Program description
Content
Engineering disciplines utilize the results of computer science and mathematics research to an ever greater extent, both in the development of products and in the products themselves. This trend will certainly continue. New results in computer science and mathematics thus become an important innovation factor in engineering and are therefore central areas of competence for an engineer and a technical university. This has a direct impact on the objectives of the computer science and engineering course.
The main objective of the course is to provide the knowledge and skills necessary for the successful application of engineering techniques in industry, trade and administration at a very high level, so that the productivity of graduates is promoted in the long term.
The master's degree programme in Computer Science and Engineering provides a broad, well-founded and in-depth basic knowledge in the fields of mathematical modelling in computer science, IT systems and engineering sciences. In addition, further knowledge in business administration and management as well as non-technical subjects is acquired in order to increase the skills required to master extensive engineering IT projects. The Master's programme prepares students for practical professional fields of computer science as well as for a doctorate.
Career prospects
The master degree course in Computer Science and Engineering offers excellent prospects on both the industrial and academic job market thanks to its in-depth training in the fields of information and communication technology, systems engineering and scientific computing. The Master's degree qualifies graduates for a doctorate.
Learning target
The desired learning outcomes of the programme are based on the objectives listed above. All the learning outcomes listed represent competences that are required in both corporate and research environments. To differentiate it from the Computer Science and Engineering Bachelor's programme, the competences listed here refer to complex problems, to the consideration of uncertainty and to working under under-specified conditions. In the following, the learning objectives are divided into the categories knowledge, skills, social competence and independence.
Knowledge
Knowledge is composed of facts, principles and theories in the subjects of computer science, mathematics and engineering.
- Students are able to reproduce, define and explain (syntax, semantics, decision problems) new and advanced representation languages of computer science and mathematics necessary for the formal modelling of application problems, so that non-standard application cases can also be treated.
- Students can reproduce advanced data and index structures for sequential and parallel algorithms and name their advantages and disadvantages for special tasks. Students can specify optimal algorithms for solving decision problems for formal modelling techniques, so that (in typical cases) an acceptable runtime behaviour is obtained.
- Students know how to integrate components so that a desired behaviour is obtained (reductionistic and self-organising approach) while taking into account safety, reliability and fault tolerance aspects.
- Students also know non-classical use cases of computer science and mathematical modelling techniques in engineering and can explain them.
- The graduates are able to reflect research objectives, to explain relevant planning to achieve them, and to name the organisational and personnel structures in research projects.
Technical Skills
The ability to apply acquired knowledge in order to master tasks and thus solve problems is supported in many facets in the Computer Science and Engineering degree program.
- Students can design interfaces that allow large and distributed systems to be built from modules whose internals can be adapted without changing the interfaces. Students are able to specify or develop communication structures that have desired properties and connect the modules in an appropriate way.
- Students can design and develop formal representational languages to solve complex problems (syntax, semantics, decision problems), and they can assess and determine the expressiveness required for specific applications. Students can map decision problems of different expressive formalisms to each other and thus compare the expressiveness of formalisms.
- Students can examine algorithms for complex decision problems for completeness and correctness or convergence behaviour and approximation quality, and they can demonstrate whether an algorithm is optimal or for which types of inputs the worst case or the typical case occurs with respect to the runtime behaviour of an algorithm.
- The student can use formal modelling techniques for engineering applications to create, verify or evaluate robust systems to solve non-trivial problems from an application context (using simulation, in terms of a data management system, as an application, etc.).
- Students can demonstrate that desired states of a complex system (in the probable case) are achieved in time (controllability, accessibility with time constraints), and that undesired states are never achieved in any case or that their achievement is unlikely (safety and liveliness properties).
Social Competence
The ability and willingness to work together with others in a goal-oriented manner, to understand their interests and social situations, to communicate and to help shape the working environment and life is broken down as follows for the degree course in Computer Science Engineering
- Students describe scientific questions in a subject area of computer science, engineering or mathematics and explain in a lecture an approach they have developed to solve them, reacting appropriately to questions, additions and comments.
- Students can form teams to solve non-trivial problems in groups with possibly vague task descriptions, define and distribute subtasks, make time arrangements, integrate partial solutions. They are able to communicate efficiently and interact in a socially appropriate manner.
- Students explain the problems described in a scientific essay and the solutions developed in the essay in a field of computer science or mathematics, evaluate the proposed solutions in a lecture and react to scientific questions, additions and comments.
