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: 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
|
Skills |
Professional Competence (Skills) In selected sub-areas students can
|
Personal Competence | |
Social Competence |
Personal Competences (Social Skills) Students will be able
|
Autonomy |
Personal Competences (Self-reliance) 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. Karl-Heinz Zimmermann |
Admission Requirements | None |
Recommended Previous Knowledge |
Basic knowledge and techniques from the Master courses in the semesters 1 and 2. |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
Students are able to acquire advanced knowledge in a subfield of Computer Science and can independently acquire deeper knowledge in the field. |
Skills |
The students are able to formulate the scientific problems to be considered and to work out solutions in an independent manner and to realize them. |
Personal Competence | |
Social Competence |
The students are able to discuss proposals for solutions of scientific problems within the team. They are able to present the results in a clear and well structured manner. |
Autonomy |
The students can provide a scientific work in a timely manner and document the results in a detailed and well readable form. They are able to actively follow anticipate the presentations of other students such that eventually a scientific discussion comes up. |
Workload in Hours | Independent Study Time 248, Study Time in Lecture 112 |
Credit points | 12 |
Course achievement | None |
Examination | Study work |
Examination duration and scale | Vortrag |
Assignment for the Following Curricula |
Computer Science: Core Qualification: Compulsory |
Course L2353: Research Project Computer Science |
Typ | Projection Course |
Hrs/wk | 8 |
CP | 12 |
Workload in Hours | Independent Study Time 248, Study Time in Lecture 112 |
Lecturer | Prof. Karl-Heinz Zimmermann |
Language | DE/EN |
Cycle | WiSe |
Content | |
Literature |
Specialization I. Computer and Software Engineering
Module M0753: Software Verification |
||||||||||||
Courses | ||||||||||||
|
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 logic-based models that properly abstract from the software under verification and, where necessary, adapt model or property. They construct proofs and property checks by hand or using tools for model checking or deductive verification, and reflect on the scope of the results. Presented with a verification problem in natural language, they select the appropriate verification technique and justify their choice. |
||||||||
Personal Competence | |||||||||
Social Competence |
Students discuss relevant topics in class. They defend their solutions orally. They communicate in English. |
||||||||
Autonomy |
Using accompanying on-line material for self study, students can assess their level of knowledge continuously and adjust it appropriately. Working on exercise problems, they receive additional feedback. Within limits, they can set their own learning goals. Upon successful completion, students can identify and precisely formulate new problems in academic or applied research in the field of software verification. Within this field, they can conduct independent studies to acquire the necessary competencies and compile their findings in academic reports. They can devise plans to arrive at new solutions or assess existing ones. |
||||||||
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 | ||||||||
Credit points | 6 | ||||||||
Course achievement |
|
||||||||
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 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 M0942: Software Security |
||||||||||||
Courses | ||||||||||||
|
Module Responsible | Prof. Dieter Gollmann |
Admission Requirements | None |
Recommended Previous Knowledge | Familiarity with C/C++, web programming |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
Students can
|
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. Dieter Gollmann |
Language | EN |
Cycle | WiSe |
Content |
|
Literature |
M. Howard, D. LeBlanc: Writing Secure Code, 2nd edition, Microsoft Press (2002) G. Hoglund, G. McGraw: Exploiting Software, Addison-Wesley (2004) L. Gong, G. Ellison, M. Dageforde: Inside Java 2 Platform Security, 2nd edition, Addison-Wesley (2003) B. LaMacchia, S. Lange, M. Lyons, R. Martin, K. T. Price: .NET Framework Security, Addison-Wesley Professional (2002) D. Gollmann: Computer Security, 3rd edition (2011) |
Course L1104: Software Security |
Typ | Recitation Section (small) |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Dieter Gollmann |
Language | EN |
Cycle | WiSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M1400: Design of Dependable Systems |
||||||||||||
Courses | ||||||||||||
|
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 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 |
|
||||||||
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:
|
Literature |
Course L2001: Designing Dependable Systems |
Typ | Recitation Section (small) |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Görschwin Fey |
Language | DE/EN |
Cycle | SoSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M1248: Compilers for Embedded Systems |
||||||||||||
Courses | ||||||||||||
|
Module Responsible | Prof. Heiko Falk |
Admission Requirements | None |
Recommended Previous Knowledge |
Module "Embedded Systems" C/C++ Programming skills |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
