within the scope of business management.
  • Students are able to explain basic theories, categories, and models in selected special areas of business management.
  • Students are able to interrelate technical and management knowledge.


Skills
  • Students are able to apply basic methods in selected areas of business management.
  • Students are able to explain and give reasons for decision proposals on practical issues in areas of business management.


Personal Competence
Social Competence
  • Students are able to communicate in small interdisciplinary groups and to jointly develop solutions for complex problems

Autonomy
  • Students are capable of acquiring necessary knowledge independently by means of research and preparation of material.


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 M0524: Non-technical Courses for Master

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 Nontechnical 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, communication studies, migration 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

  • explain specialized areas in context of the relevant non-technical disciplines,
  • 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 and specific methods of the said scientific disciplines,
  • aquestion a specific technical phenomena, models, theories from the viewpoint of another, aforementioned specialist discipline,
  • to handle simple and advanced 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 M1563: Research Project Computer Science

Courses
Title Typ Hrs/wk CP
Research Project Computer Science (L2353) Projection Course 8 12
Module Responsible Prof. Karl-Heinz Zimmermann
Admission Requirements None
Recommended Previous Knowledge

Basic knowledge and techniques from the Master courses in the semesters 1 and 2.

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

Students are able to acquire advanced knowledge in a subfield of Computer Science and can independently acquire deeper knowledge in the field.

Skills

The students are able to formulate the scientific problems to be considered and to work out solutions in an independent manner and to realize them.


Personal Competence
Social Competence

The students are able to discuss proposals for solutions of scientific problems within the team. They are able to present the results in a clear and well structured manner. 

Autonomy

The students can provide a scientific work in a timely manner and document the results in a detailed and well readable form. They are able to actively follow anticipate the presentations of other students such that eventually a scientific discussion comes up.

Workload in Hours Independent Study Time 248, Study Time in Lecture 112
Credit points 12
Course achievement None
Examination Study work
Examination duration and scale Vortrag
Assignment for the Following Curricula Computer Science: Core Qualification: Compulsory
Course L2353: Research Project Computer Science
Typ Projection Course
Hrs/wk 8
CP 12
Workload in Hours Independent Study Time 248, Study Time in Lecture 112
Lecturer Prof. Karl-Heinz Zimmermann
Language DE/EN
Cycle WiSe
Content
Literature

Specialization I. Computer and Software Engineering

Module M0753: Software Verification

Courses
Title Typ Hrs/wk CP
Software Verification (L0629) Lecture 2 3
Software Verification (L0630) Recitation Section (small) 2 3
Module Responsible Prof. Sibylle Schupp
Admission Requirements None
Recommended Previous Knowledge
  • Automata theory and formal languages
  • Computational logic
  • Object-oriented programming, algorithms, and data structures
  • Functional programming or procedural programming
  • Concurrency
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students apply the major verification techniques in model checking and deductive verification. They explain in formal terms syntax and semantics of the underlying logics, and assess the expressivity of different logics as well as their limitations. They classify formal properties of software systems. They find flaws in formal arguments, arising from modeling artifacts or underspecification. 

Skills

Students formulate provable properties of a software system in a formal language. They develop logic-based models that properly abstract from the software under verification and, where necessary, adapt model or property. They construct proofs and property checks by hand or using tools for model checking or deductive verification, and reflect on the scope of the results. Presented with a verification problem in natural language, they select the appropriate verification technique and justify their choice.   

Personal Competence
Social Competence

Students discuss relevant topics in class. They defend their solutions orally. They communicate in English. 

Autonomy

Using accompanying on-line material for self study, students can assess their level of knowledge continuously and adjust it appropriately.  Working on exercise problems, they receive additional feedback. Within limits, they can set their own learning goals. Upon successful completion, students can identify and precisely formulate new problems in academic or applied research in the field of software verification. Within this field, they can conduct independent studies to acquire the necessary competencies and compile their findings in academic reports. They can devise plans to arrive at new solutions or assess existing ones. 

Workload in Hours Independent Study Time 124, Study Time in Lecture 56
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 Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory
Computational Science and Engineering: Specialisation I. Computer Science: Elective Compulsory
Information and Communication Systems: Specialisation Communication Systems, Focus Software: Elective Compulsory
Information and Communication Systems: Specialisation Secure and Dependable IT Systems: Compulsory
International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory
Course L0629: Software Verification
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Sibylle Schupp
Language EN
Cycle WiSe
Content
  • Syntax and semantics of logic-based systems
  • Deductive verification
    • Specification
    • Proof obligations
    • Program properties
    • Automated vs. interactive theorem proving
  • Model checking
    • Foundations
    • Property languages
    • Tool support
  • Timed automata
  • Recent developments of verification techniques and applications
Literature
  • C. Baier and J-P. Katoen, Principles of Model Checking, MIT Press 2007.
  • M. Huth and M. Bryan, Logic in Computer Science. Modelling and Reasoning about Systems, 2nd Edition, 2004.
  • Selected Research Papers
Course L0630: Software Verification
Typ Recitation Section (small)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Sibylle Schupp
Language EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M0926: Distributed Algorithms

Courses
Title Typ Hrs/wk CP
Distributed Algorithms (L1071) Lecture 2 3
Distributed Algorithms (L1072) Recitation Section (large) 2 3
Module Responsible Prof. Volker Turau
Admission Requirements None
Recommended Previous Knowledge
  • Algorithms and data structures
  • Distributed systems
  • Discrete mathematics
  • Graph theory
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge Students know the main abstractions of distributed algorithms (synchronous/asynchronous model, message passing and shared memory model). They are able to describe complexity measures for distributed algorithms (round , message and memory complexity). They explain well known distributed algorithms for important problems such as leader election, mutual exclusion, graph coloring, spanning trees. They know the fundamental techniques used for randomized algorithms.
Skills Students design their own distributed algorithms and analyze their complexity. They make use of known standard algorithms. They compute the complexity of randomized algorithms.
Personal Competence
Social Competence
Autonomy
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Course achievement None
Examination Oral exam
Examination duration and scale 45 min
Assignment for the Following Curricula Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory
Computational Science and Engineering: Specialisation I. Computer Science: Elective Compulsory
Course L1071: Distributed Algorithms
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Volker Turau
Language DE/EN
Cycle WiSe
Content
  • Leader Election
  • Colorings & Independent Sets
  • Tree Algorithms
  • Minimal Spanning Trees
  • Randomized Distributed Algorithms
  • Mutual Exclusion
Literature
  1. David Peleg: Distributed Computing - A Locality-Sensitive Approach. SIAM Monograph, 2000

  2. Gerard Tel: Introduction to Distributed Algorithms, Cambridge University Press, 2nd edition, 2000
  3. Nancy Lynch: Distributed Algorithms. Morgan Kaufmann, 1996
  4. Volker Turau: Algorithmische Graphentheorie. Oldenbourg Wissenschaftsverlag, 3. Auflage, 2004.


Course L1072: Distributed Algorithms
Typ Recitation Section (large)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Volker Turau
Language DE/EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M0942: Software Security

Courses
Title Typ Hrs/wk CP
Software Security (L1103) Lecture 2 3
Software Security (L1104) Recitation Section (small) 2 3
Module Responsible Prof. Dieter Gollmann
Admission Requirements None
Recommended Previous Knowledge Familiarity with C/C++, web programming
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students can 

  • name the main causes for security vulnerabilities in software 
  • explain current methods for identifying and avoiding security vulnerabilities 
  • explain the fundamental concepts of code-based access control 
Skills

Students are capable of 

  • performing a software vulnerability analysis 
  • developing secure code 
Personal Competence
Social Competence None
Autonomy Students are capable of acquiring knowledge independently from professional publications, technical  standards, and other sources, and are capable of applying newly acquired knowledge to new problems. 
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Course achievement None
Examination Written exam
Examination duration and scale 120 minutes
Assignment for the Following Curricula Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory
Computational Science and Engineering: Specialisation I. Computer Science: Elective Compulsory
Information and Communication Systems: Specialisation Secure and Dependable IT Systems: Elective Compulsory
Course L1103: Software Security
Typ Lecture
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
  • Reliabilty and Software Security
  • Attacks exploiting character and integer representations
  • Buffer overruns
  • Vulnerabilities in memory managemet: double free attacks
  • Race conditions
  • SQL injection
  • Cross-site scripting and cross-site request forgery
  • Testing for security; taint analysis
  • Type safe languages
  • Development proceses for secure software
  • Code-based access control


Literature

M. Howard, D. LeBlanc: Writing Secure Code, 2nd edition, Microsoft Press (2002)

G. Hoglund, G. McGraw: Exploiting Software, Addison-Wesley (2004)

L. Gong, G. Ellison, M. Dageforde: Inside Java 2 Platform Security, 2nd edition, Addison-Wesley (2003)

B. LaMacchia, S. Lange, M. Lyons, R. Martin, K. T. Price: .NET Framework Security, Addison-Wesley Professional (2002)

D. Gollmann: Computer Security, 3rd edition (2011)


Course L1104: Software 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 M1400: Design of Dependable Systems

Courses
Title Typ Hrs/wk CP
Designing Dependable Systems (L2000) Lecture 2 3
Designing Dependable Systems (L2001) Recitation Section (small) 2 3
Module Responsible Prof. Görschwin Fey
Admission Requirements None
Recommended Previous Knowledge Basic knowledge about data structures and algorithms
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

In the following "dependable" summarizes the concepts Reliability, Availability, Maintainability, Safety and Security.

Knowledge about approaches for designing dependable systems, e.g.,

  • Structural solutions like modular redundancy
  • Algorithmic solutions like handling byzantine faults or checkpointing

Knowledge about methods for the analysis of dependable systems


Skills

Ability to implement dependable systems using the above approaches.

Ability to analyzs the dependability of systems using the above methods for analysis.

Personal Competence
Social Competence

Students

  • discuss relevant topics in class and
  • present their solutions orally.
Autonomy Using accompanying material students independently learn in-depth relations between concepts explained in the lecture and additional solution strategies.
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Course achievement
Compulsory Bonus Form Description
Yes None Subject theoretical and practical work Die Lösung einer Aufgabe ist Zuslassungsvoraussetzung für die Prüfung. Die Aufgabe wird in Vorlesung und Übung definiert.
Examination Oral exam
Examination duration and scale 30 min
Assignment for the Following Curricula Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory
Computational Science and Engineering: Specialisation I. Computer Science: Elective Compulsory
Information and Communication Systems: Specialisation Secure and Dependable IT Systems: Elective Compulsory
Mechatronics: Specialisation System Design: Elective Compulsory
Microelectronics and Microsystems: Specialisation Embedded Systems: Elective Compulsory
Course L2000: Designing Dependable Systems
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Görschwin Fey
Language DE/EN
Cycle SoSe
Content

Description

The term dependability comprises various aspects of a system. These are typically:
  • Reliability
  • Availability
  • Maintainability
  • Safety
  • Security
This makes dependability a core aspect that has to be considered early in system design, no matter whether software, embedded systems or full scale cyber-physical systems are considered.

Contents

The module introduces the basic concepts for the design and the analysis of dependable systems. Design examples for getting practical hands-on-experience in dependable design techniques. The module focuses towards embedded systems. The following topics are covered:
  • Modelling
  • Fault Tolerance
  • Design Concepts
  • Analysis Techniques
Literature
Course L2001: Designing Dependable Systems
Typ Recitation Section (small)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Görschwin Fey
Language DE/EN
Cycle SoSe
Content See interlocking course
Literature See interlocking course

Module M1248: Compilers for Embedded Systems

Courses
Title Typ Hrs/wk CP
Compilers for Embedded Systems (L1692) Lecture 3 4
Compilers for Embedded Systems (L1693) Project-/problem-based Learning 1 2
Module Responsible Prof. Heiko Falk
Admission Requirements None
Recommended Previous Knowledge

Module "Embedded Systems"

C/C++ Programming skills

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

The relevance of embedded systems increases from year to year. Within such systems, the amount of software to be executed on embedded processors grows continuously due to its lower costs and higher flexibility. Because of the particular application areas of embedded systems, highly optimized and application-specific processors are deployed. Such highly specialized processors impose high demands on compilers which have to generate code of highest quality. After the successful attendance of this course, the students are able

  • to illustrate the structure and organization of such compilers,
  • to distinguish and explain intermediate representations of various abstraction levels, and
  • to assess optimizations and their underlying problems in all compiler phases.

The high demands on compilers for embedded systems make effective code optimizations mandatory. The students learn in particular,

  • which kinds of optimizations are applicable at the source code level,
  • how the translation from source code to assembly code is performed,
  • which kinds of optimizations are applicable at the assembly code level,
  • how register allocation is performed, and
  • how memory hierarchies can be exploited effectively.