Competence to work independently
The ability and willingness to act independently and responsibly, to reflect on one's own actions and the actions of others, and also to further develop one's own ability to act, can be broken down as follows
- Students independently evaluate the advantages and disadvantages of representation formalisms for specific tasks, compare different algorithms and data structures as well as programming languages and programming tools, and independently select the best solution in each case.
- The graduates work independently on a scientific subfield, can present scientific approaches and results in a presentation and actively follow the presentations of other students, so that an interactive discourse on a scientific topic is created.
- Students integrate themselves independently into a project context and take on tasks in a software or hardware development project on their own responsibility.
Program structure
The curriculum of the master's degree program in Computer Science and Engineering is structured as follows. A minimum number of credits must be earned in each of the three core areas of computer science, engineering and mathematics:
- Computer Science: 18 credits
- Engineering sciences: 12 credit points
- Mathematics: 12 credit points
To deepen their studies, students can choose lectures from the entire catalogue of technical courses offered by TUHH. A total of 24 credit points must be achieved. Practical knowledge and skills are taught in a research project (12 credit points). A further 12 credit points must be earned in the courses Operation & Management and a non-technical supplementary course. The master thesis is assessed with 30 credit points. This results in a total effort of 120 credit points. The curriculum contains a mobility window in such a way that students can spend the third semester abroad.
The following three study plans describe special characteristics of the master's programme in Computer Science and Engineering.
A. Networked Embedded Systems
1. Core subjects computer science
- Software security
- Design of dependable systems
- Communication networks
2. Core subjects engineering sciences
- Digital communications
- Information theory and coding
3. Core subjects mathematics
- Linear and nonlinear optimization
- Randomized algorithms and random graphs
4. Supplementary technical courses
- Software for embedded systems
- Simulation of communication networks
- Wireless sensor networks
- Network security
B. Dependable and Secure Systems
1. Core subjects computer science
- Software security
- Software verification
- Design of dependable systems
2. Core subjects engineering sciences
- Digital signal processing and filters
- Theory and design of control systems
3. Core subjects mathematics
- Linear and nonlinear optimization
- Numerical mathematics II
4. Supplementary technical courses
- Robotics & navigation
- Application safety
- Reliability in engineering dynamics
- Process automation technology
C. Algorithms for Data Engineering
1. Core subjects computer science
- Software verification
- Algorithms for networks
- Distributed algorithms
2. Core subjects engineering sciences
- Information theory and coding
- Theory and design of control systems
3. Core subjects mathematics
- Mathematical image processing
- Hierarchical algorithms
4. Supplementary technical courses
- Digital image analysis
- Numerical mathematics II
- Quantitative methods: statistics & operations research
- Algorithmic algebra
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 |
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Skills |
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Personal Competence | |
Social Competence |
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Autonomy |
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Workload in Hours | Depends on choice of courses |
Credit points | 6 |
Courses |
Information regarding lectures and courses can be found in the corresponding module handbook published separately. |
Module M0524: Non-technical Courses for Master |
Module Responsible | Dagmar Richter |
Admission Requirements | None |
Recommended Previous Knowledge | None |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
The Nontechnical Academic Programms (NTA) imparts skills that, in view of the TUHH’s training profile, professional engineering studies require but are not able to cover fully. Self-reliance, self-management, collaboration and professional and personnel management competences. The department implements these training objectives in its teaching architecture, in its teaching and learning arrangements, in teaching areas and by means of teaching offerings in which students can qualify by opting for specific competences and a competence level at the Bachelor’s or Master’s level. The teaching offerings are pooled in two different catalogues for nontechnical complementary courses. The Learning Architecture consists of a cross-disciplinarily study offering. The centrally designed teaching offering ensures that courses in the nontechnical academic programms follow the specific profiling of TUHH degree courses. The learning architecture demands and trains independent educational planning as regards the individual development of competences. It also provides orientation knowledge in the form of “profiles”. The subjects that can be studied in parallel throughout the student’s entire study program - if need be, it can be studied in one to two semesters. In view of the adaptation problems that individuals commonly face in their first semesters after making the transition from school to university and in order to encourage individually planned semesters abroad, there is no obligation to study these subjects in one or two specific semesters during the course of studies. Teaching and Learning Arrangements provide for students, separated into B.Sc. and M.Sc., to learn with and from each other across semesters. The challenge of dealing with interdisciplinarity and a variety of stages of learning in courses are part of the learning architecture and are deliberately encouraged in specific courses. Fields of Teaching are based on