The relevance of embedded systems increases from year to year. Within such systems, the amount of software to be executed on embedded processors grows continuously due to its lower costs and higher flexibility. Because of the particular application areas of embedded systems, highly optimized and application-specific processors are deployed. Such highly specialized processors impose high demands on compilers which have to generate code of highest quality. After the successful attendance of this course, the students are able
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 worst-case execution time, energy dissipation, code size), the students learn to evaluate the influence of optimizations on these different criteria. |
Skills |
After successful completion of the course, students shall be able to translate high-level program code into machine code. They will be enabled to assess which kind of code optimization should be applied most effectively at which abstraction level (e.g., source or assembly code) within a compiler. While attending the labs, the students will learn to implement a fully functional compiler including optimizations. |
Personal Competence | |
Social Competence |
Students are able to solve similar problems alone or in a group and to present the results accordingly. |
Autonomy |
Students are able to acquire new knowledge from specific literature and to associate this knowledge with other classes. |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Course achievement | None |
Examination | Oral exam |
Examination duration and scale | 30 min |
Assignment for the Following Curricula |
Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Aircraft Systems Engineering: Specialisation Avionic Systems: Elective Compulsory Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory Mechatronics: Specialisation System Design: Elective Compulsory Mechatronics: Technical Complementary Course: Elective Compulsory Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: Elective Compulsory |
Course L1692: Compilers for Embedded Systems |
Typ | Lecture |
Hrs/wk | 3 |
CP | 4 |
Workload in Hours | Independent Study Time 78, Study Time in Lecture 42 |
Lecturer | Prof. Heiko Falk |
Language | DE/EN |
Cycle | SoSe |
Content |
|
Literature |
|
Course L1693: Compilers for Embedded Systems |
Typ | Project-/problem-based Learning |
Hrs/wk | 1 |
CP | 2 |
Workload in Hours | Independent Study Time 46, Study Time in Lecture 14 |
Lecturer | Prof. Heiko Falk |
Language | DE/EN |
Cycle | SoSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M1397: Model Checking - Proof Engines and Algorithms |
||||||||||||
Courses | ||||||||||||
|
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 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 |
|
||||||||
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 re-test scenarios. They write and analyze test specifications. They apply bug finding techniques for non-trivial problems. |
Personal Competence | |
Social Competence |
Students discuss relevant topics in class. They defend their solutions orally. |
Autonomy |
Students can assess their level of knowledge continuously and adjust it appropriately, based on feedback and on self-guided studies. Within limits, they can set their own learning goals. Upon successful completion, students can identify and precisely formulate new problems in academic or applied research in the field of software testing. Within this field, they can conduct independent studies to acquire the necessary competencies and compile their findings in academic reports. They can devise plans to arrive at new solutions or assess existing ones |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Course achievement | None |
Examination | Subject theoretical and practical work |
Examination duration and scale | Software |
Assignment for the Following Curricula |
Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems, Focus Software: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Software and Signal Processing: Elective Compulsory |
Course L1791: Software Testing |
Typ | Lecture |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Sibylle Schupp |
Language | EN |
Cycle | SoSe |
Content |
|
Literature |
|
Course L1792: Software Testing |
Typ | Project-/problem-based Learning |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Sibylle Schupp |
Language | EN |
Cycle | SoSe |
Content |
|
Literature |
|
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 |
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 mini-projects which will eventually evolve into a final project. Learning computer graphics and animation resembles learning a musical instrument. Therefore, doing your projects well and in time is essential for performing well on this course. |
Literature |
Alan H. Watt: 3D Computer Graphics. Harlow: Pearson (3rd ed., repr., 2009). Dariush Derakhshani: Introducing Autodesk Maya 2014. New York, NY : Wiley (2013). |
Course L0768: Computer Graphics |
Typ | Recitation Section (small) |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Tobias Knopp |
Language | EN |
Cycle | SoSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M0924: Software for Embedded Systems |
||||||||||||
Courses | ||||||||||||
|
Module Responsible | Prof. Bernd-Christian Renner |
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 basic principles and procedures of software engineering for embedded systems. They are able to describe the usage and pros of event based programming using interrupts. They know the components and functions of a concrete microcontroller. The participants explain requirements of real time systems. They know at least three scheduling algorithms for real time operating systems including their pros and cons. |