Since compilers for embedded systems often have to optimize for multiple objectives (e.g., average- or worst-case execution time, energy dissipation, code size), the students learn to evaluate the influence of optimizations on these different criteria.

Skills

After successful completion of the course, students shall be able to translate high-level program code into machine code. They will be enabled to assess which kind of code optimization should be applied most effectively at which abstraction level (e.g., source or assembly code) within a compiler.

While attending the labs, the students will learn to implement a fully functional compiler including optimizations.

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 None
Examination Oral exam
Examination duration and scale 30 min
Assignment for the Following Curricula Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory
Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory
Aircraft Systems Engineering: Specialisation Avionic Systems: Elective Compulsory
Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory
Mechatronics: Specialisation System Design: Elective Compulsory
Mechatronics: Technical Complementary Course: Elective Compulsory
Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: Elective Compulsory
Course L1692: Compilers for Embedded Systems
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 SoSe
Content
  • Introduction and Motivation
  • Compilers for Embedded Systems - Requirements and Dependencies
  • Internal Structure of Compilers
  • Pre-Pass Optimizations
  • HIR Optimizations and Transformations
  • Code Generation
  • LIR Optimizations and Transformations
  • Register Allocation
  • WCET-Aware Compilation
  • Outlook
Literature
  • Peter Marwedel. Embedded System Design - Embedded Systems Foundations of Cyber-Physical Systems. 2nd Edition, Springer, 2012.
  • Steven S. Muchnick. Advanced Compiler Design and Implementation. Morgan Kaufmann, 1997.
  • Andrew W. Appel. Modern compiler implementation in C. Oxford University Press, 1998.
Course L1693: Compilers for Embedded Systems
Typ Project-/problem-based Learning
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 SoSe
Content See interlocking course
Literature See interlocking course

Module M1397: Model Checking - Proof Engines and Algorithms

Courses
Title Typ Hrs/wk CP
Model Checking - Proof Engines and Algorithms (L1979) Lecture 2 3
Model Checking - Proof Engines and Algorithms (L1980) Recitation Section (small) 2 3
Module Responsible Prof. Görschwin Fey
Admission Requirements None
Recommended Previous Knowledge Basic knowledge about data structures and algorithms
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students know

  • algorithms and data structures for model checking,
  • basics of Boolean reasoning engines and
  • the impact of specification and modelling on the computational effort for model checking.
Skills

Students can

  • explain and implement algorithms and data structures for model checking,
  • decide whether a given problem can be solved using Boolean reasoning or model checking, and
  • implement the respective algorithms.
Personal Competence
Social Competence

Students

  • discuss relevant topics in class and
  • defend their solutions orally.
Autonomy Using accompanying material students independently learn in-depth relations between concepts explained in the lecture and additional solution strategies.
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Course achievement
Compulsory Bonus Form Description
Yes None Subject theoretical and practical work Die Aufgabe wird im Rahmen von Volresung und Prüfung definiert. Die Lösung der Aufgabe ist Zulassungsvoraussetzung für die Prüfung.
Examination Oral exam
Examination duration and scale 30 min
Assignment for the Following Curricula Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory
Information and Communication Systems: Specialisation Communication Systems, Focus Software: Elective Compulsory
Information and Communication Systems: Specialisation Secure and Dependable IT Systems: Elective Compulsory
Course L1979: Model Checking - Proof Engines and Algorithms
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Görschwin Fey
Language DE/EN
Cycle SoSe
Content

Correctness is a major concern in embedded systems. Model checking can fully automatically proof formal properties about digital hardware or software. Such properties are given in temporal logic, e.g., to prove "No two orthogonal traffic lights will ever be green."

And how do the underlying reasoning algorithms work so effectively in practice despite a computational complexity of NP hardness and beyond?

But what are the limitations of model checking?
How are the models generated from a given design?
The lecture will answer these questions. Open source tools will be used to gather a practical experience.

Among other topics, the lecture will consider the following topics:

  • Modelling digital Hardware, Software, and Cyber Physical Systems

  • Data structures, decision procedures and proof engines

    • Binary Decision Diagrams

    • And-Inverter-Graphs

    • Boolean Satisfiability

    • Satisfiability Modulo Theories

  • Specification Languages

    • CTL

    • LTL

    • System Verilog Assertions

  • Algorithms for

    • Reachability Analysis

    • Symbolic CTL Checking

    • Bounded LTL-Model Checking

    • Optimizations, e.g., induction, abstraction

  • Quality assurance

Literature

Edmund M. Clarke, Jr., Orna Grumberg, and Doron A. Peled. 1999. Model Checking. MIT Press, Cambridge, MA, USA.

A. Biere, A. Biere, M. Heule, H. van Maaren, and T. Walsh. 2009. Handbook of Satisfiability: Volume 185 Frontiers in Artificial Intelligence and Applications. IOS Press, Amsterdam, The Netherlands, The Netherlands.

Selected research papers

Course L1980: Model Checking - Proof Engines and Algorithms
Typ Recitation Section (small)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Görschwin Fey
Language DE/EN
Cycle SoSe
Content See interlocking course
Literature See interlocking course

Module M1301: Software Testing

Courses
Title Typ Hrs/wk CP
Software Testing (L1791) Lecture 2 3
Software Testing (L1792) Project-/problem-based Learning 2 3
Module Responsible Prof. Sibylle Schupp
Admission Requirements None
Recommended Previous Knowledge
  • Software Engineering
  • Higher Programming Languages
  • Object-Oriented Programming
  • Algorithms and Data Structures
  • Experience with (Small) Software Projects
  • Statistics
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge
Students explain the different phases of testing, describe fundamental
techniques of different types of testing, and paraphrase the basic
principles of the corresponding test process. They give examples of
software development scenarios and the corresponding test type and
technique. They explain algorithms used for particular testing
techniques and describe possible advantages and limitations.
Skills
Students identify the appropriate testing type and technique for a given
problem. They adapt and execute respective algorithms to execute a
concrete test technique properly. They interpret testing results and
execute corresponding steps for proper re-test scenarios. They write and
analyze test specifications. They apply bug finding techniques for
non-trivial problems.
Personal Competence
Social Competence

Students discuss relevant topics in class. They defend their solutions orally.
They communicate in English.

Autonomy

Students can assess their level of knowledge continuously and adjust it appropriately, based on feedback and on self-guided studies. Within limits, they can set their own learning goals. Upon successful completion, students can identify and precisely formulate new problems in academic or applied research in the field of software testing. Within this field, they can conduct independent studies to acquire the necessary competencies and compile their findings in academic reports. They can devise plans to arrive at new solutions or assess existing ones

Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Course achievement None
Examination Subject theoretical and practical work
Examination duration and scale Software
Assignment for the Following Curricula Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory
Information and Communication Systems: Specialisation Communication Systems, Focus Software: Elective Compulsory
Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Software and Signal Processing: Elective Compulsory
Course L1791: Software Testing
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Sibylle Schupp
Language EN
Cycle SoSe
Content
  • Fundamentals of software testing
  • Model-based testing
  • Test automation
  • Criteria-based testing
Literature
  • M. Pezze and M. Young, Software Testing and Analysis, John Wiley 2008.
  • P. Ammann and J. Offutt, "Introduction to Software Testing", 2nd edition 2016.
  • A. Zeller: "Why Programs Fail: A Guide to Systematic Debugging", 2nd edition 2012.
Course L1792: Software Testing
Typ Project-/problem-based Learning
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Sibylle Schupp
Language EN
Cycle SoSe
Content
  • Fundamentals of software testing
  • Model-based testing
  • Test automation
  • Criteria-based testing
Literature
  • M. Pezze and M. Young, Software Testing and Analysis, John Wiley 2008.
  • P. Ammann and J. Offutt, "Introduction to Software Testing", 2nd edition 2015.

Module M0556: Computer Graphics

Courses
Title Typ Hrs/wk CP
Computer Graphics (L0145) Lecture 2 3
Computer Graphics (L0768) Recitation Section (small) 2 3
Module Responsible Prof. Tobias Knopp
Admission Requirements None
Recommended Previous Knowledge
  •  Linear Algebra (in particular matrix/vector computation)
  •  Basic programming skills in C/C++


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

Students can explain and describe basic algorithms in 3D computer graphics.


Skills

Students are capable of

  •  implementing a basic 3D rendering pipeline. This consists of projecting simple 3D structures (e.g. cube, spheres) onto a 2D surface using a virtual camera.
  •  apply geometric transformations (e.g. rotation, scaling) in 2D and 3D computer graphics.
  •  using well-known 2D/3D APIs (OpenGL, Cairo) for solving a given problem statement.
Personal Competence
Social Competence

Students can collaborate in a small team on the realization and validation of a 3D computer graphics pipeline.



Autonomy
  •  Students are able to solve simple tasks independently with reference to the contents of the lectures and the exercise sets.
  •  Students are able to solve detailed problems independently with the aid of the tutorial’s programming task.
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Course achievement None
Examination Written exam
Examination duration and scale 90 min
Assignment for the Following Curricula Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory
Information and Communication Systems: Specialisation Communication Systems, Focus Signal Processing: Elective Compulsory
Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Software and Signal Processing: Elective Compulsory
Course L0145: Computer Graphics
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Tobias Knopp
Language EN
Cycle SoSe
Content

Computer graphics and animation are leading to an unprecedented visual revolution. The course deals with its technological foundations:

  • Object-oriented Computer Graphics
  • Projections and Transformations
  • Polygonal and Parametric Modelling
  • Illuminating, Shading, Rendering
  • Computer Animation Techniques
  • Kinematics and Dynamics Effects

Students will be be working on a series of mini-projects which will eventually evolve into a final project. Learning computer graphics and animation resembles learning a musical instrument. Therefore, doing your projects well and in time is essential for performing well on this course.

Literature
Alan H. Watt:
3D Computer Graphics.
Harlow: Pearson (3rd ed., repr., 2009).

Dariush Derakhshani:
Introducing Autodesk Maya 2014.
New York, NY : Wiley (2013).

Course L0768: Computer Graphics
Typ Recitation Section (small)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Tobias Knopp
Language EN
Cycle SoSe
Content See interlocking course
Literature See interlocking course

Module M0924: Software for Embedded Systems

Courses
Title Typ Hrs/wk CP
Software for Embdedded Systems (L1069) Lecture 2 3
Software for Embdedded Systems (L1070) Recitation Section (small) 3 3
Module Responsible Prof. Bernd-Christian Renner
Admission Requirements None
Recommended Previous Knowledge
  • Good knowledge and experience in programming language C
  • Basis knowledge in software engineering
  • Basic understanding of assembly language
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge Students know the basic principles and procedures of software engineering for embedded systems. They are able to describe the usage and pros of event based programming using interrupts. They know the components and functions of a concrete microcontroller. The participants explain requirements of real time systems. They know at least three scheduling algorithms for real time operating systems including their pros and cons.
Skills Students build interrupt-based programs for a concrete microcontroller. They build and use a preemptive scheduler. They use peripheral components (timer, ADC, EEPROM) to realize complex tasks for embedded systems. To interface with external components they utilize serial protocols.
Personal Competence
Social Competence
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 I. Computer and Software Engineering: Elective Compulsory
Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory
Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Software and Signal Processing: Elective Compulsory
Information and Communication Systems: Specialisation Communication Systems, Focus Software: Elective Compulsory
International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory
Mechatronics: Technical Complementary Course: Elective Compulsory
Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory
Mechatronics: Specialisation System Design: Elective Compulsory
Microelectronics and Microsystems: Specialisation Embedded Systems: Elective Compulsory
Microelectronics and Microsystems: Specialisation Embedded Systems: Elective Compulsory
Course L1069: Software for Embdedded Systems
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Bernd-Christian Renner
Language DE/EN
Cycle SoSe
Content
  • General-Purpose Processors
  • Programming the Atmel AVR
  • Interrupts
  • C for Embedded Systems
  • Standard Single Purpose Processors: Peripherals
  • Finite-State Machines
  • Memory
  • Operating Systems for Embedded Systems
  • Real-Time Embedded Systems
  • Boot loader and Power Management
Literature
  1. Embedded System Design,  F. Vahid and T. Givargis,  John Wiley
  2. Programming Embedded Systems: With C and Gnu Development Tools, M. Barr and A. Massa, O'Reilly

  3. C und C++ für Embedded Systems,  F. Bollow, M. Homann, K. Köhn,  MITP
  4. The Art of Designing  Embedded Systems, J. Ganssle, Newnses

  5. Mikrocomputertechnik mit Controllern der Atmel AVR-RISC-Familie,  G. Schmitt, Oldenbourg
  6. Making Embedded Systems: Design Patterns for Great Software, E. White, O'Reilly

