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 

Skills 

Personal Competence  
Social Competence 

Autonomy 

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: Nontechnical 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. Selfreliance, selfmanagement, 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 crossdisciplinarily 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 startups in a goaloriented way. The fields of teaching are augmented by soft skills offers and a foreign language offer. Here, the focus is on encouraging goaloriented 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

Skills 
Professional Competence (Skills) In selected subareas students can

Personal Competence  
Social Competence 
Personal Competences (Social Skills) Students will be able

Autonomy 
Personal Competences (Selfreliance) Students are able in selected areas

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  

Module Responsible  Prof. KarlHeinz 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. KarlHeinz Zimmermann 
Language  DE/EN 
Cycle  WiSe 
Content  
Literature 
Module M1564: Advanced Seminars Computer Science 

Courses  

Module Responsible  Prof. KarlHeinz 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

Skills 
The students are able to

Personal Competence  
Social Competence 
The students are able to

Autonomy 
The students are able to

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: Core qualification: 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. KarlHeinz Zimmermann 
Language  DE/EN 
Cycle 
WiSe/ 
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. KarlHeinz Zimmermann 
Language  DE/EN 
Cycle 
WiSe/ 
Content  
Literature 
Specialization I. Computer and Software Engineering
Module M0753: Software Verification 

Courses  

Module Responsible  Prof. Sibylle Schupp  
Admission Requirements  None  
Recommended Previous Knowledge 


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 logicbased 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 online 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 


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 

Literature 

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 M0942: Software Security 

Courses  

Module Responsible  Prof. Riccardo Scandariato 
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

Skills 
Students are capable of

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. Riccardo Scandariato 
Language  EN 
Cycle  WiSe 
Content 

Literature 
M. Howard, D. LeBlanc: Writing Secure Code, 2nd edition, Microsoft Press (2002) G. Hoglund, G. McGraw: Exploiting Software, AddisonWesley (2004) L. Gong, G. Ellison, M. Dageforde: Inside Java 2 Platform Security, 2nd edition, AddisonWesley (2003) B. LaMacchia, S. Lange, M. Lyons, R. Martin, K. T. Price: .NET Framework Security, AddisonWesley 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. Riccardo Scandariato 
Language  EN 
Cycle  WiSe 
Content  See interlocking course 
Literature  See interlocking course 
Module M1694: Security of CyberPhysical Systems 

Courses  

Module Responsible  Prof. Sibylle Fröschle 
Admission Requirements  None 
Recommended Previous Knowledge 
IT security, programming skills, statistics 
Educational Objectives  After taking part successfully, students have reached the following learning results 
Professional Competence  
Knowledge 
The students know and can explain  the threats posed by cyber attacks to cyberphysical systems (CPS)  concrete attacks at a technical level, e.g. on bus systems  security solutions specific to CPS with their capabilities and limitations  examples of security architectures for CPS and the requirements they guarantee  standard security engineering processes for CPS 
Skills 
The students are able to  identify security threats and assess the risks for a given CPS  apply attack toolkits to analyse a networked control system, and detect attacks beyond those taught in class  identify and apply security solutions suitable to the requirements  follow security engineering processes to develop a security architecture for a given CPS  recognize challenges and limitations, e.g. posed by novel types of attack 
Personal Competence  
Social Competence 
The students are able to  expertly discuss security risks and incidents of CPS and their mitigation in a solutionoriented fashion with experts and nonexperts  foster a security culture with respect to CPS and the corresponding critical infrastructures 
Autonomy 
The students are able to  follow up and critically assess current developments in the security of CPS including relevant security incidents  master a new topic within the area by selfstudy and selfinitiated interaction with experts and peers. 
Workload in Hours  Independent Study Time 124, Study Time in Lecture 56 
Credit points  6 
Course achievement  None 
Examination  Written exam 
Examination duration and scale  120 min 
Assignment for the Following Curricula 
Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Software and Signal Processing: Elective Compulsory 
Course L2691: Security of CyberPhysical Systems 
Typ  Lecture 
Hrs/wk  2 
CP  3 
Workload in Hours  Independent Study Time 62, Study Time in Lecture 28 
Lecturer  Prof. Sibylle Fröschle 
Language  EN 
Cycle  WiSe 
Content 
Embedded systems in energy, production, and transportation are currently undergoing a technological transition to highly networked automated cyberphysical systems (CPS). Such systems are potentially vulnerable to cyber attacks, and these can have physical impact. In this course we investigate security threats, solutions and architectures that are specific to CPS. The topics are as follows: Fundamentals and motivating examples Networked and embedded control systems Bus system level attacks Intruder detection systems (IDS), in particular physicsbased IDS System security architectures, including cryptographic solutions Adversarial machine learning attacks in the physical world Aspects of Location and Localization Wireless networks and infrastructures for critical applications Communication security architectures and remaining threats Intruder detection systems (IDS), in particular datacentric IDS Resilience against multiinstance attacks Security Engineering of CPS: Process and Norms 
Literature 
Recent scientific papers and reports in the public domain. 
Course L2692: Security of CyberPhysical Systems 
Typ  Recitation Section (small) 
Hrs/wk  2 
CP  3 
Workload in Hours  Independent Study Time 62, Study Time in Lecture 28 
Lecturer  Prof. Sibylle Fröschle 
Language  EN 
Cycle  WiSe 
Content  See interlocking course 
Literature  See interlocking course 
Module M0926: Distributed Algorithms 