Skills | Students build interrupt-based programs for a concrete microcontroller. They build and use a preemptive scheduler. They use peripheral components (timer, ADC, EEPROM) to realize complex tasks for embedded systems. To interface with external components they utilize serial protocols. |
Personal Competence | |
Social Competence | |
Autonomy | |
Workload in Hours | Independent Study Time 110, Study Time in Lecture 70 |
Credit points | 6 |
Course achievement | None |
Examination | Written exam |
Examination duration and scale | 90 min |
Assignment for the Following Curricula |
Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Software and Signal Processing: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems, Focus Software: Elective Compulsory International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory Mechatronics: Technical Complementary Course: Elective Compulsory Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory Mechatronics: Specialisation System Design: Elective Compulsory Microelectronics and Microsystems: Specialisation Embedded Systems: Elective Compulsory Microelectronics and Microsystems: Specialisation Embedded Systems: Elective Compulsory |
Course L1069: Software for Embdedded Systems |
Typ | Lecture |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Bernd-Christian Renner |
Language | DE/EN |
Cycle | SoSe |
Content |
|
Literature |
|
Course L1070: Software for Embdedded Systems |
Typ | Recitation Section (small) |
Hrs/wk | 3 |
CP | 3 |
Workload in Hours | Independent Study Time 48, Study Time in Lecture 42 |
Lecturer | Prof. Bernd-Christian Renner |
Language | DE/EN |
Cycle | SoSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M1427: Algorithmic Game Theory |
||||||||||||
Courses | ||||||||||||
|
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 M0839: Traffic Engineering |
||||||||||||||||
Courses | ||||||||||||||||
|
Module Responsible | Prof. Andreas Timm-Giel |
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 Timm-Giel, Dr. Phuong Nga Tran |
Language | EN |
Cycle | WiSe |
Content | Selected applications of methods for planning, optimization, and performance evaluation of communication networks, which have been introduced in the traffic engineering lecture are prepared by the students and presented in a seminar. |
Literature |
|
Course L0900: Traffic Engineering |
Typ | Lecture |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Prof. Andreas Timm-Giel, Dr. Phuong Nga Tran |
Language | EN |
Cycle | WiSe |
Content |
Network Planning and Optimization |
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 Timm-Giel |
Language | EN |
Cycle | WiSe |
Content |
Accompanying exercise for the traffic engineering course |
Literature |
Literatur: |
Module M0910: Advanced System-on-Chip 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 in-depth, hands-on experience on advanced concepts of computer architecture. Using the Hardware Description Language VHDL and using reconfigurable FPGA hardware boards, students learn how to design complex computer systems (so-called systems-on-chip, SoCs), that are commonly found in the domain of embedded systems, in actual hardware. Starting with a simple processor architecture, the students learn to how realize instruction-processing of a computer processor according to the principle of pipelining. They implement different styles of cache-based memory hierarchies, examine strategies for dynamic scheduling of machine instructions and for branch prediction, and finally construct a complex MPSoC system (multi-processor system-on-chip) that consists of multiple processor cores that are connected via a shared bus. |
Skills |
Students will be able to analyze, how highly specific and individual computer systems can be constructed using a library of given standard components. They evaluate the interferences between the physical structure of a computer system and the software executed thereon. This way, they will be enabled to estimate the effects of design decision at the hardware level on the performance of the entire system, to evaluate the whole and complex system and to propose design options to improve a system. |
Personal Competence | |
Social Competence |
Students are able to solve similar problems alone or in a group and to present the results accordingly. |
Autonomy |
Students are able to acquire new knowledge from specific literature, to transform this knowledge into actual implementations of complex hardware structures, and to associate this knowledge with contents of other classes. |
Workload in Hours | Independent Study Time 138, Study Time in Lecture 42 |
Credit points | 6 |
Course achievement | None |
Examination | Subject theoretical and practical work |
Examination duration and scale | VHDL Codes and FPGA-based implementations |
Assignment for the Following Curricula |
Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory Microelectronics and Microsystems: Specialisation Embedded Systems: Elective Compulsory |
Course L1061: Advanced System-on-Chip Design |
Typ | Project-/problem-based Learning |
Hrs/wk | 3 |
CP | 6 |
Workload in Hours | Independent Study Time 138, Study Time in Lecture 42 |
Lecturer | Prof. Heiko Falk |
Language | DE/EN |
Cycle | WiSe |
Content |
|
Literature |
|
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 | Oral exam |
Examination duration and scale | 25 min |
Assignment for the Following Curricula |
Computer Science: Specialisation I. Computer and Software Engineering: Elective Compulsory |
Course L2815: Operating System Techniques |
Typ | Lecture |
Hrs/wk | 1 |
CP | 2 |
Workload in Hours | Independent Study Time 46, Study Time in Lecture 14 |