Course L1070: Software for Embdedded Systems
Typ Recitation Section (small)
Hrs/wk 3
CP 3
Workload in Hours Independent Study Time 48, Study Time in Lecture 42
Lecturer Prof. Bernd-Christian Renner
Language DE/EN
Cycle SoSe
Content See interlocking course
Literature See interlocking course

Module M1427: Algorithmic Game Theory

Courses
Title Typ Hrs/wk CP
Algorithmic game theory (L2060) Lecture 2 4
Algorithmic game theory (L2061) Recitation Section (large) 2 2
Module Responsible Prof. Matthias Mnich
Admission Requirements None
Recommended Previous Knowledge
  • Mathematics I
  • Mathematics II
  • Algorithms and Data Structures
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge
  • Students can name the basic concepts in algorithmic game theory and mechanism design. 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 game and mechanism design strategies and can reproduce them.
Skills
  • Students can model strategic interaction systems of agents with the help of the concepts studied in this course. Moreover, they are capable of analyzing their efficiency and equilibria, 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 90 min
Assignment for the Following Curricula Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory
Computational Science and Engineering: Specialisation I. Computer Science: Elective Compulsory
Course L2060: Algorithmic game theory
Typ Lecture
Hrs/wk 2
CP 4
Workload in Hours Independent Study Time 92, Study Time in Lecture 28
Lecturer Prof. Matthias Mnich
Language DE/EN
Cycle SoSe
Content

Algorithmic game theory is a topic at the intersection of economics and computation. It deals with analyzing the behavior and interactions of strategic agents, who often try to maximize their incentives. The environment in which those agents interact is referred to as a game. We wish to understand if the agents can reach an "equilibrium", or steady state of the game, in which agents have no incentive to deviate from their chosen strategies. The algorithmic part is to design efficient methods to find equilibria in games, and to make recommendations to the agents so that they can quickly reach a state of personal satisfaction.

We will also study mechanism design. In mechanism design, we wish to design markets and auctions and give strategic options to agents, so that they have an incentive to act rationally. We also wish to design the markets and auctions so that they are efficient, in the sense that all goods are cleared and agents do not overpay for the goods which they acquire.

Topics:

  • basic equilibrium concepts (Nash equilibria, correlated equilibria, ...)
  • strategic actions (best-response dynamics, no-regret dynamics, ...)
  • auction design (revenue-maximizing auctions, Vickrey auctions)
  • stable matching theory (preference aggregations, kidney exchanges, ...)
  • price of anarchy and selfish routing (Braess' paradox, congestion games, ...)
Literature
  • T. Roughgarden: Twenty Lectures on Algorithmic Game Theory, Cambridge University Press, 2016.
  • N. Nisan, T. Roughgarden, E. Tardos, V. Vazirani. Algorithmic Game Theory. Cambridge University Press, 2007.
Course L2061: Algorithmic game theory
Typ Recitation Section (large)
Hrs/wk 2
CP 2
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Lecturer Prof. Matthias Mnich
Language DE/EN
Cycle SoSe
Content See interlocking course
Literature See interlocking course

Module M0839: Traffic Engineering

Courses
Title Typ Hrs/wk CP
Seminar Traffic Engineering (L0902) Seminar 2 2
Traffic Engineering (L0900) Lecture 2 2
Traffic Engineering Exercises (L0901) Recitation Section (small) 1 2
Module Responsible Prof. Andreas Timm-Giel
Admission Requirements None
Recommended Previous Knowledge
  • Fundamentals of communication or computer networks
  • Stochastics
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students are able to describe methods for planning, optimisation and performance evaluation of communication networks.

Skills

Students are able to solve typical planning and optimisation tasks for communication networks. Furthermore they are able to evaluate the network performance using queuing theory.

Students are able to apply independently what they have learned to other and new problems. They can present their results in front of experts and discuss them.

Personal Competence
Social Competence
Autonomy

Students are able to acquire the necessary expert knowledge to understand the functionality and performance of new communication networks independently.

Workload in Hours Independent Study Time 110, Study Time in Lecture 70
Credit points 6
Course achievement None
Examination Oral exam
Examination duration and scale 30 min
Assignment for the Following Curricula Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory
Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory
Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Networks: Elective Compulsory
Course L0902: Seminar Traffic Engineering
Typ Seminar
Hrs/wk 2
CP 2
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Lecturer Prof. Andreas Timm-Giel, Dr. Phuong Nga Tran
Language EN
Cycle WiSe
Content Selected applications of methods for planning, optimization, and performance evaluation of communication networks, which have been introduced in the traffic engineering lecture are prepared by the students and presented in a seminar.
Literature
  • U. Killat, Entwurf und Analyse von Kommunikationsnetzen, Vieweg + Teubner
  • further literature announced in the lecture
Course L0900: Traffic Engineering
Typ Lecture
Hrs/wk 2
CP 2
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Lecturer Prof. Andreas Timm-Giel, Dr. Phuong Nga Tran
Language EN
Cycle WiSe
Content

Network Planning and Optimization
• Linear Programming (LP)
• Network planning with LP solvers
• Planning of communication networks
Queueing Theory for Communication Networks
• Stochastic processes
• Queueing systems
• Switches (circuit- and packet switching)
• Network of queues

Literature

Literatur:
U. Killat, Entwurf und Analyse von Kommunikationsnetzen, Springer
Weitere Literatur wird in der Lehrveranstaltung bekanntgegeben
/
 Literature:
U. Killat, Entwurf und Analyse von Kommunikationsnetzen, Springer
further literature announced in the lecture

Course L0901: Traffic Engineering Exercises
Typ Recitation Section (small)
Hrs/wk 1
CP 2
Workload in Hours Independent Study Time 46, Study Time in Lecture 14
Lecturer Prof. Andreas Timm-Giel
Language EN
Cycle WiSe
Content

Accompanying exercise for the traffic engineering course

Literature

Literatur:
U. Killat, Entwurf und Analyse von Kommunikationsnetzen, Springer
Weitere Literatur wird in der Lehrveranstaltung bekanntgegeben / Literature:
U. Killat, Entwurf und Analyse von Kommunikationsnetzen, Springer
further literature announced in the lecture

Module M0910: Advanced System-on-Chip Design (Lab)

Courses
Title Typ Hrs/wk CP
Advanced System-on-Chip Design (L1061) Project-/problem-based Learning 3 6
Module Responsible Prof. Heiko Falk
Admission Requirements None
Recommended Previous Knowledge

Successful completion of the practical FPGA lab of module "Computer Architecture" is a mandatory prerequisite.

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

This module provides in-depth, hands-on experience on advanced concepts of computer architecture. Using the Hardware Description Language VHDL and using reconfigurable FPGA hardware boards, students learn how to design complex computer systems (so-called systems-on-chip, SoCs), that are commonly found in the domain of embedded systems, in actual hardware.

Starting with a simple processor architecture, the students learn to how realize instruction-processing of a computer processor according to the principle of pipelining. They implement different styles of cache-based memory hierarchies, examine strategies for dynamic scheduling of machine instructions and for branch prediction, and finally construct a complex MPSoC system (multi-processor system-on-chip) that consists of multiple processor cores that are connected via a shared bus.

Skills Students will be able to analyze, how highly specific and individual computer systems can be constructed using a library of given standard components. They evaluate the interferences between the physical structure of a computer system and the software executed thereon. This way, they will be enabled to estimate the effects of design decision at the hardware level on the performance of the entire system, to evaluate the whole and complex system and to propose design options to improve a system.
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, to transform this knowledge into actual implementations of complex hardware structures, and to associate this knowledge with contents of other classes.

Workload in Hours Independent Study Time 138, Study Time in Lecture 42
Credit points 6
Course achievement None
Examination Subject theoretical and practical work
Examination duration and scale VHDL Codes and FPGA-based implementations
Assignment for the Following Curricula Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory
Microelectronics and Microsystems: Specialisation Embedded Systems: Elective Compulsory
Course L1061: Advanced System-on-Chip Design
Typ Project-/problem-based Learning
Hrs/wk 3
CP 6
Workload in Hours Independent Study Time 138, Study Time in Lecture 42
Lecturer Prof. Heiko Falk
Language DE/EN
Cycle WiSe
Content
  • Introduction into fundamental technologies (FPGAs, MIPS single-cycle machine)
  • Pipelined instruction execution
  • Cache-based memory hierarchies
  • Busses and their arbitration
  • Multi-Processor Systems-on-Chip
  • Optional: Advanced pipelining concepts (dynamic scheduling, branch prediction)
Literature
  • D. Patterson, J. Hennessy. Rechnerorganisation und -entwurf. Elsevier, 2005.
  • A. Tanenbaum, J. Goodman. Computerarchitektur. Pearson, 2001.
  • A. Clements. The Principles of Computer Hardware. 3. Auflage, Oxford University Press, 2000.

Module M1742: Operating System Techniques

Courses
Title Typ Hrs/wk CP
Operating System Techniques (L2815) Lecture 1 2
Operating System Techniques (L2816) Project-/problem-based Learning 3 4
Module Responsible Prof. Christian Dietrich
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 Oral exam
Examination duration and scale 25 min
Assignment for the Following Curricula Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory
Course L2815: Operating System Techniques
Typ Lecture
Hrs/wk 1
CP 2
Workload in Hours Independent Study Time 46, Study Time in Lecture 14
Lecturer Prof. Christian Dietrich
Language DE
Cycle WiSe
Content
Literature
Course L2816: Operating System Techniques
Typ Project-/problem-based Learning
Hrs/wk 3
CP 4
Workload in Hours Independent Study Time 78, Study Time in Lecture 42
Lecturer Prof. Christian Dietrich
Language DE
Cycle WiSe
Content
Literature

Module M1741: Operating System Construction

Courses
Title Typ Hrs/wk CP
Operating System Construction (L2812) Lecture 2 3
Operating System Construction (L2814) Project-/problem-based Learning 2 2
Operating System Construction (L2813) Recitation Section (large) 1 1
Module Responsible Prof. Christian Dietrich
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 20 % Subject theoretical and practical work
Examination Oral exam
Examination duration and scale 25 min
Assignment for the Following Curricula Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory
Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory
Course L2812: Operating System Construction
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Christian Dietrich
Language DE
Cycle SoSe
Content
Literature
Course L2814: Operating System Construction
Typ Project-/problem-based Learning
Hrs/wk 2
CP 2
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Lecturer Prof. Christian Dietrich
Language DE
Cycle SoSe
Content
Literature
Course L2813: Operating System Construction
Typ Recitation Section (large)
Hrs/wk 1
CP 1
Workload in Hours Independent Study Time 16, Study Time in Lecture 14
Lecturer Prof. Christian Dietrich
Language DE
Cycle SoSe
Content
Literature

Specialization II: Intelligence Engineering

Module M0633: Industrial Process Automation

Courses
Title Typ Hrs/wk CP
Industrial Process Automation (L0344) Lecture 2 3
Industrial Process Automation (L0345) Recitation Section (small) 2 3
Module Responsible Prof. Alexander Schlaefer
Admission Requirements None
Recommended Previous Knowledge

mathematics and optimization methods
principles of automata 
principles of algorithms and data structures
programming skills

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

The students can evaluate and assess discrete event systems. They can evaluate properties of processes and explain methods for process analysis. The students can compare methods for process modelling and select an appropriate method for actual problems. They can discuss scheduling methods in the context of actual problems and give a detailed explanation of advantages and disadvantages of different programming methods. The students can relate process automation to methods from robotics and sensor systems as well as to recent topics like 'cyberphysical systems' and 'industry 4.0'.


Skills

The students are able to develop and model processes and evaluate them accordingly. This involves taking into account optimal scheduling, understanding algorithmic complexity, and implementation using PLCs.

Personal Competence
Social Competence

The students work in teams to solve problems.


Autonomy

The students can reflect their knowledge and document the results of their work. 


Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Course achievement
Compulsory Bonus Form Description
No 10 % Excercises
Examination Written exam
Examination duration and scale 90 minutes
Assignment for the Following Curricula Bioprocess Engineering: Specialisation A - General Bioprocess Engineering: Elective Compulsory
Chemical and Bioprocess Engineering: Specialisation Chemical Process Engineering: Elective Compulsory
Chemical and Bioprocess Engineering: Specialisation General Process Engineering: Elective Compulsory
Computer Science: Specialisation II: Intelligence Engineering: Elective Compulsory
Electrical Engineering: Specialisation Control and Power Systems Engineering: Elective Compulsory
Aircraft Systems Engineering: Specialisation Cabin Systems: Elective Compulsory
International Management and Engineering: Specialisation II. Mechatronics: Elective Compulsory
International Management and Engineering: Specialisation II. Product Development and Production: Elective Compulsory
Mechanical Engineering and Management: Specialisation Mechatronics: Elective Compulsory
Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory
Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: Elective Compulsory
Process Engineering: Specialisation Chemical Process Engineering: Elective Compulsory
Process Engineering: Specialisation Process Engineering: Elective Compulsory
Course L0344: Industrial Process Automation
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Alexander Schlaefer
Language EN
Cycle WiSe
Content

- foundations of problem solving and system modeling, discrete event systems
- properties of processes, modeling using automata and Petri-nets
- design considerations for processes (mutex, deadlock avoidance, liveness)
- optimal scheduling for processes
- optimal decisions when planning manufacturing systems, decisions under uncertainty
- software design and software architectures for automation, PLCs

Literature

J. Lunze: „Automatisierungstechnik“, Oldenbourg Verlag, 2012
Reisig: Petrinetze: Modellierungstechnik, Analysemethoden, Fallstudien; Vieweg+Teubner 2010
Hrúz, Zhou: Modeling and Control of Discrete-event Dynamic Systems; Springer 2007
Li, Zhou: Deadlock Resolution in Automated Manufacturing Systems, Springer 2009
Pinedo: Planning and Scheduling in Manufacturing and Services, Springer 2009

Course L0345: Industrial Process Automation
Typ Recitation Section (small)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Alexander Schlaefer
Language EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M0550: Digital Image Analysis

Courses
Title Typ Hrs/wk CP
Digital Image Analysis (L0126) Lecture 4 6
Module Responsible Prof. Rolf-Rainer Grigat
Admission Requirements None
Recommended Previous Knowledge

System theory of one-dimensional signals (convolution and correlation, sampling theory, interpolation and decimation, Fourier transform, linear time-invariant systems), linear algebra (Eigenvalue decomposition, SVD), basic stochastics and statistics (expectation values, influence of sample size, correlation and covariance, normal distribution and its parameters), basics of Matlab, basics in optics

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

Students can

  • Describe imaging processes
  • Depict the physics of sensorics
  • Explain linear and non-linear filtering of signals
  • Establish interdisciplinary connections in the subject area and arrange them in their context
  • Interpret effects of the most important classes of imaging sensors and displays using mathematical methods and physical models.


Skills

Students are able to

  • Use highly sophisticated methods and procedures of the subject area
  • Identify problems and develop and implement creative solutions.

Students can solve simple arithmetical problems relating to the specification and design of image processing and image analysis systems.

Students are able to assess different solution approaches in multidimensional decision-making areas.

Students can undertake a prototypical analysis of processes in Matlab.


Personal Competence
Social Competence k.A.


Autonomy

Students can solve image analysis tasks independently using the relevant literature.


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 Minutes, Content of Lecture and materials in StudIP
Assignment for the Following Curricula Computer Science: Specialisation II: Intelligence Engineering: Elective Compulsory
Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory
Electrical Engineering: Specialisation Medical Technology: Elective Compulsory
Information and Communication Systems: Specialisation Communication Systems, Focus Signal Processing: Elective Compulsory
Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Software and Signal Processing: Elective Compulsory
International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory
Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory
Microelectronics and Microsystems: Specialisation Communication and Signal Processing: Elective Compulsory
Microelectronics and Microsystems: Specialisation Communication and Signal Processing: Elective Compulsory
Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Numerics and Computer Science: Elective Compulsory
Course L0126: Digital Image Analysis
Typ Lecture
Hrs/wk 4
CP 6
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Lecturer Prof. Rolf-Rainer Grigat
Language EN
Cycle WiSe
Content
  • Image representation, definition of images and volume data sets, illumination, radiometry, multispectral imaging, reflectivities, shape from shading
  • Perception of luminance and color, color spaces and transforms, color matching functions, human visual system, color appearance models
  • imaging sensors (CMOS, CCD, HDR, X-ray, IR), sensor characterization(EMVA1288), lenses and optics
  • spatio-temporal sampling (interpolation, decimation, aliasing, leakage, moiré, flicker, apertures)
  • features (filters, edge detection, morphology, invariance, statistical features, texture)
  • optical flow ( variational methods, quadratic optimization, Euler-Lagrange equations)
  • segmentation (distance, region growing, cluster analysis, active contours, level sets, energy minimization and graph cuts)
  • registration (distance and similarity, variational calculus, iterative closest points)
Literature

Bredies/Lorenz, Mathematische Bildverarbeitung, Vieweg, 2011
Wedel/Cremers, Stereo Scene Flow for 3D Motion Analysis, Springer 2011
Handels, Medizinische Bildverarbeitung, Vieweg, 2000
Pratt, Digital Image Processing, Wiley, 2001
Jain, Fundamentals of Digital Image Processing, Prentice Hall, 1989

Module M1336: Soft Computing - Introduction to Machine Learning

Courses
Title Typ Hrs/wk CP
Soft Computing - Introduction to Machine Learning (L1869) Lecture 4 6
Module Responsible Prof. Karl-Heinz Zimmermann
Admission Requirements None
Recommended Previous Knowledge

Bachelor in Computer Science.

Basics in higher mathematics are inevitable, like calculus, linear algebra, graph theory, and optimization.

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

Students are able to formalize, compute, and analyze belief networks, alignments of sequences, hidden Markov models, phylogenetic tree models, classical regression and clustering methods, neural networks, and fuzzy controllers. 

Skills Students can apply the relevant algorithms and determine their complexity, and they can make use of the statistics language R.
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 to other fields.

Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Course achievement None
Examination Oral exam
Examination duration and scale 25 min
Assignment for the Following Curricula Computer Science: Specialisation II: Intelligence Engineering: Elective Compulsory
International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory
Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory
Mechatronics: Specialisation System Design: Elective Compulsory
Mechatronics: Technical Complementary Course: Elective Compulsory
Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Numerics and Computer Science: Elective Compulsory
Course L1869: Soft Computing - Introduction to Machine Learning
Typ Lecture
Hrs/wk 4
CP 6
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Lecturer Prof. Karl-Heinz Zimmermann, Dr. Mehwish Saleemi
Language DE/EN
Cycle WiSe
Content Students are able to formalize, compute, and analyze belief networks, alignments of sequences, hidden Markov models, phylogenetic tree models, neural networks, and fuzzy controllers. In particular, inference and learning in belief networks are important topics that the students should be able to master.

Students can apply the relevant algorithms and determine their complexity, and they can make use of the statistics language R.

Literature

1. David Barber, Bayes Reasoning and Machine Learning, Cambridge Univ. Press, Cambridge, 2012.
2. Volker Claus, Stochastische Automaten, Teubner, Stuttgart, 1971.
3. Ernst Klement, Radko Mesiar, Endre Pap, Triangular Norms, Kluwer, Dordrecht, 2000.
4. Timo Koski, John M. Noble, Bayesian Networks, Wiley, New York, 2009.
5. Dimitris Margaritis, Learning Bayesian Network Model Structure from Data, PhD thesis, Carnegie Mellon
University, Pittsburgh, 2003.
6. Hidetoshi Nishimori, Statistical Physics of Spin Glasses and Information Processing, Oxford Univ. Press,
London, 2001.
7. James R. Norris, Markov Chains, Cambridge Univ. Press, Cambridge, 1996.
8. Maria Rizzo, Statistical Computing with R, Chapman & Hall/CRC, Boca Raton, 2008.
9. Peter Sprites, Clark Glymour, Richard Scheines, Causation, Prediction, and Search, Springer, New York,
1993.
10. Raul Royas, Neural Networks, Springer, Berlin, 1996.
11. Lior Pachter, Bernd Sturmfels, Algebraic Statistics for Computational Biology, Cambridge Univ. Press,
Cambridge, 2005.
12. David A. Sprecher, From Algebra to Computational Algorithms, Docent Press, Boston, 2017.
13. Karl-Heinz Zimmermann, Algebraic Statistics, TubDok, Hamburg, 2016.

Module M0629: Intelligent Autonomous Agents and Cognitive Robotics

Courses
Title Typ Hrs/wk CP
Intelligent Autonomous Agents and Cognitive Robotics (L0341) Lecture 2 4
Intelligent Autonomous Agents and Cognitive Robotics (L0512) Recitation Section (small) 2 2
Module Responsible Rainer Marrone
Admission Requirements None
Recommended Previous Knowledge Vectors, matrices, Calculus
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students can explain the agent abstraction, define intelligence in terms of rational behavior, and give details about agent design (goals, utilities, environments). They can describe the main features of environments. The notion of adversarial agent cooperation can be discussed in terms of decision problems and algorithms for solving these problems. For dealing with uncertainty in real-world scenarios, students can summarize how Bayesian networks can be employed as a knowledge representation and reasoning formalism in static and dynamic settings. In addition, students can define decision making procedures in simple and sequential settings, with and with complete access to the state of the environment. In this context, students can describe techniques for solving (partially observable) Markov decision problems, and they can recall techniques for measuring the value of information. Students can identify techniques for simultaneous localization and mapping, and can explain planning techniques for achieving desired states. Students can explain coordination problems and decision making in a multi-agent setting in term of different types of equilibria, social choice functions, voting protocol, and mechanism design techniques.

Skills

Students can select an appropriate agent architecture for concrete agent application scenarios. For simplified agent application students can derive decision trees and apply basic optimization techniques. For those applications they can also create Bayesian networks/dynamic Bayesian networks and apply bayesian reasoning for simple queries. Students can also name and apply different sampling techniques for simplified agent scenarios. For simple and complex decision making students can compute the best action or policies for concrete settings. In multi-agent situations students will apply techniques for finding different equilibria states,e.g., Nash equilibria. For multi-agent decision making students will apply different voting protocols and compare and explain the results.


Personal Competence
Social Competence

Students are able to discuss their solutions to problems with others. They communicate in English

Autonomy

Students are able of checking their understanding of complex concepts by solving varaints of concrete 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 minutes
Assignment for the Following Curricula Computer Science: Specialisation II: Intelligence Engineering: Elective Compulsory
International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory
Mechatronics: Technical Complementary Course: Elective Compulsory
Mechatronics: Specialisation Intelligent Systems and Robotics: 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
Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Numerics and Computer Science: Elective Compulsory
Course L0341: Intelligent Autonomous Agents and Cognitive Robotics
Typ Lecture
Hrs/wk 2
CP 4
Workload in Hours Independent Study Time 92, Study Time in Lecture 28
Lecturer Rainer Marrone
Language EN
Cycle WiSe
Content
  • Definition of agents, rational behavior, goals, utilities, environment types
  • Adversarial agent cooperation: 
    Agents with complete access to the state(s) of the environment, games, Minimax algorithm, alpha-beta pruning, elements of chance
  • Uncertainty: 
    Motivation: agents with no direct access to the state(s) of the environment, probabilities, conditional probabilities, product rule, Bayes rule, full joint probability distribution, marginalization, summing out, answering queries, complexity, independence assumptions, naive Bayes, conditional independence assumptions
  • Bayesian networks: 
    Syntax and semantics of Bayesian networks, answering queries revised (inference by enumeration), typical-case complexity, pragmatics: reasoning from effect (that can be perceived by an agent) to cause (that cannot be directly perceived).
  • Probabilistic reasoning over time:
    Environmental state may change even without the agent performing actions, dynamic Bayesian networks, Markov assumption, transition model, sensor model, inference problems: filtering, prediction, smoothing, most-likely explanation, special cases: hidden Markov models, Kalman filters, Exact inferences and approximations
  • Decision making under uncertainty:
    Simple decisions: utility theory, multivariate utility functions, dominance, decision networks, value of informatio
    Complex decisions: sequential decision problems, value iteration, policy iteration, MDPs
    Decision-theoretic agents: POMDPs, reduction to multidimensional continuous MDPs, dynamic decision networks
  • Simultaneous Localization and Mapping
  • Planning
  • Game theory (Golden Balls: Split or Share) 
    Decisions with multiple agents, Nash equilibrium, Bayes-Nash equilibrium
  • Social Choice 
    Voting protocols, preferences, paradoxes, Arrow's Theorem,
  • Mechanism Design 
    Fundamentals, dominant strategy implementation, Revelation Principle, Gibbard-Satterthwaite Impossibility Theorem, Direct mechanisms, incentive compatibility, strategy-proofness, Vickrey-Groves-Clarke mechanisms, expected externality mechanisms, participation constraints, individual rationality, budget balancedness, bilateral trade, Myerson-Satterthwaite Theorem
Literature
  1. Artificial Intelligence: A Modern Approach (Third Edition), Stuart Russell, Peter Norvig, Prentice Hall, 2010, Chapters 2-5, 10-11, 13-17
  2. Probabilistic Robotics, Thrun, S., Burgard, W., Fox, D. MIT Press 2005

  3. Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations, Yoav Shoham, Kevin Leyton-Brown, Cambridge University Press, 2009

Course L0512: Intelligent Autonomous Agents and Cognitive Robotics
Typ Recitation Section (small)
Hrs/wk 2
CP 2
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Lecturer Rainer Marrone
Language EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M0630: Robotics and Navigation in Medicine

Courses
Title Typ Hrs/wk CP
Robotics and Navigation in Medicine (L0335) Lecture 2 3
Robotics and Navigation in Medicine (L0338) Project Seminar 2 2
Robotics and Navigation in Medicine (L0336) Recitation Section (small) 1 1
Module Responsible Prof. Alexander Schlaefer
Admission Requirements None
Recommended Previous Knowledge
  • principles of math (algebra, analysis/calculus)
  • principles of programming, e.g., in Java or C++
  • solid R or Matlab skills
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

The students can explain kinematics and tracking systems in clinical contexts and illustrate systems and their components in detail. Systems can be evaluated with respect to collision detection and  safety and regulations. Students can assess typical systems regarding design and  limitations.