Courses  

Module Responsible  Prof. Volker Turau 
Admission Requirements  None 
Recommended Previous Knowledge 

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 

Literature 

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 M1400: Design of Dependable Systems 

Courses  

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.,
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


Autonomy  Using accompanying material students independently learn indepth 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 


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:
Contents The module introduces the basic concepts for the design and the analysis of dependable systems. Design examples for getting practical handsonexperience in dependable design techniques. The module focuses towards embedded systems. The following topics are covered:

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 M1685: Selected Aspects in Computer Science 

Courses  

Module Responsible  Prof. Görschwin Fey 
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  30 min 
Assignment for the Following Curricula 
Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory 
Course L2672: Selected Aspects in Computer Science 
Typ  Lecture 
Hrs/wk  3 
CP  4 
Workload in Hours  Independent Study Time 78, Study Time in Lecture 42 
Lecturer  Dozenten des SD E 
Language  DE/EN 
Cycle 
WiSe/ 
Content  
Literature 
Course L2673: Selected Aspects in Computer Science 
Typ  Recitation Section (small) 
Hrs/wk  1 
CP  2 
Workload in Hours  Independent Study Time 46, Study Time in Lecture 14 
Lecturer  Dozenten des SD E 
Language  DE/EN 
Cycle 
WiSe/ 
Content  See interlocking course 
Literature  See interlocking course 
Module M1682: Secure Software Engineering 

Courses  

Module Responsible  Prof. Riccardo Scandariato 
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  Written exam 
Examination duration and scale  120 min 
Assignment for the Following Curricula 
Computer Science: Specialisation I. Computer and Software Engineering: 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 
Course L2667: Secure Software Engineering 
Typ  Lecture 
Hrs/wk  2 
CP  3 
Workload in Hours  Independent Study Time 62, Study Time in Lecture 28 
Lecturer  Prof. Riccardo Scandariato 
Language  EN 
Cycle  SoSe 
Content  
Literature 
Course L2668: Secure Software Engineering 
Typ  Project/problembased Learning 
Hrs/wk  2 
CP  3 
Workload in Hours  Independent Study Time 62, Study Time in Lecture 28 
Lecturer  Prof. Riccardo Scandariato 
Language  EN 
Cycle  SoSe 
Content  
Literature 
Module M1397: Model Checking  Proof Engines and Algorithms 

Courses  

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


Skills 
Students can


Personal Competence  
Social Competence 
Students


Autonomy  Using accompanying material students independently learn indepth 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 


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

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  

Module Responsible  Prof. Sibylle Schupp 
Admission Requirements  None 
Recommended Previous Knowledge 

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 retest scenarios. They write and analyze test specifications. They apply bug finding techniques for nontrivial problems. 
Personal Competence  
Social Competence 
Students discuss relevant topics in class. They defend their solutions orally. 
Autonomy 
Students can assess their level of knowledge continuously and adjust it appropriately, based on feedback and on selfguided 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 

Literature 

Course L1792: Software Testing 
Typ  Project/problembased 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 

Literature 

Module M1427: Algorithmic Game Theory 

Courses  

Module Responsible  Prof. Matthias Mnich 
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  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:

Literature 

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 M1248: Compilers for Embedded Systems 

Courses  

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 applicationspecific 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
The high demands on compilers for embedded systems make effective code optimizations mandatory. The students learn in particular,
Since compilers for embedded systems often have to optimize for multiple objectives (e.g., average or worstcase 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 highlevel 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: Core qualification: 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 

Literature 

Course L1693: Compilers for Embedded Systems 
Typ  Project/problembased 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 M0556: Computer Graphics 