Lecturer | Prof. Christian Dietrich |
Language | DE |
Cycle | WiSe |
Content | |
Literature |
Course L2816: Operating System Techniques |
Typ | Project-/problem-based Learning |
Hrs/wk | 3 |
CP | 4 |
Workload in Hours | Independent Study Time 78, Study Time in Lecture 42 |
Lecturer | Prof. Christian Dietrich |
Language | DE |
Cycle | WiSe |
Content | |
Literature |
Module M1741: Operating System Construction |
||||||||||||||||
Courses | ||||||||||||||||
|
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-/problem-based Learning |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Prof. Christian Dietrich |
Language | DE |
Cycle | SoSe |
Content | |
Literature |
Course L2813: Operating System Construction |
Typ | Recitation Section (large) |
Hrs/wk | 1 |
CP | 1 |
Workload in Hours | Independent Study Time 16, Study Time in Lecture 14 |
Lecturer | Prof. Christian Dietrich |
Language | DE |
Cycle | SoSe |
Content | |
Literature |
Specialization II: Intelligence Engineering
Module M0633: Industrial Process Automation |
||||||||||||
Courses | ||||||||||||
|
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: 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. Rolf-Rainer Grigat |
Admission Requirements | None |
Recommended Previous Knowledge |
System theory of one-dimensional signals (convolution and correlation, sampling theory, interpolation and decimation, Fourier transform, linear time-invariant systems), linear algebra (Eigenvalue decomposition, SVD), basic stochastics and statistics (expectation values, influence of sample size, correlation and covariance, normal distribution and its parameters), basics of Matlab, basics in optics |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
Students can
|
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 decision-making areas. Students can undertake a prototypical analysis of processes in Matlab. |
Personal Competence | |
Social Competence |
k.A. |
Autonomy |
Students can solve image analysis tasks independently using the relevant literature. |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Course achievement | None |
Examination | Written exam |
Examination duration and scale | 60 Minutes, Content of Lecture and materials in StudIP |
Assignment for the Following Curricula |
Computer Science: Specialisation II: Intelligence Engineering: Elective Compulsory Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Electrical Engineering: Specialisation Medical Technology: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems, Focus Signal Processing: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Software and Signal Processing: Elective Compulsory International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory Microelectronics and Microsystems: Specialisation Communication and Signal Processing: Elective Compulsory Microelectronics and Microsystems: Specialisation Communication and Signal Processing: Elective Compulsory Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Numerics and Computer Science: Elective Compulsory |
Course L0126: Digital Image Analysis |
Typ | Lecture |
Hrs/wk | 4 |
CP | 6 |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Lecturer | Prof. Rolf-Rainer Grigat |
Language | EN |
Cycle | WiSe |
Content |
|
Literature |
Bredies/Lorenz, Mathematische Bildverarbeitung, Vieweg, 2011 |
Module M1336: Soft Computing - Introduction to Machine Learning |
||||||||
Courses | ||||||||
|
Module Responsible | Prof. Karl-Heinz Zimmermann |
Admission Requirements | None |
Recommended Previous Knowledge |
Bachelor in Computer Science. Basics in higher mathematics are inevitable, like calculus, linear algebra, graph theory, and optimization. |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
Students are able to formalize, compute, and analyze belief networks, alignments of sequences, hidden Markov models, phylogenetic tree models, classical regression and clustering methods, neural networks, and fuzzy controllers. |
Skills | Students can apply the relevant algorithms and determine their complexity, and they can make use of the statistics language R. |
Personal Competence | |
Social Competence |
Students are able to solve specific problems alone or in a group and to present the results accordingly. |
Autonomy |
Students are able to acquire new knowledge from newer literature and to associate the acquired knowledge to other fields. |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Course achievement | None |
Examination | Oral exam |
Examination duration and scale | 25 min |
Assignment for the Following Curricula |
Computer Science: Specialisation II: Intelligence Engineering: Elective Compulsory International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory Mechatronics: Specialisation System Design: Elective Compulsory Mechatronics: Technical Complementary Course: Elective Compulsory Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Numerics and Computer Science: Elective Compulsory |
Course L1869: Soft Computing - Introduction to Machine Learning |
Typ | Lecture |
Hrs/wk | 4 |
CP | 6 |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Lecturer | Prof. Karl-Heinz Zimmermann, Dr. Mehwish Saleemi |
Language | DE/EN |
Cycle | WiSe |
Content |
Students are able to formalize, compute, and analyze belief
networks, alignments of sequences, hidden Markov models, phylogenetic
tree models, neural networks, and fuzzy controllers. In particular,
inference and learning in belief networks are important
topics that the students should be able to master. Students can apply the relevant algorithms and determine their complexity, and they can make use of the statistics language R. |