Skills

The students are able to design and evaluate navigation systems and robotic systems for medical applications.


Personal Competence
Social Competence

The students discuss the results of other groups, provide helpful feedback and can incoorporate feedback into their work.

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 Computer Science: Specialisation II: Intelligence Engineering: Elective Compulsory
Electrical Engineering: Specialisation Medical Technology: Elective Compulsory
International Management and Engineering: Specialisation II. Electrical Engineering: Elective Compulsory
International Management and Engineering: Specialisation II. Process Engineering and Biotechnology: Elective Compulsory
Mechatronics: Specialisation Intelligent Systems and Robotics: 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
Product Development, Materials and Production: Specialisation Product Development: Elective Compulsory
Product Development, Materials and Production: Specialisation Production: Elective Compulsory
Product Development, Materials and Production: Specialisation Materials: Elective Compulsory
Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Bio- and Medical Technology: Elective Compulsory
Course L0335: Robotics and Navigation in Medicine
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Alexander Schlaefer
Language EN
Cycle SoSe
Content

- kinematics
- calibration
- tracking systems
- navigation and image guidance
- motion compensation
The seminar extends and complements the contents of the lecture with respect to recent research results.


Literature

Spong et al.: Robot Modeling and Control, 2005
Troccaz: Medical Robotics, 2012
Further literature will be given in the lecture.

Course L0338: Robotics and Navigation in Medicine
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 EN
Cycle SoSe
Content See interlocking course
Literature See interlocking course
Course L0336: Robotics and Navigation in Medicine
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 EN
Cycle SoSe
Content See interlocking course
Literature See interlocking course

Module M0551: Pattern Recognition and Data Compression

Courses
Title Typ Hrs/wk CP
Pattern Recognition and Data Compression (L0128) Lecture 4 6
Module Responsible Prof. Rolf-Rainer Grigat
Admission Requirements None
Recommended Previous Knowledge

Linear algebra (including PCA, unitary transforms), stochastics and statistics, binary arithmetics

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

Students can name the basic concepts of pattern recognition and data compression.

Students are able to discuss logical connections between the concepts covered in the course and to explain them by means of examples.


Skills

Students can apply statistical methods to classification problems in pattern recognition and to prediction in data compression. On a sound theoretical and methodical basis they can analyze characteristic value assignments and classifications and describe data compression and video signal coding. They are able to use highly sophisticated methods and processes of the subject area. Students are capable of assessing different solution approaches in multidimensional decision-making areas.



Personal Competence
Social Competence

k.A.

Autonomy

Students are capable of identifying problems independently and of solving them scientifically, using the methods they have learnt.


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 Minutes, Content of Lecture and materials in StudIP
Assignment for the Following Curricula Computer Science: Specialisation II: Intelligence Engineering: Elective Compulsory
Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory
Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Software and Signal Processing: Elective Compulsory
Information and Communication Systems: Specialisation Communication Systems, Focus Signal Processing: Elective Compulsory
International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory
International Management and Engineering: Specialisation II. Electrical Engineering: Elective Compulsory
Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory
Mechatronics: Technical Complementary Course: Elective Compulsory
Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: Elective Compulsory
Course L0128: Pattern Recognition and Data Compression
Typ Lecture
Hrs/wk 4
CP 6
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Lecturer Prof. Rolf-Rainer Grigat
Language EN
Cycle SoSe
Content

Structure of a pattern recognition system, statistical decision theory, classification based on statistical models, polynomial regression, dimension reduction, multilayer perceptron regression, radial basis functions, support vector machines, unsupervised learning and clustering, algorithm-independent machine learning, mixture models and EM, adaptive basis function models and boosting, Markov random fields

Information, entropy, redundancy, mutual information, Markov processes, basic coding schemes (code length, run length coding, prefix-free codes), entropy coding (Huffman, arithmetic coding), dictionary coding (LZ77/Deflate/LZMA2, LZ78/LZW), prediction, DPCM, CALIC, quantization (scalar and vector quantization), transform coding, prediction, decorrelation (DPCM, DCT, hybrid DCT, JPEG, JPEG-LS), motion estimation, subband coding, wavelets, HEVC (H.265,MPEG-H)

Literature

Schürmann: Pattern Classification, Wiley 1996
Murphy, Machine Learning, MIT Press, 2012
Barber, Bayesian Reasoning and Machine Learning, Cambridge, 2012
Duda, Hart, Stork: Pattern Classification, Wiley, 2001
Bishop: Pattern Recognition and Machine Learning, Springer 2006

Salomon, Data Compression, the Complete Reference, Springer, 2000
Sayood, Introduction to Data Compression, Morgan Kaufmann, 2006
Ohm, Multimedia Communication Technology, Springer, 2004
Solari, Digital video and audio compression, McGraw-Hill, 1997
Tekalp, Digital Video Processing, Prentice Hall, 1995

Module M0627: Machine Learning and Data Mining

Courses
Title Typ Hrs/wk CP
Machine Learning and Data Mining (L0340) Lecture 2 4
Machine Learning and Data Mining (L0510) Recitation Section (small) 2 2
Module Responsible NN
Admission Requirements None
Recommended Previous Knowledge
  • Calculus
  • Stochastics
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students can explain the difference between instance-based and model-based learning approaches, and they can enumerate basic machine learning technique for each of the two basic approaches, either on the basis of static data, or on the basis of incrementally incoming data . For dealing with uncertainty, students can describe suitable representation formalisms, and they explain how axioms, features, parameters, or structures used in these formalisms can be learned automatically with different algorithms. Students are also able to sketch different clustering techniques. They depict how the performance of learned classifiers can be improved by ensemble learning, and they can summarize how this influences computational learning theory. Algorithms for reinforcement learning can also be explained by students.

Skills

Student derive decision trees and, in turn, propositional rule sets from simple and static data tables and are able to name and explain basic optimization techniques. They present and apply the basic idea of first-order inductive leaning. Students apply the BME, MAP, ML, and EM algorithms for learning parameters of Bayesian networks and compare the different algorithms. They also know how to carry out Gaussian mixture learning. They can contrast kNN classifiers, neural networks, and support vector machines, and name their basic application areas and algorithmic properties. Students can describe basic clustering techniques and explain the basic components of those techniques. Students compare related machine learning techniques, e.g., k-means clustering and nearest neighbor classification. They can distinguish various ensemble learning techniques and compare the different goals of those techniques.




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 minutes
Assignment for the Following Curricula Computer Science: Specialisation II: Intelligence Engineering: Elective Compulsory
International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory
Mechatronics: Technical Complementary Course: Elective Compulsory
Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory
Mechatronics: Specialisation System Design: Elective Compulsory
Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: Elective Compulsory
Course L0340: Machine Learning and Data Mining
Typ Lecture
Hrs/wk 2
CP 4
Workload in Hours Independent Study Time 92, Study Time in Lecture 28
Lecturer Rainer Marrone
Language EN
Cycle SoSe
Content
  • Decision trees
  • First-order inductive learning
  • Incremental learning: Version spaces
  • Uncertainty
  • Bayesian networks
  • Learning parameters of Bayesian networks
    BME, MAP, ML, EM algorithm
  • Learning structures of Bayesian networks
  • Gaussian Mixture Models
  • kNN classifier, neural network classifier, support vector machine (SVM) classifier
  • Clustering
    Distance measures, k-means clustering, nearest neighbor clustering
  • Kernel Density Estimation
  • Ensemble Learning
  • Reinforcement Learning
  • Computational Learning Theory
Literature
  1. Artificial Intelligence: A Modern Approach (Third Edition), Stuart Russel, Peter Norvig, Prentice Hall, 2010, Chapters 13, 14, 18-21
  2. Machine Learning: A Probabilistic Perspective, Kevin Murphy, MIT Press 2012
Course L0510: Machine Learning and Data Mining
Typ Recitation Section (small)
Hrs/wk 2
CP 2
Workload in Hours Independent Study Time 32, Study Time in Lecture 28
Lecturer Rainer Marrone
Language EN
Cycle SoSe
Content See interlocking course
Literature See interlocking course

Module M0623: Intelligent Systems in Medicine

Courses
Title Typ Hrs/wk CP
Intelligent Systems in Medicine (L0331) Lecture 2 3
Intelligent Systems in Medicine (L0334) Project Seminar 2 2
Intelligent Systems in Medicine (L0333) Recitation Section (small) 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, Java/C++ and R/Matlab
  • advanced programming skills
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

The students are able to analyze and solve clinical treatment planning and decision support problems using methods for search, optimization, and planning. They are able to explain methods for classification and their respective advantages and disadvantages in clinical contexts. The students can compare  different methods for representing medical knowledge. They can evaluate methods in the context of clinical data  and explain challenges due to the clinical nature of the data and its acquisition and due to privacy and safety requirements.

Skills

The students can give reasons for selecting and adapting methods for classification, regression, and prediction. They can assess the methods based on actual patient data and evaluate the implemented methods.

Personal Competence
Social Competence

The students discuss the results of other groups, provide helpful feedback and can incoorporate feedback into their work.

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 % Presentation
Yes 10 % Written elaboration
Examination Written exam
Examination duration and scale 90 minutes
Assignment for the Following Curricula Computer Science: Specialisation II: Intelligence Engineering: Elective Compulsory
Electrical Engineering: Specialisation Medical Technology: Elective Compulsory
Interdisciplinary Mathematics: Specialisation Computational Methods in Biomedical Imaging: Compulsory
Mechatronics: Specialisation Intelligent Systems and Robotics: 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
Theoretical Mechanical Engineering: Specialisation Bio- and Medical Technology: Elective Compulsory
Course L0331: Intelligent Systems in Medicine
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Alexander Schlaefer
Language EN
Cycle WiSe
Content

- methods for search, optimization,  planning,  classification, regression and prediction in a clinical context
- representation of medical knowledge
- understanding challenges due to clinical and patient related data and data acquisition
The students will work in groups to apply the methods introduced during the lecture using problem based learning.