Courses  

Module Responsible  Prof. Tobias Knopp 
Admission Requirements  None 
Recommended Previous Knowledge 

Educational Objectives  After taking part successfully, students have reached the following learning results 
Professional Competence  
Knowledge 
Students can explain and describe basic algorithms in 3D computer graphics. 
Skills 
Students are capable of

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

Workload in Hours  Independent Study Time 124, Study Time in Lecture 56 
Credit points  6 
Course achievement  None 
Examination  Written exam 
Examination duration and scale  90 min 
Assignment for the Following Curricula 
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 International Management and Engineering: Specialisation II. Information Technology: 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:
Students will be be working on a series of miniprojects 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 M1741: Operating System Construction 

Courses  

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 


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/problembased 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 
Module M0910: Advanced SystemonChip Design (Lab) 

Courses  

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 indepth, handson 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 (socalled systemsonchip, 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 instructionprocessing of a computer processor according to the principle of pipelining. They implement different styles of cachebased memory hierarchies, examine strategies for dynamic scheduling of machine instructions and for branch prediction, and finally construct a complex MPSoC system (multiprocessor systemonchip) 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 FPGAbased 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 SystemonChip Design 
Typ  Project/problembased 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 

Literature 

Module M0839: Traffic Engineering 

Courses  

Module Responsible  Prof. Andreas TimmGiel 
Admission Requirements  None 
Recommended Previous Knowledge 

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 TimmGiel, 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 

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 TimmGiel, Dr. Phuong Nga Tran 
Language  EN 
Cycle  WiSe 
Content 
Network Planning and Optimization 
Literature 
Literatur: 
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 TimmGiel 
Language  EN 
Cycle  WiSe 
Content 
Accompanying exercise for the traffic engineering course 
Literature 
Literatur: 
Module M1742: Operating System Techniques 

Courses  

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  Subject theoretical and practical work 
Examination duration and scale  40 minutes, contents of course and practical labs 
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/problembased 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 
Specialization II: Intelligence Engineering
Module M0633: Industrial Process Automation 

Courses  

Module Responsible  Prof. Alexander Schlaefer  
Admission Requirements  None  
Recommended Previous Knowledge 
mathematics and optimization methods 

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 


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: Core qualification: 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 
Literature 
J. Lunze: „Automatisierungstechnik“, Oldenbourg Verlag, 2012 
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  

Module Responsible  Prof. RolfRainer Grigat 
Admission Requirements  None 
Recommended Previous Knowledge 
System theory of onedimensional signals (convolution and correlation, sampling theory, interpolation and decimation, Fourier transform, linear timeinvariant 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

Skills 
Students are able to
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 decisionmaking 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 Theoretical Mechanical Engineering: Specialisation Robotics 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. RolfRainer Grigat 
Language  EN 
Cycle  WiSe 
Content 

Literature 
Bredies/Lorenz, Mathematische Bildverarbeitung, Vieweg, 2011 
Module M0629: Intelligent Autonomous Agents and Cognitive Robotics 

Courses  

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 realworld 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 multiagent 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 multiagent situations students will apply techniques for finding different equilibria states,e.g., Nash equilibria. For multiagent 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 
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 

Literature 

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  

Module Responsible  Prof. Alexander Schlaefer  
Admission Requirements  None  
Recommended Previous Knowledge 


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 


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 
Literature 
Spong et al.: Robot Modeling and Control, 2005 
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 M1702: Process Imaging 