research findings from the academic disciplines cultural studies, social studies, arts, historical studies, communication studies, migration studies and sustainability research, and from engineering didactics. In addition, from the winter semester 2014/15 students on all Bachelor’s courses will have the opportunity to learn about business management and start-ups in a goal-oriented way. The fields of teaching are augmented by soft skills offers and a foreign language offer. Here, the focus is on encouraging goal-oriented communication skills, e.g. the skills required by outgoing engineers in international and intercultural situations. The Competence Level of the courses offered in this area is different as regards the basic training objective in the Bachelor’s and Master’s fields. These differences are reflected in the practical examples used, in content topics that refer to different professional application contexts, and in the higher scientific and theoretical level of abstraction in the B.Sc. This is also reflected in the different quality of soft skills, which relate to the different team positions and different group leadership functions of Bachelor’s and Master’s graduates in their future working life. Specialized Competence (Knowledge) Students can
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Skills |
Professional Competence (Skills) In selected sub-areas students can
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Personal Competence | |
Social Competence |
Personal Competences (Social Skills) Students will be able
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Autonomy |
Personal Competences (Self-reliance) Students are able in selected areas
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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 M1421: Research Project |
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Courses | ||||||||
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Module Responsible | Prof. Görschwin Fey |
Admission Requirements | None |
Recommended Previous Knowledge |
Basic knowledge and techniques in the chosen field of specialization. |
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 specific field of Computer Science or a closely related subject. |
Skills |
Students are able to work self-dependent in a field of Computer Science or a closely related field. |
Personal Competence | |
Social Competence | |
Autonomy | |
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 | Presentation of a current research topic (25-30 min and 5 min discussion). |
Assignment for the Following Curricula |
Computer Science in Engineering: Core Qualification: Compulsory |
Course L2042: Research Project IIW |
Typ | Projection Course |
Hrs/wk | 8 |
CP | 12 |
Workload in Hours | Independent Study Time 248, Study Time in Lecture 112 |
Lecturer | Prof. Volker Turau (sgwe) |
Language | DE/EN |
Cycle |
WiSe/ |
Content |
Current research topics of the chosen specialization. |
Literature |
Aktuelle Literatur zu Forschungsthemen aus der gewählten Vertiefungsrichtung. |
Specialization I. Computer Science
Module M0942: Software Security |
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Courses | ||||||||||||
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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
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Skills |
Students are capable of
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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 |
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Literature |
M. Howard, D. LeBlanc: Writing Secure Code, 2nd edition, Microsoft Press (2002) G. Hoglund, G. McGraw: Exploiting Software, Addison-Wesley (2004) L. Gong, G. Ellison, M. Dageforde: Inside Java 2 Platform Security, 2nd edition, Addison-Wesley (2003) B. LaMacchia, S. Lange, M. Lyons, R. Martin, K. T. Price: .NET Framework Security, Addison-Wesley Professional (2002) D. Gollmann: Computer Security, 3rd edition (2011) |
Course L1104: Software Security |
Typ | Recitation Section (small) |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Riccardo Scandariato |
Language | EN |
Cycle | WiSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M0753: Software Verification |
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Courses | ||||||||||||
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Module Responsible | Prof. Sibylle Schupp | ||||||||
Admission Requirements | None | ||||||||
Recommended Previous Knowledge |
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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. |
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Skills |
Students formulate provable properties of a software system in a formal language. They develop logic-based models that properly abstract from the software under verification and, where necessary, adapt model or property. They construct proofs and property checks by hand or using tools for model checking or deductive verification, and reflect on the scope of the results. Presented with a verification problem in natural language, they select the appropriate verification technique and justify their choice. |
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Personal Competence | |||||||||
Social Competence |
Students discuss relevant topics in class. They defend their solutions orally. They communicate in English. |
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Autonomy |
Using accompanying on-line material for self study, students can assess their level of knowledge continuously and adjust it appropriately. Working on exercise problems, they receive additional feedback. Within limits, they can set their own learning goals. Upon successful completion, students can identify and precisely formulate new problems in academic or applied research in the field of software verification. Within this field, they can conduct independent studies to acquire the necessary competencies and compile their findings in academic reports. They can devise plans to arrive at new solutions or assess existing ones. |
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Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 | ||||||||