Literature |
1. David Barber, Bayes Reasoning and Machine Learning, Cambridge Univ. Press, Cambridge, 2012. |
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 real-world scenarios, students can summarize how Bayesian networks can be employed as a knowledge representation and reasoning formalism in static and dynamic settings. In addition, students can define decision making procedures in simple and sequential settings, with and with complete access to the state of the environment. In this context, students can describe techniques for solving (partially observable) Markov decision problems, and they can recall techniques for measuring the value of information. Students can identify techniques for simultaneous localization and mapping, and can explain planning techniques for achieving desired states. Students can explain coordination problems and decision making in a multi-agent setting in term of different types of equilibria, social choice functions, voting protocol, and mechanism design techniques. |
Skills |
Students can select an appropriate agent architecture for concrete agent application scenarios. For simplified agent application students can derive decision trees and apply basic optimization techniques. For those applications they can also create Bayesian networks/dynamic Bayesian networks and apply bayesian reasoning for simple queries. Students can also name and apply different sampling techniques for simplified agent scenarios. For simple and complex decision making students can compute the best action or policies for concrete settings. In multi-agent situations students will apply techniques for finding different equilibria states,e.g., Nash equilibria. For multi-agent decision making students will apply different voting protocols and compare and explain the results. |
Personal Competence | |
Social Competence |
Students are able to discuss their solutions to problems with others. They communicate in English |
Autonomy |
Students are able of checking their understanding of complex concepts by solving varaints of concrete problems |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Course achievement | None |
Examination | Written exam |
Examination duration and scale | 90 minutes |
Assignment for the Following Curricula |
Computer Science: Specialisation II: Intelligence Engineering: Elective Compulsory International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory Mechatronics: Technical Complementary Course: Elective Compulsory Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory Biomedical Engineering: Specialisation Artificial Organs and Regenerative Medicine: Elective Compulsory Biomedical Engineering: Specialisation Implants and Endoprostheses: Elective Compulsory Biomedical Engineering: Specialisation Medical Technology and Control Theory: Elective Compulsory Biomedical Engineering: Specialisation Management and Business Administration: Elective Compulsory Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Numerics and Computer Science: Elective Compulsory |
Course L0341: Intelligent Autonomous Agents and Cognitive Robotics |
Typ | Lecture |
Hrs/wk | 2 |
CP | 4 |
Workload in Hours | Independent Study Time 92, Study Time in Lecture 28 |
Lecturer | Rainer Marrone |
Language | EN |
Cycle | WiSe |
Content |
|
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 M0551: Pattern Recognition and Data Compression |
||||||||
Courses | ||||||||
|
Module Responsible | Prof. Rolf-Rainer Grigat |
Admission Requirements | None |
Recommended Previous Knowledge |
Linear algebra (including PCA, unitary transforms), stochastics and statistics, binary arithmetics |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
Students can name the basic concepts of pattern recognition and data compression. Students are able to discuss logical connections between the concepts covered in the course and to explain them by means of examples. |
Skills |
Students can apply statistical methods to classification problems in pattern recognition and to prediction in data compression. On a sound theoretical and methodical basis they can analyze characteristic value assignments and classifications and describe data compression and video signal coding. They are able to use highly sophisticated methods and processes of the subject area. Students are capable of assessing different solution approaches in multidimensional decision-making areas. |
Personal Competence | |
Social Competence |
k.A. |
Autonomy |
Students are capable of identifying problems independently and of solving them scientifically, using the methods they have learnt. |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Course achievement | None |
Examination | Written exam |
Examination duration and scale | 60 Minutes, Content of Lecture and materials in StudIP |
Assignment for the Following Curricula |
Computer Science: Specialisation II: Intelligence Engineering: Elective Compulsory Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Software and Signal Processing: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems, Focus Signal Processing: Elective Compulsory International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory International Management and Engineering: Specialisation II. Electrical Engineering: Elective Compulsory Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory Mechatronics: Technical Complementary Course: Elective Compulsory Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: Elective Compulsory |
Course L0128: Pattern Recognition and Data Compression |
Typ | Lecture |
Hrs/wk | 4 |
CP | 6 |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Lecturer | Prof. Rolf-Rainer Grigat |