Courses  

Module Responsible  Prof. Alexander Penn 
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  Written exam 
Examination duration and scale  120 min 
Assignment for the Following Curricula 
Bioprocess Engineering: Specialisation A  General Bioprocess Engineering: Elective Compulsory Bioprocess Engineering: Specialisation A  General Bioprocess Engineering: Elective Compulsory Bioprocess Engineering: Specialisation B  Industrial Bioprocess Engineering: Elective Compulsory Bioprocess Engineering: Specialisation B  Industrial Bioprocess Engineering: Elective Compulsory Bioprocess Engineering: Specialisation C  Bioeconomic Process Engineering, Focus Energy and Bioprocess Technology: Elective Compulsory Bioprocess Engineering: Specialisation C  Bioeconomic Process Engineering, Focus Energy and Bioprocess Technology: Elective Compulsory Chemical and Bioprocess Engineering: Specialisation General Process Engineering: Elective Compulsory Chemical and Bioprocess Engineering: Specialisation General Process Engineering: Elective Compulsory Chemical and Bioprocess Engineering: Specialisation Bioprocess Engineering: Elective Compulsory Chemical and Bioprocess Engineering: Specialisation Bioprocess Engineering: Elective Compulsory Chemical and Bioprocess Engineering: Specialisation Chemical Process Engineering: Elective Compulsory Chemical and Bioprocess Engineering: Specialisation Chemical Process Engineering: Elective Compulsory Computer Science: Specialisation II: Intelligence Engineering: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems, Focus Signal Processing: Elective Compulsory International Management and Engineering: Specialisation II. Process Engineering and Biotechnology: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: Elective Compulsory Process Engineering: Specialisation Process Engineering: Elective Compulsory Process Engineering: Specialisation Process Engineering: Elective Compulsory Process Engineering: Specialisation Chemical Process Engineering: Elective Compulsory Process Engineering: Specialisation Chemical Process Engineering: Elective Compulsory Process Engineering: Specialisation Environmental Process Engineering: Elective Compulsory Process Engineering: Specialisation Environmental Process Engineering: Elective Compulsory Water and Environmental Engineering: Specialisation Environment: Elective Compulsory Water and Environmental Engineering: Specialisation Environment: Elective Compulsory Water and Environmental Engineering: Specialisation Water: Elective Compulsory Water and Environmental Engineering: Specialisation Water: Elective Compulsory 
Course L2723: Process Imaging 
Typ  Lecture 
Hrs/wk  2 
CP  3 
Workload in Hours  Independent Study Time 62, Study Time in Lecture 28 
Lecturer  Prof. Alexander Penn 
Language  EN 
Cycle  SoSe 
Content  
Literature 
Course L2724: Process Imaging 
Typ  Project/problembased Learning 
Hrs/wk  2 
CP  3 
Workload in Hours  Independent Study Time 62, Study Time in Lecture 28 
Lecturer  Prof. Alexander Penn 
Language  EN 
Cycle  SoSe 
Content  
Literature 
Module M0627: Machine Learning and Data Mining 

Courses  

Module Responsible  NN 
Admission Requirements  None 
Recommended Previous Knowledge 

Educational Objectives  After taking part successfully, students have reached the following learning results 
Professional Competence  
Knowledge 
Students can explain the difference between instancebased and modelbased 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 firstorder 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., kmeans 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 System Design: 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 
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 

Literature 

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  

Module Responsible  Prof. Alexander Schlaefer  
Admission Requirements  None  
Recommended Previous Knowledge 


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


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: Technical Complementary Course: 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 
Literature 
Russel & Norvig: Artificial Intelligence: a Modern Approach, 2012 
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  

Module Responsible  Patrick Göttsch 
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 96, Study Time in Lecture 84 
Credit points  6 
Course achievement  None 
Examination  Written elaboration 
Examination duration and scale  510 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: Technical Complementary Course: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: Elective Compulsory 
Course L1794: Applied Humanoid Robotics 
Typ  Project/problembased 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/ 
Content 

Literature 

Module M1249: Medical Imaging 

Courses  

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 Theoretical Mechanical Engineering: Technical Complementary Course: 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 

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  

Module Responsible  Dr. Prashant Batra 
Admission Requirements  None 
Recommended Previous Knowledge 
Mathe IIII (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 Bezoutequation Division with remainder (over rings) fast arithmetic algorithms (conversion, fast multiplications) discrete Fouriertransformation 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 Sylvestermatrix, elimination elimination in rings, elimination of many variables Buchberger algorithm, Gröbner basis Minkowskis Lattice Point theorem and integervalued optimization LLLalgorithm 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 Yap, Chee Keng Free download for students from author's website: http://cs.nyu.edu/yap/book/berlin/ Cox, David; Little, John; O’Shea, Donal eBook: http://dx.doi.org/10.1007/9780387356518
Koepf, Wolfram springer eBook: http://dx.doi.org/10.1007/3540298959 Kaplan, Michael 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  

Module Responsible  Prof. Matthias Mnich 
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  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 

Literature 

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  

Module Responsible  Prof. Sabine Le Borne 
Admission Requirements  None 
Recommended Previous Knowledge 

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

Skills 
Students are able to

Personal Competence  
Social Competence 
Students are able to

Autonomy 
Students are capable

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 Technomathematics: Specialisation I. Mathematics: Elective Compulsory Theoretical Mechanical Engineering: Technical Complementary Course: 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 

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 M1405: Randomised Algorithms and Random Graphs 