Credit points | 6 | ||||||||
Course achievement |
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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 |
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Literature |
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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 M1427: Algorithmic Game Theory |
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Courses | ||||||||||||
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Module Responsible | Prof. Matthias Mnich |
Admission Requirements | None |
Recommended Previous Knowledge |
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Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
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Skills |
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Personal Competence | |
Social Competence |
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Autonomy |
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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:
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Literature |
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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 M1400: Design of Dependable Systems |
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Courses | ||||||||||||
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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 |
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Skills |
Ability to implement dependable systems using the above approaches. Ability to analyzs the dependability of systems using the above methods for analysis. |
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Personal Competence | |||||||||
Social Competence |
Students
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Autonomy | Using accompanying material students independently learn in-depth relations between concepts explained in the lecture and additional solution strategies. | ||||||||
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 | ||||||||
Credit points | 6 | ||||||||
Course achievement |
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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 hands-on-experience in dependable design techniques. The module focuses towards embedded systems. The following topics are covered:
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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 M1812: Constraint Satisfaction Problems |
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Module Responsible | Prof. Antoine Mottet |
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 Computational Science and Engineering: Specialisation I. Computer Science: Elective Compulsory |
Course L3002: Constraint Satisfaction Problems |
Typ | Lecture | |
Hrs/wk | 2 | |
CP | 3 | |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 | |
Lecturer | Prof. Antoine Mottet | |
Language | EN | |
Cycle | SoSe | |
Content |
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Literature |
Course L3003: Constraint Satisfaction Problems |
Typ | Recitation Section (large) |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Antoine Mottet |
Language | EN |
Cycle | SoSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M1810: Autonomous Cyber-Physical Systems |
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Module Responsible | Prof. Bernd-Christian Renner | ||||||||
Admission Requirements | None | ||||||||
Recommended Previous Knowledge |
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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 |
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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 Secure and Dependable IT Systems, Focus Software and Signal Processing: Elective Compulsory |
Course L3000: Autonomous Cyber-Physical Systems |
Typ | Lecture |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Bernd-Christian Renner |
Language | EN |
Cycle | SoSe |
Content | |
Literature |
Course L3001: Autonomous Cyber-Physical Systems |
Typ | Recitation Section (small) |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Bernd-Christian Renner |
Language | EN |
Cycle | SoSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M1774: Advanced Internet Computing |
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Module Responsible | Prof. Stefan Schulte |
Admission Requirements | None |
Recommended Previous Knowledge | Good programming skills are necessary. Previous knowledge in the field of distributed systems is helpful. |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
After successful completion of the course, students are able to:
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Skills |
The students acquire the ability to model Internet-based distributed systems and to work with these systems. This comprises especially the ability to select and utilize fitting technologies for different application areas. Furthermore, students are able to critically assess the chosen technologies. |
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 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, Focus Networks: Elective Compulsory |
Course L2916: Advanced Internet Computing |
Typ | Lecture |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Stefan Schulte |
Language | EN |
Cycle | SoSe |
Content |
This lecture discusses modern Internet-based distributed systems in three blocks: (i) Cloud computing, (ii) the Internet of Things, and (iii) blockchain technologies. The following topics will be covered in the single lectures:
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Literature | Will be discussed in the lecture |
Course L2917: Advanced Internet Computing |
Typ | Project-/problem-based Learning |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Stefan Schulte |
Language | EN |
Cycle | SoSe |
Content |
This project-/problemoriented part of the module augments the theoretical content of the lecture by a concrete technical problem, which needs to be solved by the students in group work during the semester. Possible topics are (blockchain-based) sensor data integration, Big Data processing, Cloud-based redundant data storages, and Cloud-based Onion Routing. |