Language | EN |
Cycle | SoSe |
Content |
Structure of a pattern recognition system, statistical decision theory, classification based on statistical models, polynomial regression, dimension reduction, multilayer perceptron regression, radial basis functions, support vector machines, unsupervised learning and clustering, algorithm-independent machine learning, mixture models and EM, adaptive basis function models and boosting, Markov random fields Information, entropy, redundancy, mutual information, Markov processes, basic coding schemes (code length, run length coding, prefix-free codes), entropy coding (Huffman, arithmetic coding), dictionary coding (LZ77/Deflate/LZMA2, LZ78/LZW), prediction, DPCM, CALIC, quantization (scalar and vector quantization), transform coding, prediction, decorrelation (DPCM, DCT, hybrid DCT, JPEG, JPEG-LS), motion estimation, subband coding, wavelets, HEVC (H.265,MPEG-H) |
Literature |
Schürmann: Pattern Classification, Wiley 1996 Salomon, Data Compression, the Complete Reference, Springer, 2000 |
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 instance-based and model-based learning approaches, and they can enumerate basic machine learning technique for each of the two basic approaches, either on the basis of static data, or on the basis of incrementally incoming data . For dealing with uncertainty, students can describe suitable representation formalisms, and they explain how axioms, features, parameters, or structures used in these formalisms can be learned automatically with different algorithms. Students are also able to sketch different clustering techniques. They depict how the performance of learned classifiers can be improved by ensemble learning, and they can summarize how this influences computational learning theory. Algorithms for reinforcement learning can also be explained by students. |
Skills |
Student derive decision trees and, in turn, propositional rule sets from simple and static data tables and are able to name and explain basic optimization techniques. They present and apply the basic idea of first-order inductive leaning. Students apply the BME, MAP, ML, and EM algorithms for learning parameters of Bayesian networks and compare the different algorithms. They also know how to carry out Gaussian mixture learning. They can contrast kNN classifiers, neural networks, and support vector machines, and name their basic application areas and algorithmic properties. Students can describe basic clustering techniques and explain the basic components of those techniques. Students compare related machine learning techniques, e.g., k-means clustering and nearest neighbor classification. They can distinguish various ensemble learning techniques and compare the different goals of those techniques. |
Personal Competence | |
Social Competence | |
Autonomy | |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Course achievement | None |
Examination | Written exam |
Examination duration and scale | 90 minutes |
Assignment for the Following Curricula |
Computer Science: Specialisation II: Intelligence Engineering: Elective Compulsory International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory Mechatronics: Technical Complementary Course: Elective Compulsory Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory Mechatronics: Specialisation System Design: Elective Compulsory Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: Elective Compulsory |
Course L0340: Machine Learning and Data Mining |
Typ | Lecture |
Hrs/wk | 2 |
CP | 4 |
Workload in Hours | Independent Study Time 92, Study Time in Lecture 28 |
Lecturer | Rainer Marrone |
Language | EN |
Cycle | SoSe |
Content |
|
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: 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 | 5-10 pages |
Assignment for the Following Curricula |
Computer Science: Specialisation II: Intelligence Engineering: Elective Compulsory Electrical Engineering: Specialisation Control and Power Systems Engineering: Elective Compulsory Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Bio- and Medical Technology: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: Elective Compulsory |
Course L1794: Applied Humanoid Robotics |
Typ | Project-/problem-based Learning |
Hrs/wk | 6 |
CP | 6 |
Workload in Hours | Independent Study Time 96, Study Time in Lecture 84 |
Lecturer | Patrick Göttsch |
Language | DE/EN |
Cycle |
WiSe/ |
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 |
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 I-III (Real analysis,computing in Vector spaces , principle of complete induction) Diskrete Mathematik I (gropus, rings, ideals, fields; euclidean algorithm) |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
Students can discuss logical connections between the following concepts and explain them by means of examples: Smith normal form, Chinese remainder theorem, grid point sets, integer solution of inequality systems. |
Skills |
Students are able to access independently further logical connections between the concepts with which they have become familiar and are able to verify them. Students are able to develop a suitable solution approach to given problems, to pursue it and to evaluate the results critically, such as in solving multivariate equation systems and in grid point theory. |
Personal Competence | |
Social Competence | |
Autonomy | |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Course achievement | None |
Examination | Oral exam |
Examination duration and scale | 30 min |
Assignment for the Following Curricula |