Literature

Russel & Norvig: Artificial Intelligence: a Modern Approach, 2012
Berner: Clinical Decision Support Systems: Theory and Practice, 2007
Greenes: Clinical Decision Support: The Road Ahead, 2007
Further literature will be given in the lecture


Course L0334: Intelligent Systems in Medicine
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 EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course
Course L0333: Intelligent Systems in Medicine
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 EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M1302: Applied Humanoid Robotics

Courses
Title Typ Hrs/wk CP
Applied Humanoid Robotics (L1794) Project-/problem-based Learning 6 6
Module Responsible Patrick Göttsch
Admission Requirements None
Recommended Previous Knowledge
  • Object oriented programming; algorithms and data structures
  • Introduction to control systems
  • Control systems theory and design
  • Mechanics
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge
  • Students can explain humanoid robots.
  • Students can explain the basic concepts, relationships and methods of forward- and inverse kinematics
  • Students learn to apply basic control concepts for different tasks in humanoid robotics.
Skills
  • Students can implement models for humanoid robotic systems in Matlab and C++, and use these models for robot motion or other tasks.
  • They are capable of using models in Matlab for simulation and testing these models if necessary with C++ code on the real robot system.
  • They are capable of selecting methods for solving abstract problems, for which no standard methods are available, and apply it successfully.
Personal Competence
Social Competence
  • Students can develop joint solutions in mixed teams and present these.
  • They can provide appropriate feedback to others, and  constructively handle feedback on their own results
Autonomy
  • Students are able to obtain required information from provided literature sources, and to put in into the context of the lecture.
  • They can independently define tasks and apply the appropriate means to solve them.
Workload in Hours Independent Study Time 96, Study Time in Lecture 84
Credit points 6
Course achievement None
Examination Written elaboration
Examination duration and scale 5-10 pages
Assignment for the Following Curricula Computer Science: Specialisation II: Intelligence Engineering: Elective Compulsory
Electrical Engineering: Specialisation Control and Power Systems Engineering: Elective Compulsory
Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Bio- and Medical Technology: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: Elective Compulsory
Course L1794: Applied Humanoid Robotics
Typ Project-/problem-based Learning
Hrs/wk 6
CP 6
Workload in Hours Independent Study Time 96, Study Time in Lecture 84
Lecturer Patrick Göttsch
Language DE/EN
Cycle WiSe/SoSe
Content
  • Fundamentals of kinematics
  • Static and dynamic stability of humanoid robotic systems
  • Combination of different software environments (Matlab, C++, etc.)
  • Introduction to the necessary  software frameworks
  • Team project
  • Presentation and Demonstration of intermediate and final results
Literature
  • B. Siciliano, O. Khatib. "Handbook of Robotics. Part A: Robotics Foundations", Springer (2008)

Module M1249: Medical Imaging

Courses
Title Typ Hrs/wk CP
Medical Imaging (L1694) Lecture 2 3
Medical Imaging (L1695) Recitation Section (small) 2 3
Module Responsible Prof. Tobias Knopp
Admission Requirements None
Recommended Previous Knowledge Basic knowledge in linear algebra, numerics, and signal processing
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

After successful completion of the module, students are able to describe reconstruction methods for different tomographic imaging modalities such as computed tomography and magnetic resonance imaging. They know the necessary basics from the fields of signal processing and inverse problems and are familiar with both analytical and iterative image reconstruction methods. The students have a deepened knowledge of the imaging operators of computed tomography and magnetic resonance imaging.

Skills

The students are able to implement reconstruction methods and test them using tomographic measurement data. They can visualize the reconstructed images and evaluate the quality of their data and results. In addition, students can estimate the temporal complexity of imaging algorithms.

Personal Competence
Social Competence

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

Autonomy

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

Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Course achievement None
Examination Written exam
Examination duration and scale 90 min
Assignment for the Following Curricula Computer Science: Specialisation II: Intelligence Engineering: Elective Compulsory
Electrical Engineering: Specialisation Medical Technology: Elective Compulsory
Interdisciplinary Mathematics: Specialisation Computational Methods in Biomedical Imaging: Compulsory
Microelectronics and Microsystems: Specialisation Communication and Signal Processing: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Bio- and Medical Technology: Elective Compulsory
Course L1694: Medical Imaging
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Tobias Knopp
Language DE/EN
Cycle WiSe
Content
  • Overview about different imaging methods
  • Signal processing
  • Inverse problems
  • Computed tomography
  • Magnetic resonance imaging
  • Compressed Sensing
  • Magnetic particle imaging

Literature

Bildgebende Verfahren in der Medizin; O. Dössel; Springer, Berlin, 2000

Bildgebende Systeme für die medizinische Diagnostik; H. Morneburg (Hrsg.); Publicis MCD, München, 1995

Introduction to the Mathematics of Medical Imaging; C. L.Epstein; Siam, Philadelphia, 2008

Medical Image Processing, Reconstruction and Restoration; J. Jan; Taylor and Francis, Boca Raton, 2006

Principles of Magnetic Resonance Imaging; Z.-P. Liang and P. C. Lauterbur; IEEE Press, New York, 1999

Course L1695: Medical Imaging
Typ Recitation Section (small)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Tobias Knopp
Language DE/EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Specialization III. Mathematics

Module M0667: Algorithmic Algebra

Courses
Title Typ Hrs/wk CP
Algorithmic Algebra (L0422) Lecture 3 5
Algorithmic Algebra (L0423) Recitation Section (small) 1 1
Module Responsible Dr. Prashant Batra
Admission Requirements None
Recommended Previous Knowledge

Mathe I-III (Real analysis,computing in Vector spaces , principle of complete induction)  Diskrete Mathematik I (gropus, rings, ideals, fields; euclidean algorithm)

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

Students can discuss logical connections between the following concepts and explain them by means of examples: Smith normal form, Chinese remainder theorem, grid point sets, integer solution of inequality systems.

Skills

Students are able to access independently further logical connections between the concepts with which they have become familiar and are able to verify them.

Students are able to develop a suitable solution approach to given problems, to pursue it and to evaluate the results critically, such as in solving multivariate equation systems and in grid point theory.

Personal Competence
Social Competence
Autonomy
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Course achievement None
Examination Oral exam
Examination duration and scale 30 min
Assignment for the Following Curricula Computer Science: Specialisation III. Mathematics: Elective Compulsory
Course L0422: Algorithmic Algebra
Typ Lecture
Hrs/wk 3
CP 5
Workload in Hours Independent Study Time 108, Study Time in Lecture 42
Lecturer Dr. Prashant Batra
Language DE
Cycle WiSe
Content

Extended euclidean algorithm, solution of the Bezout-equation

Division with remainder (over rings)

 fast arithmetic algorithms (conversion, fast multiplications)

discrete Fourier-transformation over rings

Computation with  modular remainders, solving of remainder systems (chinese remainder theorem), solvability of integer linear systems over the integers

linearization of polynomial equations-- matrix approach

Sylvester-matrix, elimination

elimination in rings, elimination of many variables

Buchberger algorithm, Gröbner basis

Minkowskis Lattice Point theorem and integer-valued  optimization

LLL-algorithm for construction of  'short' lattice vectors in polynomial time

Literature von zur Gathen, Joachim; Gerhard, Jürgen

Modern computer algebra. 3rd ed. (English) Zbl 1277.68002
Cambridge: Cambridge University Press (ISBN 978-1-107-03903-2/hbk; 978-1-139-85606-5/ebook).


Yap, Chee Keng
Fundamental problems of algorithmic algebra. (English) Zbl 0999.68261
Oxford: Oxford University Press. xvi, 511 p. $ 87.00 (2000).


Free download for students from author's website: http://cs.nyu.edu/yap/book/berlin/

Cox, David; Little, John; O’Shea, Donal
Ideals, varieties, and algorithms. An introduction to computational algebraic geometry and commutative algebra. 3rd ed. (English) Zbl 1118.13001
Undergraduate Texts in Mathematics. New York, NY: Springer (ISBN 978-0-387-35650-1/hbk; 978-0-387-35651-8/ebook). xv, 551 p.

eBook: http://dx.doi.org/10.1007/978-0-387-35651-8


Concrete abstract algebra : from numbers to Gröbner bases / Niels Lauritzen
Verfasser: 
Lauritzen, Niels
Ausgabe: 
Reprinted with corr.
Erschienen: 
Cambridge [u.a.] : Cambridge Univ. Press, 2006
Umfang: 
XIV, 240 S. : graph. Darst.
Anmerkung: 
Includes bibliographical references and index
ISBN: 
0-521-82679-9, 978-0-521-82679-2 (hbk.) : GBP 55.00
0-521-53410-0, 978-0-521-53410-9 (pbk.) : USD 39.99

Koepf, Wolfram
Computer algebra. An algorithmic oriented introduction. (Computeralgebra. Eine algorithmisch orientierte Einführung.) (German) Zbl 1161.68881
Berlin: Springer (ISBN 3-540-29894-0/pbk). xiii, 515 p.

springer eBook: http://dx.doi.org/10.1007/3-540-29895-9

Kaplan, Michael
Computer algebra. (Computeralgebra.) (German) Zbl 1093.68148
Berlin: Springer (ISBN 3-540-21379-1/pbk). xii, 391 p.

springer eBook:

http://dx.doi.org/10.1007/b137968



Course L0423: Algorithmic Algebra
Typ Recitation Section (small)
Hrs/wk 1
CP 1
Workload in Hours Independent Study Time 16, Study Time in Lecture 14
Lecturer Dr. Prashant Batra
Language DE
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M1428: Linear and Nonlinear Optimization

Courses
Title Typ Hrs/wk CP
Linear and Nonlinear Optimization (L2062) Lecture 4 4
Linear and Nonlinear Optimization (L2063) Recitation Section (large) 1 2
Module Responsible Prof. Matthias Mnich
Admission Requirements None
Recommended Previous Knowledge
  • Discrete Algebraic Structures
  • Mathematics I
  • 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 linear and non-linear 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 linear and non-linear 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 110, Study Time in Lecture 70
Credit points 6
Course achievement None
Examination Oral exam
Examination duration and scale 30 min
Assignment for the Following Curricula Computer Science: Specialisation III. Mathematics: Elective Compulsory
Computational Science and Engineering: Specialisation III. Mathematics: Elective Compulsory
Course L2062: Linear and Nonlinear Optimization
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
  • Modelling linear programming problems
  • Graphical method
  • Algebraic background
  • Convexity
  • Polyhedral theory
  • Simplex method
  • Degeneracy and convergence
  • duality
  • interior-point methods
  • quadratic optimization
  • integer linear programming
Literature
  • A. Schrijver: Combinatorial Optimization: Polyhedra and Efficiency. Springer, 2003
  • B. Korte and T. Vygen: Combinatorial Optimization: Theory and Algorithms. Springer, 2018
  • T. Cormen, Ch. Leiserson, R. Rivest, C. Stein: Introduction to Algorithms. MIT Press, 2013
Course L2063: Linear and Nonlinear Optimization
Typ Recitation Section (large)
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 M0716: Hierarchical Algorithms

Courses
Title Typ Hrs/wk CP
Hierarchical Algorithms (L0585) Lecture 2 3
Hierarchical Algorithms (L0586) Recitation Section (small) 2 3
Module Responsible Prof. Sabine Le Borne
Admission Requirements None
Recommended Previous Knowledge
  • Mathematics I, II, III for Engineering students (german or english) or Analysis & Linear Algebra I + II as well as Analysis III for Technomathematicians
  • Programming experience in C
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students are able to

  • name representatives of hierarchical algorithms and list their characteristics,
  • explain construction techniques for hierarchical algorithms,
  • discuss aspects regarding the efficient implementation of hierarchical algorithms.
Skills

Students are able to

  • implement the hierarchical algorithms discussed in the lecture,
  • analyse the storage and computational complexities of the algorithms,
  • adapt algorithms to problem settings of various applications and thus develop problem adapted variants.
Personal Competence
Social Competence

Students are able to

  • work together in heterogeneously composed teams (i.e., teams from different study programs and background knowledge), explain theoretical foundations and support each other with practical aspects regarding the implementation of algorithms.
Autonomy

Students are capable

  • to assess whether the supporting theoretical and practical excercises are better solved individually or in a team,
  • to work on complex problems over an extended period of time,
  • to assess their individual progess and, if necessary, to ask questions and seek help.
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Course achievement None
Examination Oral exam
Examination duration and scale 20 min
Assignment for the Following Curricula Computer Science: Specialisation III. Mathematics: Elective Compulsory
Mathematical Modelling in Engineering: Theory, Numerics, Applications: Specialisation ll. Modelling and Simulation of Complex Systems (TUHH): Elective Compulsory
Technomathematics: Specialisation I. Mathematics: Elective Compulsory
Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Numerics and Computer Science: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Simulation Technology: Elective Compulsory
Course L0585: Hierarchical Algorithms
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Sabine Le Borne
Language DE/EN
Cycle WiSe
Content
  • Low rank matrices
  • Separable expansions
  • Hierarchical matrix partitions
  • Hierarchical matrices
  • Formatted matrix operations
  • Applications
  • Additional topics (e.g. H2 matrices, matrix functions, tensor products)
Literature W. Hackbusch: Hierarchische Matrizen: Algorithmen und Analysis
Course L0586: Hierarchical Algorithms
Typ Recitation Section (small)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Sabine Le Borne
Language DE/EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M1337: Curves, Cryptosystems and Quantum Computing

Courses
Title Typ Hrs/wk CP
Curves, Cryptosystems and Quantum Computing (L1870) Lecture 4 6
Module Responsible Prof. Karl-Heinz Zimmermann
Admission Requirements None
Recommended Previous Knowledge Higher algebra, linear algebra, and mathematical analysis.
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge The students understand the basic theory of elliptic curves, classical cryptosysteme, basic methods of cryptanalysis, cryptography of elliptic curves, quantum computing and the post-quantum computing scenario.
Skills The students are in the position to apply the group law of elliptic curves, to find out if a curve is non-singular, to sketch cryptographic algorithms that make use of elliptic curves and to specify quantum algorithms.
Personal Competence
Social Competence

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

Autonomy

Students are able to acquire new knowledge from specific standard books and to associate the acquired knowledge to other classes.

Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Course achievement None
Examination Oral exam
Examination duration and scale 25 min
Assignment for the Following Curricula Computer Science: Specialisation III. Mathematics: Elective Compulsory
Course L1870: Curves, Cryptosystems and Quantum Computing
Typ Lecture
Hrs/wk 4
CP 6
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Lecturer Prof. Karl-Heinz Zimmermann
Language DE/EN
Cycle SoSe
Content
Literature

Module M1310: Discrete Differential Geometry

Courses
Title Typ Hrs/wk CP
Discrete Differential Geometry (L1808) Lecture 4 6
Module Responsible Prof. Karl-Heinz Zimmermann
Admission Requirements None
Recommended Previous Knowledge Linear Algebra, Multivariate Calculus 
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

These lectures are on geometrical aspects of the solutions of differential equations and their treatment on the computer. The required basics from linear algebra and analysis are reviewed at the beginning. Applications are to curved surfaces in space, to mechanics and mechatronics, to different types of field equations, and to the tranfer of mathematical constructions to data types, compiler functions, programming languages, and special compute circuits.

- basic prerequisites from linear algebra, tensors, exterior algebra, Clifford algebras

- basic prerequisites from coordinate-free analysis, vector fields and differential forms, integration, discretization

- local differential geometry: connections, symplectic geometry and Hamiltonian systems, Riemannian geometry, discretization

- global differential geometry: manifolds, Lie groups, fiber bundles, random processes, space and time


Skills
Personal Competence
Social Competence
Autonomy
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Course achievement None
Examination Oral exam
Examination duration and scale 25 min
Assignment for the Following Curricula Computer Science: Specialisation III. Mathematics: Elective Compulsory
Course L1808: Discrete Differential Geometry
Typ Lecture
Hrs/wk 4
CP 6
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Lecturer Prof. Georg Friedrich Mayer-Lindenberg
Language DE/EN
Cycle SoSe
Content

These lectures deal with geometric aspects of differential equations and with their treatment on the computer. The prerequisites from linear algebra and analysis are reviewed at the beginning. Applications are to curved surfaces, to classical mechanics and mechatronics, to various field equations, to computer graphics and to transferring mathematical constructions to data types, compiler functions, programming languages, and special hardware. Keywords:

Basics from linear algebra, tensors, exterior algebra, Clifford algebras, tuple types

Basics of coordinate-free analysis, vector fields and differential forms, integration, discrete exterior calculus

Local differential geometry: connections, symplectic geometry, Riemannian geometry, discrete mechanics and connections

Global differential geometry: manifolds, Lie groups, fibre bundles, Fourier decompositions, random processes, space and time


Literature

Agricola, Friedrich,  Vektoranalysis,  Vieweg/Teubner 2010

A.C. Da Silva,  Lectures on Symplectic Geometry, Springer L.N. Math. 1764

J. Snygg, Differential Geometry using Clifford's Algebra, Birkhäuser 2010

T. Frankel, The Geometry of Physics, Cambridge U. P. 2012

M.Desbrun et al., Discrete exterior calculus, arXiv:math/0508341v2

J.Marsden  et al., Discrete Mechanics and Variational Integrators, Acta numerica. 2001

Module M1405: Randomised Algorithms and Random Graphs

Courses
Title Typ Hrs/wk CP
Randomised Algorithms and Random Graphs (L2010) Lecture 2 3
Randomised Algorithms and Random Graphs (L2011) Recitation Section (large) 2 3
Module Responsible Prof. Anusch Taraz
Admission Requirements None
Recommended Previous Knowledge
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge
  • Students can describe basic concepts in the area of Randomized Algorithms and Random Graphs such as random walks, tail bounds, fingerprinting and algebraic techniques, first and second moment methods, and various random graph models. They are able to explain them using appropriate examples.
  • Students can discuss logical connections between these concepts.  They are capable of illustrating these connections with the help of examples.
  • They know proof strategies and can apply them.

Skills
  • Students can model problems with the help of the concepts studied in this course. Moreover, they are capable of solving them by applying established methods.
  • Students are able to explore and verify further logical connections between the concepts studied in the course.
  • For a given problem, the students can develop and execute a suitable technique, and are able to critically evaluate the results.
Personal Competence
Social Competence
  • Students are able to work together in teams. They are capable to establish 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 III. Mathematics: Elective Compulsory
Computational Science and Engineering: Specialisation III. Mathematics: Elective Compulsory
Mathematical Modelling in Engineering: Theory, Numerics, Applications: Specialisation l. Numerics (TUHH): Elective Compulsory
Course L2010: Randomised Algorithms and Random Graphs
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Anusch Taraz, Prof. Volker Turau
Language DE/EN
Cycle SoSe
Content

Randomized Algorithms:

  • introduction and recalling basic tools from probability
  • randomized search
  • random walks
  • text search with fingerprinting
  • parallel and distributed algorithms
  • online algorithms


Random Graphs: 

  • typical properties 
  • first and second moment method
  • tail bounds 
  • thresholds and phase transitions 
  • probabilistic method  
  • models for complex networks 

Literature
  • Motwani, Raghavan: Randomized Algorithms
  • Worsch: Randomisierte Algorithmen
  • Dietzfelbinger: Randomisierte Algorithmen
  • Bollobas: Random Graphs
  • Alon, Spencer: The Probabilistic Method
  • Frieze, Karonski: Random Graphs
  • van der Hofstad: Random Graphs and Complex Networks


Course L2011: Randomised Algorithms and Random Graphs
Typ Recitation Section (large)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Anusch Taraz, Prof. Volker Turau
Language DE/EN
Cycle SoSe
Content See interlocking course
Literature See interlocking course

Module M0711: Numerical Mathematics II

Courses
Title Typ Hrs/wk CP
Numerical Mathematics II (L0568) Lecture 2 3
Numerical Mathematics II (L0569) Recitation Section (small) 2 3
Module Responsible Prof. Sabine Le Borne
Admission Requirements None
Recommended Previous Knowledge
  • Numerical Mathematics I
  • MATLAB knowledge
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students are able to

  • name advanced numerical methods for interpolation, integration, linear least squares problems, eigenvalue problems, nonlinear root finding problems and explain their core ideas,
  • repeat convergence statements for the numerical methods,
  • sketch convergence proofs,
  • explain practical aspects of numerical methods concerning runtime and storage needs


    explain aspects regarding the practical implementation of numerical methods with respect to computational and storage complexity.


Skills

Students are able to

  • implement, apply and compare advanced numerical methods in MATLAB,
  • justify the convergence behaviour of numerical methods with respect to the problem and solution algorithm and to transfer it to related problems,
  • for a given problem, develop a suitable solution approach, if necessary through composition of several algorithms, to execute this approach and to critically evaluate the results


Personal Competence
Social Competence

Students are able to

  • work together in heterogeneously composed teams (i.e., teams from different study programs and background knowledge), explain theoretical foundations and support each other with practical aspects regarding the implementation of algorithms.
Autonomy

Students are capable

  • to assess whether the supporting theoretical and practical excercises are better solved individually or in a team,
  • to assess their individual progess and, if necessary, to ask questions and seek help.
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Course achievement None
Examination Oral exam
Examination duration and scale 25 min
Assignment for the Following Curricula Computer Science: Specialisation III. Mathematics: Elective Compulsory
Computational Science and Engineering: Specialisation III. Mathematics: Elective Compulsory
Technomathematics: Specialisation I. Mathematics: Elective Compulsory
Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory
Theoretical Mechanical Engineering: Core Qualification: Elective Compulsory
Course L0568: Numerical Mathematics II
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Sabine Le Borne, Dr. Jens-Peter Zemke
Language DE/EN
Cycle SoSe
Content
  1. Error and stability: Notions and estimates
  2. Interpolation: Rational and trigonometric interpolation
  3. Quadrature: Gaussian quadrature, orthogonal polynomials
  4. Linear systems: Perturbation theory of decompositions, structured matrices
  5. Eigenvalue problems: LR-, QD-, QR-Algorithmus
  6. Krylov space methods: Arnoldi-, Lanczos methods
Literature
  • Stoer/Bulirsch: Numerische Mathematik 1, Springer
  • Dahmen, Reusken: Numerik für Ingenieure und Naturwissenschaftler, Springer
Course L0569: Numerical Mathematics II
Typ Recitation Section (small)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Sabine Le Borne, Dr. Jens-Peter Zemke
Language DE/EN
Cycle SoSe
Content See interlocking course
Literature See interlocking course

Module M0714: Numerical Treatment of Ordinary Differential Equations

Courses
Title Typ Hrs/wk CP
Numerical Treatment of Ordinary Differential Equations (L0576) Lecture 2 3
Numerical Treatment of Ordinary Differential Equations (L0582) Recitation Section (small) 2 3
Module Responsible Prof. Daniel Ruprecht
Admission Requirements None
Recommended Previous Knowledge
  • Mathematik I, II, III für Ingenieurstudierende (deutsch oder englisch) oder Analysis & Lineare Algebra I + II sowie Analysis III für Technomathematiker
  • Basic MATLAB knowledge
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students are able to

  • list numerical methods for the solution of ordinary differential equations and explain their core ideas,
  • repeat convergence statements for the treated numerical methods (including the prerequisites tied to the underlying problem),
  • explain aspects regarding the practical execution of a method.
  • select the appropriate numerical method for concrete problems, implement the numerical algorithms efficiently and interpret the numerical results
Skills

Students are able to

  • implement (MATLAB), apply and compare numerical methods for the solution of ordinary differential equations,
  • to justify the convergence behaviour of numerical methods with respect to the posed problem and selected algorithm,
  • for a given problem, develop a suitable solution approach, if necessary by the composition of several algorithms, to execute this approach and to critically evaluate the results.


Personal Competence
Social Competence

Students are able to

  • work together in heterogeneously composed teams (i.e., teams from different study programs and background knowledge), explain theoretical foundations and support each other with practical aspects regarding the implementation of algorithms.
Autonomy

Students are capable

  • to assess whether the supporting theoretical and practical excercises are better solved individually or in a team,
  • to assess their individual progress and, if necessary, to ask questions and seek help.
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Course achievement None
Examination Written exam
Examination duration and scale 90 min
Assignment for the Following Curricula Bioprocess Engineering: Specialisation A - General Bioprocess Engineering: Elective Compulsory
Chemical and Bioprocess Engineering: Specialisation Chemical Process Engineering: Elective Compulsory
Chemical and Bioprocess Engineering: Specialisation General Process Engineering: Elective Compulsory
Computer Science: Specialisation III. Mathematics: Elective Compulsory
Electrical Engineering: Specialisation Control and Power Systems Engineering: Elective Compulsory
Energy Systems: Core Qualification: Elective Compulsory
Aircraft Systems Engineering: Specialisation Aircraft Systems: Elective Compulsory
Mathematical Modelling in Engineering: Theory, Numerics, Applications: Specialisation l. Numerics (TUHH): Compulsory
Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory
Technomathematics: Specialisation I. Mathematics: Elective Compulsory
Theoretical Mechanical Engineering: Core Qualification: Compulsory
Process Engineering: Specialisation Chemical Process Engineering: Elective Compulsory
Process Engineering: Specialisation Process Engineering: Elective Compulsory
Course L0576: Numerical Treatment of Ordinary Differential Equations
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Daniel Ruprecht
Language DE/EN
Cycle SoSe
Content

Numerical methods for Initial Value Problems

  • single step methods
  • multistep methods
  • stiff problems
  • differential algebraic equations (DAE) of index 1

Numerical methods for Boundary Value Problems

  • multiple shooting method
  • difference methods
  • variational methods


Literature
  • E. Hairer, S. Noersett, G. Wanner: Solving Ordinary Differential Equations I: Nonstiff Problems
  • E. Hairer, G. Wanner: Solving Ordinary Differential Equations II: Stiff and Differential-Algebraic Problems
Course L0582: Numerical Treatment of Ordinary Differential Equations
Typ Recitation Section (small)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Daniel Ruprecht
Language DE/EN
Cycle SoSe
Content See interlocking course
Literature See interlocking course

Module M0881: Mathematical Image Processing

Courses
Title Typ Hrs/wk CP
Mathematical Image Processing (L0991) Lecture 3 4
Mathematical Image Processing (L0992) Recitation Section (small) 1 2
Module Responsible Prof. Marko Lindner
Admission Requirements None
Recommended Previous Knowledge
  • Analysis: partial derivatives, gradient, directional derivative
  • Linear Algebra: eigenvalues, least squares solution of a linear system
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge

Students are able to 

  • characterize and compare diffusion equations
  • explain elementary methods of image processing
  • explain methods of image segmentation and registration
  • sketch and interrelate basic concepts of functional analysis 
Skills

Students are able to 

  • implement and apply elementary methods of image processing  
  • explain and apply modern methods of image processing
Personal Competence
Social Competence

Students are able to work together in heterogeneously composed teams (i.e., teams from different study programs and background knowledge) and to explain theoretical foundations.