Literature |
Will be discussed in the lecture. |
Module M0836: Communication Networks |
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Module Responsible | Prof. Andreas Timm-Giel |
Admission Requirements | None |
Recommended Previous Knowledge |
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Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
Students are able to describe the principles and structures of communication networks in detail. They can explain the formal description methods of communication networks and their protocols. They are able to explain how current and complex communication networks work and describe the current research in these examples. |
Skills |
Students are able to evaluate the performance of communication networks using the learned methods. They are able to work out problems themselves and apply the learned methods. They can apply what they have learned autonomously on further and new communication networks. |
Personal Competence | |
Social Competence |
Students are able to define tasks themselves in small teams and solve these problems together using the learned methods. They can present the obtained results. They are able to discuss and critically analyse the solutions. |
Autonomy |
Students are able to obtain the necessary expert knowledge for understanding the functionality and performance capabilities of new communication networks independently. |
Workload in Hours | Independent Study Time 110, Study Time in Lecture 70 |
Credit points | 6 |
Course achievement | None |
Examination | Presentation |
Examination duration and scale | 1.5 hours colloquium with three students, therefore about 30 min per student. Topics of the colloquium are the posters from the previous poster session and the topics of the module. |
Assignment for the Following Curricula |
Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Electrical Engineering: Specialisation Control and Power Systems Engineering: Elective Compulsory Aircraft Systems Engineering: Core Qualification: Elective Compulsory Computer Science in Engineering: Specialisation I. Computer Science: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Networks: Elective Compulsory International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory Mechatronics: Technical Complementary Course: Elective Compulsory Microelectronics and Microsystems: Specialisation Communication and Signal Processing: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: Elective Compulsory |
Course L0899: Selected Topics of Communication Networks |
Typ | Project-/problem-based Learning |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Prof. Andreas Timm-Giel |
Language | EN |
Cycle | WiSe |
Content | Example networks selected by the students will be researched on in a PBL course by the students in groups and will be presented in a poster session at the end of the term. |
Literature |
|
Course L0897: Communication Networks |
Typ | Lecture |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Prof. Andreas Timm-Giel, Dr. Koojana Kuladinithi |
Language | EN |
Cycle | WiSe |
Content | |
Literature |
Further literature is announced at the beginning of the lecture. |
Course L0898: Communication Networks Excercise |
Typ | Project-/problem-based Learning |
Hrs/wk | 1 |
CP | 2 |
Workload in Hours | Independent Study Time 46, Study Time in Lecture 14 |
Lecturer | Prof. Andreas Timm-Giel |
Language | EN |
Cycle | WiSe |
Content | Part of the content of the lecture Communication Networks are reflected in computing tasks in groups, others are motivated and addressed in the form of a PBL exercise. |
Literature |
|
Module M1249: Medical Imaging |
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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 Computer Science in Engineering: Specialisation I. Computer Science: Elective Compulsory Interdisciplinary Mathematics: Specialisation Computational Methods in Biomedical Imaging: Compulsory Microelectronics and Microsystems: Specialisation Communication and Signal Processing: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Bio- and Medical Technology: Elective Compulsory |
Course L1694: Medical Imaging |
Typ | Lecture |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Tobias Knopp |
Language | DE/EN |
Cycle | WiSe |
Content |
|
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 |
Module M0926: Distributed Algorithms |
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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 Computer Science in 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 |
Specialization II. Engineering Science
Module M0676: Digital Communications |
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Courses | ||||||||||||||||
|
Module Responsible | Prof. Gerhard Bauch | ||||||||
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 understand, compare and design modern digital information transmission schemes. They are familiar with the properties of linear and non-linear digital modulation methods. They can describe distortions caused by transmission channels and design and evaluate detectors including channel estimation and equalization. They know the principles of single carrier transmission and multi-carrier transmission as well as the fundamentals of basic multiple access schemes. | ||||||||
Skills | The students are able to design and analyse a digital information transmission scheme including multiple access. They are able to choose a digital modulation scheme taking into account transmission rate, required bandwidth, error probability, and further signal properties. They can design an appropriate detector including channel estimation and equalization taking into account performance and complexity properties of suboptimum solutions. They are able to set parameters of a single carrier or multi carrier transmission scheme and trade the properties of both approaches against each other. | ||||||||
Personal Competence | |||||||||
Social Competence |
The students can jointly solve specific problems. |
||||||||
Autonomy |