Computer Science: Specialisation III. Mathematics: Elective Compulsory |
Course L0422: Algorithmic Algebra |
Typ | Lecture | ||||||||||||||
Hrs/wk | 3 | ||||||||||||||
CP | 5 | ||||||||||||||
Workload in Hours | Independent Study Time 108, Study Time in Lecture 42 | ||||||||||||||
Lecturer | Dr. Prashant Batra | ||||||||||||||
Language | DE | ||||||||||||||
Cycle | WiSe | ||||||||||||||
Content |
Extended euclidean algorithm, solution of the Bezout-equation Division with remainder (over rings) fast arithmetic algorithms (conversion, fast multiplications) discrete Fourier-transformation over rings Computation with modular remainders, solving of remainder systems (chinese remainder theorem), solvability of integer linear systems over the integers linearization of polynomial equations-- matrix approach Sylvester-matrix, elimination elimination in rings, elimination of many variables Buchberger algorithm, Gröbner basis Minkowskis Lattice Point theorem and integer-valued optimization LLL-algorithm for construction of 'short' lattice vectors in polynomial time |
||||||||||||||
Literature |
von zur Gathen, Joachim; Gerhard, Jürgen Modern computer algebra. 3rd ed. (English) Zbl 1277.68002 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/10.1007/978-0-387-35651-8
Koepf, Wolfram springer eBook: http://dx.doi.org/10.1007/3-540-29895-9 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 Mathematical Modelling in Engineering: Theory, Numerics, Applications: Specialisation ll. Modelling and Simulation of Complex Systems (TUHH): Elective Compulsory Technomathematics: Specialisation I. Mathematics: Elective Compulsory Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Numerics and Computer Science: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Simulation Technology: Elective Compulsory |
Course L0585: Hierarchical Algorithms |
Typ | Lecture |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Sabine Le Borne |
Language | DE/EN |
Cycle | WiSe |
Content |
|
Literature | W. Hackbusch: Hierarchische Matrizen: Algorithmen und Analysis |
Course L0586: Hierarchical Algorithms |
Typ | Recitation Section (small) |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Sabine Le Borne |
Language | DE/EN |
Cycle | WiSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M1337: Curves, Cryptosystems and Quantum Computing |
||||||||
Courses | ||||||||
|
Module Responsible | Prof. Karl-Heinz Zimmermann |
Admission Requirements | None |
Recommended Previous Knowledge |
Higher algebra, linear algebra, and mathematical analysis. |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge | The students understand the basic theory of elliptic curves, classical cryptosysteme, basic methods of cryptanalysis, cryptography of elliptic curves, quantum computing and the post-quantum computing scenario. |
Skills | The students are in the position to apply the group law of elliptic curves, to find out if a curve is non-singular, to sketch cryptographic algorithms that make use of elliptic curves and to specify quantum algorithms. |
Personal Competence | |
Social Competence |
Students are able to solve specific problems alone or in a group and to present the results accordingly. |
Autonomy |
Students are able to acquire new knowledge from specific standard books and to associate the acquired knowledge to other classes. |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Course achievement | None |
Examination | Oral exam |
Examination duration and scale | 25 min |
Assignment for the Following Curricula |
Computer Science: Specialisation III. Mathematics: Elective Compulsory |
Course L1870: Curves, Cryptosystems and Quantum Computing |
Typ | Lecture |
Hrs/wk | 4 |
CP | 6 |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Lecturer | Prof. Karl-Heinz Zimmermann |
Language | DE/EN |
Cycle | SoSe |
Content | |
Literature |
Module M1310: Discrete Differential Geometry |
||||||||
Courses | ||||||||
|
Module Responsible | Prof. Karl-Heinz Zimmermann |
Admission Requirements | None |
Recommended Previous Knowledge |
Linear Algebra, Multivariate Calculus |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
These lectures are on geometrical aspects of the solutions of differential equations and their treatment on the computer. The required basics from linear algebra and analysis are reviewed at the beginning. Applications are to curved surfaces in space, to mechanics and mechatronics, to different types of field equations, and to the tranfer of mathematical constructions to data types, compiler functions, programming languages, and special compute circuits. - basic prerequisites from linear algebra, tensors, exterior algebra, Clifford algebras - basic prerequisites from coordinate-free analysis, vector fields and differential forms, integration, discretization - local differential geometry: connections, symplectic geometry and Hamiltonian systems, Riemannian geometry, discretization - global differential geometry: manifolds, Lie groups, fiber bundles, random processes, space and time |
Skills | |
Personal Competence | |
Social Competence | |
Autonomy | |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Course achievement | None |
Examination | Oral exam |
Examination duration and scale | 25 min |
Assignment for the Following Curricula |
Computer Science: Specialisation III. Mathematics: Elective Compulsory |
Course L1808: Discrete Differential Geometry |
Typ | Lecture |
Hrs/wk | 4 |
CP | 6 |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Lecturer | Prof. Georg Friedrich Mayer-Lindenberg |
Language | DE/EN |
Cycle | SoSe |
Content |