Courses  

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 

Skills 

Personal Competence  
Social Competence 

Autonomy 

Workload in Hours  Independent Study Time 124, Study Time in Lecture 56 
Credit points  6 
Course achievement  None 
Examination  Oral exam 
Examination duration and scale  30 min 
Assignment for the Following Curricula 
Computer Science: Specialisation III. Mathematics: Elective Compulsory Computational Science and Engineering: Specialisation III. Mathematics: 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:

Literature 

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 M0714: Numerical Treatment of Ordinary Differential Equations 

Courses  

Module Responsible  Prof. Daniel Ruprecht 
Admission Requirements  None 
Recommended Previous Knowledge 

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

Skills 
Students are able to

Personal Competence  
Social Competence 
Students are able to

Autonomy 
Students are capable

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: Core qualification: Elective Compulsory Interdisciplinary Mathematics: Specialisation II. Numerical  Modelling Training: 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
Numerical methods for Boundary Value Problems

Literature 

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 M1668: Probability Theory 

Courses  

Module Responsible  Prof. Matthias Schulte 
Admission Requirements  None 
Recommended Previous Knowledge 
Familiarity with the basic concepts of probability 
Educational Objectives  After taking part successfully, students have reached the following learning results 
Professional Competence  
Knowledge 

Skills 

Personal Competence  
Social Competence 

Autonomy 

Workload in Hours  Independent Study Time 124, Study Time in Lecture 56 
Credit points  6 
Course achievement  None 
Examination  Oral exam 
Examination duration and scale  30 min 
Assignment for the Following Curricula 
Computer Science: Specialisation III. Mathematics: Elective Compulsory Interdisciplinary Mathematics: Specialisation II. Numerical  Modelling Training: Compulsory 
Course L2643: Probability Theory 
Typ  Lecture 
Hrs/wk  3 
CP  4 
Workload in Hours  Independent Study Time 78, Study Time in Lecture 42 
Lecturer  Prof. Matthias Schulte 
Language  EN 
Cycle  SoSe 
Content 

Literature 
H. Bauer, Probability theory and elements of measure theory, second edition, Academic Press, 1981. A. Klenke, Probability Theory: A Comprehensive Course, second edition, Springer, 2014. G. F. Lawler, Introduction to Stochastic Processes, second edition, Chapman & Hall/CRC, 2006. A. N. Shiryaev, Probability, second edition, Springer, 1996. 
Course L2644: Probability Theory 
Typ  Recitation Section (small) 
Hrs/wk  1 
CP  2 
Workload in Hours  Independent Study Time 46, Study Time in Lecture 14 
Lecturer  Prof. Matthias Schulte 
Language  EN 
Cycle  SoSe 
Content  See interlocking course 
Literature  See interlocking course 
Module M0711: Numerical Mathematics II 

Courses  

Module Responsible  Prof. Sabine Le Borne 
Admission Requirements  None 
Recommended Previous Knowledge 

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

Skills 
Students are able to

Personal Competence  
Social Competence 
Students are able to

Autonomy 
Students are capable

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. JensPeter Zemke 
Language  DE/EN 
Cycle  SoSe 
Content 

Literature 

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. JensPeter Zemke 
Language  DE/EN 
Cycle  SoSe 
Content  See interlocking course 
Literature  See interlocking course 
Module M0881: Mathematical Image Processing 

Courses  

Module Responsible  Prof. Marko Lindner 
Admission Requirements  None 
Recommended Previous Knowledge 

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

Skills 
Students are able to

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 

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: Technical Complementary Course: 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 

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  

Module Responsible  Dr. JensPeter Zemke 
Admission Requirements  None 
Recommended Previous Knowledge 

Educational Objectives  After taking part successfully, students have reached the following learning results 
Professional Competence  
Knowledge  Students are able to name, state and classify stateoftheart 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

Autonomy 
Students are able to

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. JensPeter Zemke 
Language  DE/EN 
Cycle  WiSe 
Content 

Literature 

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. JensPeter Zemke 
Language  DE/EN 
Cycle  WiSe 
Content  See interlocking course 
Literature  See interlocking course 
Module M1020: Numerics of Partial Differential Equations 

Courses  

Module Responsible  Prof. Daniel Ruprecht 
Admission Requirements  None 
Recommended Previous Knowledge 

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

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 

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: Technical Complementary Course: 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

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 
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  

Module Responsible  Prof. KarlHeinz 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  

Module Responsible  Prof. KarlHeinz 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 
Thesis
Module M002: Master Thesis 

Courses  

Module Responsible  Professoren der TUHH 
Admission Requirements 

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

Skills 
The students are able:

Personal Competence  
Social Competence 
Students can

Autonomy 
Students are able:

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 