The students are able to acquire relevant information from appropriate literature sources. They can control their level of knowledge during the lecture period by solving tutorial problems, software tools, clicker system. |
||||||||
Workload in Hours | Independent Study Time 110, Study Time in Lecture 70 | ||||||||
Credit points | 6 | ||||||||
Course achievement |
|
||||||||
Examination | Written exam | ||||||||
Examination duration and scale | 90 min | ||||||||
Assignment for the Following Curricula |
Electrical Engineering: Core Qualification: Compulsory Computational Science and Engineering: Specialisation II. Engineering Science: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems: Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Networks: Elective Compulsory International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory International Management and Engineering: Specialisation II. Electrical Engineering: Elective Compulsory Microelectronics and Microsystems: Core Qualification: Elective Compulsory |
Course L0444: Digital Communications |
Typ | Lecture |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Gerhard Bauch |
Language | DE/EN |
Cycle | WiSe |
Content |
|
Literature |
K. Kammeyer: Nachrichtenübertragung, Teubner P.A. Höher: Grundlagen der digitalen Informationsübertragung, Teubner. J.G. Proakis, M. Salehi: Digital Communications. McGraw-Hill. S. Haykin: Communication Systems. Wiley R.G. Gallager: Principles of Digital Communication. Cambridge A. Goldsmith: Wireless Communication. Cambridge. D. Tse, P. Viswanath: Fundamentals of Wireless Communication. Cambridge. |
Course L0445: Digital Communications |
Typ | Recitation Section (large) |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Prof. Gerhard Bauch |
Language | DE/EN |
Cycle | WiSe |
Content | See interlocking course |
Literature | See interlocking course |
Course L0646: Laboratory Digital Communications |
Typ | Practical Course |
Hrs/wk | 1 |
CP | 1 |
Workload in Hours | Independent Study Time 16, Study Time in Lecture 14 |
Lecturer | Prof. Gerhard Bauch |
Language | DE/EN |
Cycle | WiSe |
Content |
- DSL transmission - Random processes - Digital data transmission |
Literature |
K. Kammeyer: Nachrichtenübertragung, Teubner P.A. Höher: Grundlagen der digitalen Informationsübertragung, Teubner. J.G. Proakis, M. Salehi: Digital Communications. McGraw-Hill. S. Haykin: Communication Systems. Wiley R.G. Gallager: Principles of Digital Communication. Cambridge A. Goldsmith: Wireless Communication. Cambridge. D. Tse, P. Viswanath: Fundamentals of Wireless Communication. Cambridge. |
Module M1666: Intelligent Systems Lab |
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Courses | ||||||||
|
Module Responsible | Prof. Alexander Schlaefer | ||||||||
Admission Requirements | None | ||||||||
Recommended Previous Knowledge |
Very good programming skills Good knowledge in mathematics Prior knowledge in machine learning is very helpful Prior knowledge in image processing / computer vision is helpful Prior knowledge in robotics is very helpful Prior knowledge in microprocessor programming is helpful |
||||||||
Educational Objectives | After taking part successfully, students have reached the following learning results | ||||||||
Professional Competence | |||||||||
Knowledge |
Students will be able to explain aspects of intelligent systems (e.g. autonomy, sensing the environment, interacting with the environment) and provide links to ai / robotics / machine learning / computer vision. |
||||||||
Skills |
Students can analyze a complex application scenario and use artificial intelligence methods (particularly from robotics, machine learning, computer vision) to implement an intelligent system. Furthermore, students will be able to define criteria to assess the function of the system and evaluate the system. |
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Personal Competence | |||||||||
Social Competence |
The students can define project aims and scope and organize the project as team work. They can present their results in an appropriate manner. |
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Autonomy |
The students take responsibility for their tasks and coordinate their individual work with other group members. They deliver their work on time. They independently acquire additional knowledge by doing a specific literature research. |
||||||||
Workload in Hours | Independent Study Time 96, Study Time in Lecture 84 | ||||||||
Credit points | 6 | ||||||||
Course achievement |
|
||||||||
Examination | Written elaboration | ||||||||
Examination duration and scale | approx. 8 pages, time frame: over the course of the semester | ||||||||
Assignment for the Following Curricula |
Computational Science and Engineering: Specialisation II. Engineering Science: Elective Compulsory |
Course L2709: Intelligent Systems Lab |
Typ | Project-/problem-based Learning |
Hrs/wk | 6 |
CP | 6 |
Workload in Hours | Independent Study Time 96, Study Time in Lecture 84 |
Lecturer | Prof. Alexander Schlaefer |
Language | DE/EN |
Cycle | SoSe |
Content |
The actual project topic will be defined as part of the project. |
Literature |
Wird in der Veranstaltung bekannt gegeben. |
Module M0673: Information Theory and Coding |
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Courses | ||||||||||||
|
Module Responsible | Prof. Gerhard Bauch |
Admission Requirements | None |
Recommended Previous Knowledge |
|
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge | The students know the basic definitions for quantification of information in the sense of information theory. They know Shannon's source coding theorem and channel coding theorem and are able to determine theoretical limits of data compression and error-free data transmission over noisy channels. They understand the principles of source coding as well as error-detecting and error-correcting channel coding. They are familiar with the principles of decoding, in particular with modern methods of iterative decoding. They know fundamental coding schemes, their properties and decoding algorithms. |