These lectures deal with geometric aspects of differential equations and with their treatment on the computer. The prerequisites from linear algebra and analysis are reviewed at the beginning. Applications are to curved surfaces, to classical mechanics and mechatronics, to various field equations, to computer graphics and to transferring mathematical constructions to data types, compiler functions, programming languages, and special hardware. Keywords: Basics from linear algebra, tensors, exterior algebra, Clifford algebras, tuple types Basics of coordinate-free analysis, vector fields and differential forms, integration, discrete exterior calculus Local differential geometry: connections, symplectic geometry, Riemannian geometry, discrete mechanics and connections Global differential geometry: manifolds, Lie groups, fibre bundles, Fourier decompositions, random processes, space and time |
Literature |
Agricola, Friedrich, Vektoranalysis, Vieweg/Teubner 2010 A.C. Da Silva, Lectures on Symplectic Geometry, Springer L.N. Math. 1764 J. Snygg, Differential Geometry using Clifford's Algebra, Birkhäuser 2010 T. Frankel, The Geometry of Physics, Cambridge U. P. 2012 M.Desbrun et al., Discrete exterior calculus, arXiv:math/0508341v2 J.Marsden et al., Discrete Mechanics and Variational Integrators, Acta numerica. 2001 |
Module M1405: Randomised Algorithms and Random Graphs |
||||||||||||
Courses | ||||||||||||
|
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 Mathematical Modelling in Engineering: Theory, Numerics, Applications: Specialisation l. Numerics (TUHH): Elective Compulsory |
Course L2010: Randomised Algorithms and Random Graphs |
Typ | Lecture |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Anusch Taraz, Prof. Volker Turau |
Language | DE/EN |
Cycle | SoSe |
Content |
Randomized Algorithms:
|
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 |
||||||||||||
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. 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 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: Specialisation Aircraft Systems: Elective Compulsory Mathematical Modelling in Engineering: Theory, Numerics, Applications: Specialisation l. Numerics (TUHH): Compulsory Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory Technomathematics: Specialisation I. Mathematics: Elective Compulsory Theoretical Mechanical Engineering: Core Qualification: Compulsory Process Engineering: Specialisation Chemical Process Engineering: Elective Compulsory Process Engineering: Specialisation Process Engineering: Elective Compulsory |
Course L0576: Numerical Treatment of Ordinary Differential Equations |
Typ | Lecture |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Daniel Ruprecht |
Language | DE/EN |
Cycle | SoSe |
Content |
Numerical methods for Initial Value Problems
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 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: 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. 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 Computational Science and Engineering: Specialisation III. Mathematics: Elective Compulsory Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory Mechatronics: Technical Complementary Course: Elective Compulsory Technomathematics: Specialisation I. Mathematics: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Robotics and Computer Science: Elective Compulsory |
Course L2322: Mathematics of Neural Networks |
Typ | Lecture |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Dr. Jens-Peter Zemke |
Language | DE/EN |
Cycle | WiSe |
Content |
|
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. Jens-Peter 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: 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. Karl-Heinz Zimmermann |
Admission Requirements | None |
Recommended Previous Knowledge | |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge | |
Skills | |
Personal Competence | |
Social Competence | |
Autonomy | |
Workload in Hours | Depends on choice of courses |
Credit points | 6 |
Assignment for the Following Curricula |
Computer Science: Specialisation IV. Subject Specific Focus: Elective Compulsory |
Module M1566: Technical Complementary Course II for CSMS |
||||
Courses | ||||
|
Module Responsible | Prof. Karl-Heinz Zimmermann |
Admission Requirements | None |
Recommended Previous Knowledge | |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge | |
Skills | |
Personal Competence | |
Social Competence | |
Autonomy | |
Workload in Hours | Depends on choice of courses |
Credit points | 6 |
Assignment for the Following Curricula |
Computer Science: Specialisation IV. Subject Specific Focus: Elective Compulsory |
Module M1564: Advanced Seminars Computer Science |
||||||||||||
Courses | ||||||||||||
|
Module Responsible | Prof. Karl-Heinz Zimmermann |
Admission Requirements | None |
Recommended Previous Knowledge |
Basic knowledge of Computer Science and Mathematics at the Master's level. |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
The students are able to
|
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: Specialisation IV. Subject Specific Focus: Elective Compulsory |
Course L2352: Advanced Seminar Computer Science I |
Typ | Seminar |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Karl-Heinz Zimmermann |
Language | DE/EN |
Cycle |
WiSe/ |
Content | |
Literature |
Course L2429: Introductory Seminar Computer Science II |
Typ | Seminar |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Karl-Heinz Zimmermann |
Language | DE/EN |
Cycle |
WiSe/ |
Content | |
Literature |
Thesis
Module M-002: 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 |