Autonomy
  • Students are capable of checking their understanding of complex concepts on their own. They can specify open questions precisely and know where to get help in solving them.
  • Students have developed sufficient persistence to be able to work for longer periods in a goal-oriented manner on hard problems.
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Course achievement None
Examination Oral exam
Examination duration and scale 20 min
Assignment for the Following Curricula Bioprocess Engineering: Specialisation A - General Bioprocess Engineering: Elective Compulsory
Computer Science: Specialisation III. Mathematics: Elective Compulsory
Computational Science and Engineering: Specialisation III. Mathematics: Elective Compulsory
Interdisciplinary Mathematics: Specialisation Computational Methods in Biomedical Imaging: Compulsory
Mechatronics: Technical Complementary Course: Elective Compulsory
Mechatronics: Specialisation System Design: Elective Compulsory
Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory
Technomathematics: Specialisation I. Mathematics: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: Elective Compulsory
Process Engineering: Specialisation Process Engineering: Elective Compulsory
Course L0991: Mathematical Image Processing
Typ Lecture
Hrs/wk 3
CP 4
Workload in Hours Independent Study Time 78, Study Time in Lecture 42
Lecturer Prof. Marko Lindner
Language DE/EN
Cycle WiSe
Content
  • basic methods of image processing
  • smoothing filters
  • the diffusion / heat equation
  • variational formulations in image processing
  • edge detection
  • de-convolution
  • inpainting
  • image segmentation
  • image registration
Literature Bredies/Lorenz: Mathematische Bildverarbeitung
Course L0992: Mathematical Image Processing
Typ Recitation Section (small)
Hrs/wk 1
CP 2
Workload in Hours Independent Study Time 46, Study Time in Lecture 14
Lecturer Prof. Marko Lindner
Language DE/EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M1552: Mathematics of Neural Networks

Courses
Title Typ Hrs/wk CP
Mathematics of Neural Networks (L2322) Lecture 2 3
Mathematics of Neural Networks (L2323) Recitation Section (small) 2 3
Module Responsible Dr. Jens-Peter Zemke
Admission Requirements None
Recommended Previous Knowledge
  1. Mathematics I-III
  2. Numerical Mathematics 1/ Numerics
  3. Programming skills, preferably in Python
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge Students are able to name, state and classify state-of-the-art neural networks and their corresponding mathematical basics. They can assess the difficulties of different neural networks.
Skills Students are able to implement, understand, and, tailored to the field of application, apply neural networks.
Personal Competence
Social Competence

Students can

  • develop and document joint solutions in small teams;
  • form groups to further develop the ideas and transfer them to other areas of applicability;
  • form a team to develop, build, and advance a software library.
Autonomy

Students are able to

  • correctly assess the time and effort of self-defined work;
  • assess whether the supporting theoretical and practical excercises are better solved individually or in a team;
  • define test problems for testing and expanding the methods;
  • assess their individual progess and, if necessary, to ask questions and seek help.
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Course achievement None
Examination Oral exam
Examination duration and scale 25 min
Assignment for the Following Curricula Computer Science: Specialisation III. Mathematics: Elective Compulsory
Computational Science and Engineering: Specialisation III. Mathematics: Elective Compulsory
Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory
Mechatronics: Technical Complementary Course: Elective Compulsory
Technomathematics: Specialisation I. Mathematics: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: Elective Compulsory
Course L2322: Mathematics of Neural Networks
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Dr. Jens-Peter Zemke
Language DE/EN
Cycle WiSe
Content
  1. Basics: analogy; layout of neural nets, universal approximation, NP-completeness
  2. Feedforward nets: backpropagation, variants of Stochastistic Gradients
  3. Deep Learning: problems and solution strategies
  4. Deep Belief Networks: energy based models, Contrastive Divergence
  5. CNN: idea, layout, FFT and Winograds algorithms, implementation details
  6. RNN: idea, dynamical systems, training, LSTM
  7. ResNN: idea, relation to neural ODEs
  8. Standard libraries: Tensorflow, Keras, PyTorch
  9. Recent trends
Literature
  1. Skript
  2. Online-Werke:
    • http://neuralnetworksanddeeplearning.com/
    • https://www.deeplearningbook.org/


Course L2323: Mathematics of Neural Networks
Typ Recitation Section (small)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Dr. Jens-Peter Zemke
Language DE/EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Module M1020: Numerics of Partial Differential Equations

Courses
Title Typ Hrs/wk CP
Numerics of Partial Differential Equations (L1247) Lecture 2 3
Numerics of Partial Differential Equations (L1248) Recitation Section (small) 2 3
Module Responsible Prof. Daniel Ruprecht
Admission Requirements None
Recommended Previous Knowledge
  • Mathematik I - IV (for Engineering Students) or Analysis & Linear Algebra I + II for Technomathematicians
  • Numerical mathematics 1
  • Numerical treatment of ordinary differential equations
Educational Objectives After taking part successfully, students have reached the following learning results
Professional Competence
Knowledge
  • Students can classify partial differential equations according to the three basic types.
  • For each type, students know suitable numerical approaches.
  • Students know the theoretical convergence results for these approaches.
Skills Students are capable to formulate solution strategies for given problems involving partial differential equations, to comment on theoretical properties concerning convergence and to implement and test these methods in practice.
Personal Competence
Social Competence

Students are able to work together in heterogeneously composed teams (i.e., teams from different study programs and background knowledge) and to explain theoretical foundations.

Autonomy
  • Students are capable of checking their understanding of complex concepts on their own. They can specify open questions precisely and know where to get help in solving them.
  • Students have developed sufficient persistence to be able to work for longer periods in a goal-oriented manner on hard problems.
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Course achievement None
Examination Oral exam
Examination duration and scale 25 min
Assignment for the Following Curricula Computer Science: Specialisation III. Mathematics: Elective Compulsory
Technomathematics: Specialisation I. Mathematics: Elective Compulsory
Theoretical Mechanical Engineering: Specialisation Simulation Technology: Elective Compulsory
Course L1247: Numerics of Partial Differential Equations
Typ Lecture
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Daniel Ruprecht
Language DE/EN
Cycle WiSe
Content

Elementary Theory and Numerics of PDEs

  • types of PDEs
  • well posed problems
  • finite differences
  • finite elements
  • finite volumes
  • applications
Literature

Dietrich Braess: Finite Elemente: Theorie, schnelle Löser und Anwendungen in der Elastizitätstheorie, Berlin u.a., Springer 2007

Susanne Brenner, Ridgway Scott: The Mathematical Theory of Finite Element Methods, Springer, 2008

Peter Deuflhard, Martin Weiser: Numerische Mathematik 3
Course L1248: Numerics of Partial Differential Equations
Typ Recitation Section (small)
Hrs/wk 2
CP 3
Workload in Hours Independent Study Time 62, Study Time in Lecture 28
Lecturer Prof. Daniel Ruprecht
Language DE/EN
Cycle WiSe
Content See interlocking course
Literature See interlocking course

Specialization IV. Subject Specific Focus

Module M1565: Technical Complementary Course I for CSMS

Courses
Title Typ Hrs/wk CP
Module Responsible Prof. Karl-Heinz Zimmermann
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 Depends on choice of courses
Credit points 6
Assignment for the Following Curricula Computer Science: Specialisation IV. Subject Specific Focus: Elective Compulsory

Module M1566: Technical Complementary Course II for CSMS

Courses
Title Typ Hrs/wk CP
Module Responsible Prof. Karl-Heinz Zimmermann
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 Depends on choice of courses
Credit points 6
Assignment for the Following Curricula Computer Science: Specialisation IV. Subject Specific Focus: Elective Compulsory

Module M1564: Advanced Seminars Computer Science

Courses
Title Typ Hrs/wk CP
Advanced Seminar Computer Science I (L2352) Seminar 2 3
Introductory Seminar Computer Science II (L2429) Seminar 2 3
Module Responsible Prof. Karl-Heinz Zimmermann
Admission Requirements None
Recommended Previous Knowledge

Basic knowledge of Computer Science and Mathematics at the Master's level.


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

The students are able to

  • explicate a specific topic in the field of Computer Science,
  • describe complex issues,
  • present different views and evaluate in a critical way. 
Skills

The students are able to

  • familiarize in a specific topic of Computer Science in limited time,
  • realize a literature survey on the specific topic and cite in a correct way,
  • elaborate a presentation and give a lecture to a selected audience,
  • sum up the presentation in 10-15 lines,
  • answer questions in the final discussion.
Personal Competence
Social Competence

The students are able to

  • elaborate and introduce a topic for a certain audience,
  • discuss the topic, content and structure of the presentation with the instructor,
  • discuss certain aspects with the audience, and
  • as the lecturer listen and respond to questions from the audience.
Autonomy

The students are able to

  • define the task in question in an autonomous way,
  • develop the necessary knowledge,
  • use appropriate work equipment, and
  • guided by an instructor critically check the working status.
Workload in Hours Independent Study Time 124, Study Time in Lecture 56
Credit points 6
Course achievement None
Examination Presentation
Examination duration and scale x
Assignment for the Following Curricula Computer Science: Specialisation IV. Subject Specific Focus: Elective Compulsory
Course L2352: Advanced Seminar Computer Science I
Typ Seminar
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/SoSe
Content
Literature
Course L2429: Introductory Seminar Computer Science II
Typ Seminar
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/SoSe
Content
Literature

Thesis

Module M-002: Master Thesis

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

    At least 60 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 use specialized knowledge (facts, theories, and methods) of their subject competently on specialized issues.
  • The students can explain in depth the relevant approaches and terminologies in one or more areas of their subject, describing current developments and taking up a critical position on them.
  • The students can place a research task in their subject area in its context and describe and critically assess the state of research.


Skills

The students are able:

  • To select, apply and, if necessary, develop further methods that are suitable for solving the specialized problem in question.
  • To apply knowledge they have acquired and methods they have learnt in the course of their studies to complex and/or incompletely defined problems in a solution-oriented way.
  • To develop new scientific findings in their subject area and subject them to a critical assessment.
Personal Competence
Social Competence

Students can

  • Both in writing and orally outline a scientific issue for an expert audience accurately, understandably and in a structured way.
  • Deal with issues competently in an expert discussion and answer them in a manner that is appropriate to the addressees while upholding their own assessments and viewpoints convincingly.


Autonomy

Students are able:

  • To structure a project of their own in work packages and to work them off accordingly.
  • To work their way in depth into a largely unknown subject and to access the information required for them to do so.
  • To apply the techniques of scientific work comprehensively in research of their own.
Workload in Hours Independent Study Time 900, Study Time in Lecture 0
Credit points 30
Course achievement None
Examination Thesis
Examination duration and scale According to General Regulations
Assignment for the Following Curricula Civil Engineering: Thesis: Compulsory
Bioprocess Engineering: Thesis: Compulsory
Chemical and Bioprocess Engineering: Thesis: Compulsory
Computer Science: Thesis: Compulsory
Electrical Engineering: Thesis: Compulsory
Energy and Environmental Engineering: Thesis: Compulsory
Energy Systems: Thesis: Compulsory
Environmental Engineering: Thesis: Compulsory
Aircraft Systems Engineering: Thesis: Compulsory
Global Innovation Management: Thesis: Compulsory
Computational Science and Engineering: Thesis: Compulsory
Information and Communication Systems: Thesis: Compulsory
Interdisciplinary Mathematics: Thesis: Compulsory
International Management and Engineering: Thesis: Compulsory
Joint European Master in Environmental Studies - Cities and Sustainability: Thesis: Compulsory
Logistics, Infrastructure and Mobility: Thesis: Compulsory
Materials Science: Thesis: Compulsory
Mechanical Engineering and Management: Thesis: Compulsory
Mechatronics: Thesis: Compulsory
Biomedical Engineering: Thesis: Compulsory
Microelectronics and Microsystems: Thesis: Compulsory
Product Development, Materials and Production: Thesis: Compulsory
Renewable Energies: Thesis: Compulsory
Naval Architecture and Ocean Engineering: Thesis: Compulsory
Ship and Offshore Technology: Thesis: Compulsory
Teilstudiengang Lehramt Metalltechnik: Thesis: Compulsory
Theoretical Mechanical Engineering: Thesis: Compulsory
Process Engineering: Thesis: Compulsory
Water and Environmental Engineering: Thesis: Compulsory
Certification in Engineering & Advisory in Aviation: Thesis: Compulsory