Skills | The students are able to determine the limits of data compression as well as of data transmission through noisy channels and based on those limits to design basic parameters of a transmission scheme. They can estimate the parameters of an error-detecting or error-correcting channel coding scheme for achieving certain performance targets. They are able to compare the properties of basic channel coding and decoding schemes regarding error correction capabilities, decoding delay, decoding complexity and to decide for a suitable method. They are capable of implementing basic coding and decoding schemes in software. |
Personal Competence | |
Social Competence |
The students can jointly solve specific problems. |
Autonomy |
The students are able to acquire relevant information from appropriate literature sources. They can control their level of knowledge during the lecture period by solving tutorial problems, software tools, clicker system. |
Workload in Hours | Independent Study Time 110, Study Time in Lecture 70 |
Credit points | 6 |
Course achievement | None |
Examination | Written exam |
Examination duration and scale | 90 min |
Assignment for the Following Curricula |
Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Computational Science and Engineering: Specialisation II. Engineering Science: Elective Compulsory Information and Communication Systems: Core Qualification: Compulsory International Management and Engineering: Specialisation II. Electrical Engineering: Elective Compulsory Mechatronics: Technical Complementary Course: Elective Compulsory |
Course L0436: Information Theory and Coding |
Typ | Lecture |
Hrs/wk | 3 |
CP | 4 |
Workload in Hours | Independent Study Time 78, Study Time in Lecture 42 |
Lecturer | Prof. Gerhard Bauch |
Language | DE/EN |
Cycle | SoSe |
Content |
|
Literature |
Bossert, M.: Kanalcodierung. Oldenbourg. Friedrichs, B.: Kanalcodierung. Springer. Lin, S., Costello, D.: Error Control Coding. Prentice Hall. Roth, R.: Introduction to Coding Theory. Johnson, S.: Iterative Error Correction. Cambridge. Richardson, T., Urbanke, R.: Modern Coding Theory. Cambridge University Press. Gallager, R. G.: Information theory and reliable communication. Whiley-VCH Cover, T., Thomas, J.: Elements of information theory. Wiley. |
Course L0438: Information Theory and Coding |
Typ | Recitation Section (large) |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Prof. Gerhard Bauch |
Language | DE/EN |
Cycle | SoSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M0846: Control Systems Theory and Design |
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Courses | ||||||||||||
|
Module Responsible | Prof. Herbert Werner |
Admission Requirements | None |
Recommended Previous Knowledge | Introduction to Control Systems |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
|
Skills |
|
Personal Competence | |
Social Competence |
Students can work in small groups on specific problems to arrive at joint solutions. |
Autonomy |
Students can obtain information from provided sources (lecture notes, software documentation, experiment guides) and use it when solving given problems. They can assess their knowledge in weekly on-line tests and thereby control their learning progress. |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Course achievement | None |
Examination | Written exam |
Examination duration and scale | 120 min |
Assignment for the Following Curricula |
Electrical Engineering: Core Qualification: Compulsory Energy Systems: Core Qualification: Elective Compulsory Aircraft Systems Engineering: Core Qualification: Elective Compulsory Computer Science in Engineering: Specialisation II. Engineering Science: Elective Compulsory International Management and Engineering: Specialisation II. Electrical Engineering: Elective Compulsory International Management and Engineering: Specialisation II. Mechatronics: Elective Compulsory Mechanical Engineering and Management: Specialisation Mechatronics: Elective Compulsory Mechatronics: Core Qualification: 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: Compulsory Biomedical Engineering: Specialisation Management and Business Administration: Elective Compulsory Product Development, Materials and Production: Core Qualification: Elective Compulsory Theoretical Mechanical Engineering: Core Qualification: Compulsory |
Course L0656: Control Systems Theory and Design |
Typ | Lecture |
Hrs/wk | 2 |
CP | 4 |
Workload in Hours | Independent Study Time 92, Study Time in Lecture 28 |
Lecturer | Prof. Herbert Werner |
Language | EN |
Cycle | WiSe |
Content |
State space methods (single-input single-output) • State space models and transfer functions, state feedback Digital Control System identification and model order reduction Case study |
Literature |
|
Course L0657: Control Systems Theory and Design |
Typ | Recitation Section (small) |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Prof. Herbert Werner |
Language | EN |
Cycle | WiSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M0677: Digital Signal Processing and Digital Filters |
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Courses | ||||||||||||
|
Module Responsible | Prof. Gerhard Bauch |
Admission Requirements | None |
Recommended Previous Knowledge |
|
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
The students know and understand basic algorithms of digital signal processing. They are familiar with the spectral transforms of discrete-time signals and are able to describe and analyse signals and systems in time and image domain. They know basic structures of digital filters and can identify and assess important properties including stability. They are aware of the effects caused by quantization of filter coefficients and signals. They are familiar with the basics of adaptive filters. They can perform traditional and parametric methods of spectrum estimation, also taking a limited observation window into account. The students are familiar with the contents of lecture and tutorials. They can explain and apply them to new problems. |
Skills | The students are able to apply methods of digital signal processing to new problems. They can choose and parameterize suitable filter striuctures. In particular, the can design adaptive filters according to the minimum mean squared error (MMSE) criterion and develop an efficient implementation, e.g. based on the LMS or RLS algorithm. Furthermore, the students are able to apply methods of spectrum estimation and to take the effects of a limited observation window into account. |
Personal Competence | |
Social Competence |
The students can jointly solve specific problems. |
Autonomy |
The students are able to acquire relevant information from appropriate literature sources. They can control their level of knowledge during the lecture period by solving tutorial problems, software tools, clicker system. |
Workload in Hours | Independent Study Time 110, Study Time in Lecture 70 |
Credit points | 6 |
Course achievement | None |
Examination | Written exam |
Examination duration and scale | 90 min |
Assignment for the Following Curricula |
Electrical Engineering: Specialisation Control and Power Systems Engineering: Elective Compulsory Computer Science in Engineering: Specialisation II. Engineering Science: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems, Focus Signal Processing: Elective Compulsory Mechanical Engineering and Management: Specialisation Mechatronics: 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 L0446: Digital Signal Processing and Digital Filters |
Typ | Lecture |
Hrs/wk | 3 |
CP | 4 |
Workload in Hours | Independent Study Time 78, Study Time in Lecture 42 |
Lecturer | Prof. Gerhard Bauch |
Language | EN |
Cycle | WiSe |
Content |
|
Literature |
K.-D. Kammeyer, K. Kroschel: Digitale Signalverarbeitung. Vieweg Teubner. V. Oppenheim, R. W. Schafer, J. R. Buck: Zeitdiskrete Signalverarbeitung. Pearson StudiumA. V. W. Hess: Digitale Filter. Teubner. Oppenheim, R. W. Schafer: Digital signal processing. Prentice Hall. S. Haykin: Adaptive flter theory. L. B. Jackson: Digital filters and signal processing. Kluwer. T.W. Parks, C.S. Burrus: Digital filter design. Wiley. |
Course L0447: Digital Signal Processing and Digital Filters |
Typ | Recitation Section (large) |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Prof. Gerhard Bauch |
Language | EN |
Cycle | WiSe |
Content | See interlocking course |
Literature | See interlocking course |
Specialization III. Mathematics
Module M1428: Linear and Nonlinear Optimization |
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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 M0881: Mathematical Image Processing |
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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: 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 M1405: Randomised Algorithms and Random Graphs |
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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 M0711: Numerical Mathematics II |
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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: Core Qualification: Elective Compulsory |
Course L0568: Numerical Mathematics II |
Typ | Lecture |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Sabine Le Borne, Dr. Jens-Peter Zemke |
Language | DE/EN |
Cycle | SoSe |
Content |
|
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. Jens-Peter Zemke |
Language | DE/EN |
Cycle | SoSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M1552: Advanced Machine Learning |
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Courses | ||||||||||||
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Module Responsible | Dr. Jens-Peter 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 state-of-the-art neural networks and their corresponding mathematical basics. They can assess the difficulties of different neural networks. |
Skills | Students are able to implement, understand, and, tailored to the field of application, apply neural networks. |
Personal Competence | |
Social Competence |
Students can
|
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 Computer Science in 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: Advanced Machine Learning |
Typ | Lecture |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Dr. Jens-Peter Zemke |
Language | DE/EN |
Cycle | WiSe |
Content |
|
Literature |
|
Course L2323: Advanced Machine Learning |
Typ | Recitation Section (small) |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Dr. Jens-Peter Zemke |
Language | DE/EN |
Cycle | WiSe |
Content | See interlocking course |
Literature | See interlocking course |
Specialization IV. Subject Specific Focus
Module M1434: Technical Complementary Course I for Computational Science and Engineering |
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Courses | ||||
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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 | |
Skills | |
Personal Competence | |
Social Competence | |
Autonomy | |
Workload in Hours | Depends on choice of courses |
Credit points | 12 |
Assignment for the Following Curricula |
Computational Science and Engineering: Specialisation IV. Subject Specific Focus: Elective Compulsory |
Module M1435: Technical Complementary Course II for Computational Science and Engineering |
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Courses | ||||
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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 | Depends on choice of courses |
Credit points | 12 |
Assignment for the Following Curricula |
Computer Science in Engineering: Specialisation IV. Subject Specific Focus: Elective Compulsory |
Thesis
Module M-002: Master Thesis |
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Courses | ||||
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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 Systems: Thesis: Compulsory Environmental Engineering: Thesis: Compulsory Aircraft Systems Engineering: Thesis: Compulsory Global Innovation Management: Thesis: Compulsory Computer Science in Engineering: Thesis: Compulsory Information and Communication Systems: Thesis: Compulsory Interdisciplinary Mathematics: Thesis: Compulsory International Production Management: 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 |