Module Manual
Master
Computer Science
Cohort: Winter Term 2015
Updated: 31st May 2017
Content
Module M0523: Business & Management |
Module Responsible | Prof. Matthias Meyer |
Admission Requirements | None |
Recommended Previous Knowledge | None |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
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Skills |
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Personal Competence | |
Social Competence | |
Autonomy |
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Workload in Hours | Depends on choice of courses |
Credit points | 6 |
Courses |
Information regarding lectures and courses can be found in the corresponding module handbook published separately. |
Module M0524: Nontechnical Elective Complementary 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 Non-technical Elective Study Area 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 “non-technical department” 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 and sustainability research, and from engineering didactics. In addition, from the winter semester 2014/15 students on all Bachelor’s courses will have the opportunity to learn about business management and start-ups in a goal-oriented way. The fields of teaching are augmented by soft skills offers and a foreign language offer. Here, the focus is on encouraging goal-oriented communication skills, e.g. the skills required by outgoing engineers in international and intercultural situations. The Competence Level of the courses offered in this area is different as regards the basic training objective in the Bachelor’s and Master’s fields. These differences are reflected in the practical examples used, in content topics that refer to different professional application contexts, and in the higher scientific and theoretical level of abstraction in the B.Sc. This is also reflected in the different quality of soft skills, which relate to the different team positions and different group leadership functions of Bachelor’s and Master’s graduates in their future working life. Specialized Competence (Knowledge) Students can
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Skills |
Professional Competence (Skills) In selected sub-areas students can
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Personal Competence | |
Social Competence |
Personal Competences (Social Skills) Students will be able
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Autonomy |
Personal Competences (Self-reliance) Students are able in selected areas
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Workload in Hours | Depends on choice of courses |
Credit points | 6 |
Courses |
Information regarding lectures and courses can be found in the corresponding module handbook published separately. |
Module M0667: Algorithmic Algebra |
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Courses | ||||||||||||
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Module Responsible | Dr. Prashant Batra |
Admission Requirements | |
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 |
Examination | Oral exam |
Examination duration and scale | |
Assignment for the Following Curricula |
Computer Science: Core qualification: Elective Compulsory Computational Science and Engineering: Specialisation Scientific Computing: 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 |
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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 M0586: Efficient Algorithms |
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Courses | ||||||||||||
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Module Responsible | Prof. Siegfried Rump |
Admission Requirements | None |
Recommended Previous Knowledge |
Programming in Matlab and/or C Basic knowledge in discrete mathematics |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
The students are able to explain the basic theory and methods of network algorithms and in particular their data structures. They are able to analyze the computational behavior and computing time of linear programming algorithms as well network algorithms. Moreover the students can distinguish between efficiently solvable and NP-hard problems. |
Skills |
The students are able to analyze complex tasks and can determine possibilities to transform them into networking algorithms. In particular they can efficiently implement basic algorithms and data structures of LP- and network algorithms and identify possible weaknesses. They are able to distinguish between different efficient data structures and are able to use them appropriately. |
Personal Competence | |
Social Competence |
The students have the skills to solve problems together in small groups and to present the achieved results in an appropriate manner. |
Autonomy |
The students are able to retrieve necessary informations from the given literature and to combine them with the topics of the lecture. Throughout the lecture they can check their abilities and knowledge on the basis of given exercises and test questions providing an aid to optimize their learning process. |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Examination | Oral exam |
Examination duration and scale | 30 minutes |
Assignment for the Following Curricula |
Computer Science: Core qualification: Elective Compulsory Computational Science and Engineering: Core qualification: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Numerics and Computer Science: Elective Compulsory |
Course L0120: Efficient Algorithms |
Typ | Lecture |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Siegfried Rump |
Language | DE |
Cycle | WiSe |
Content |
- Linear Programming - Data structures - Leftist heaps - Minimum spanning tree - Shortest path - Maximum flow - NP-hard problems via max-cut |
Literature |
R. E. Tarjan: Data Structures and Network Algorithms. CBMS 44, Society for Industrial and Applied Mathematics, Philadelphia, PA, 1983. Wesley, 2011 http://algs4.cs.princeton.edu/home/ V. Chvátal, ``Linear Programming'', Freeman, New York, 1983. |
Course L1207: Efficient Algorithms |
Typ | Recitation Section (small) |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Siegfried Rump |
Language | DE |
Cycle | WiSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M0683: Algebraic Statistics for Computational Biology |
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Courses | ||||||||||||
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Module Responsible | Prof. Karl-Heinz Zimmermann |
Admission Requirements | None. |
Recommended Previous Knowledge | Mathematical Calculus, Linear Algebra, and Higher Abstract Algebra. |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge | The students know the alignment of sequences, the hidden Markov model, and phylogenetic tree models including the respective algorithms. Moreover, they know the EM algorithm, general algebraic statistical models and developping invariants for them, Gröbner bases in polynomial rings, elimination theory for systems of polynomial equations, and the use of computer algebra systems to solve problems in the context of this class. |
Skills |
The students are able to formalize, compute, and analyze alignments of sequences, hidden Markov models, and phylogenetic tree models. Moreover, they can compute Gröbner bases in polynomial rings, use elimination theory to tackle systems of polynomial equations, and provide invariants for algebraic statistical models. |
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 this knowledge with other fields. |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Examination | Oral exam |
Examination duration and scale | 30 min |
Assignment for the Following Curricula |
Bioprocess Engineering: Specialisation A - General Bioprocess Engineering: Elective Compulsory Chemical and Bioprocess Engineering: Specialisation Bioprocess Engineering: Elective Compulsory Chemical and Bioprocess Engineering: Specialisation General Process Engineering: Elective Compulsory Computer Science: Core qualification: Elective Compulsory Computational Science and Engineering: Core qualification: Elective Compulsory International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory |
Course L0457: Algebraic Statistics for Computational Biology |
Typ | Recitation Section (small) |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Karl-Heinz Zimmermann |
Language | DE/EN |
Cycle | WiSe |
Content | |
Literature |
Course L0456: Algebraic Statistics for Computational Biology |
Typ | Lecture |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Karl-Heinz Zimmermann |
Language | DE/EN |
Cycle | WiSe |
Content | |
Literature |
Module M0711: Numerical Mathematics II |
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Courses | ||||||||||||
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Module Responsible | Prof. Blanca Ayuso Dios |
Admission Requirements | None |
Recommended Previous Knowledge |
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Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
Students are able to
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Skills |
Students are able to
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Personal Competence | |
Social Competence |
Students are able to
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Autonomy |
Students are capable
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Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Examination | Oral exam |
Examination duration and scale | 30 min |
Assignment for the Following Curricula |
Computer Science: Core qualification: Elective Compulsory Computational Science and Engineering: Core qualification: Elective Compulsory Technomathematics: Specialisation Mathematics: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Numerics and Computer Science: 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. Blanca Ayuso Dios |
Language | DE/EN |
Cycle | WiSe |
Content |
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Literature |
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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. Blanca Ayuso Dios |
Language | DE/EN |
Cycle | WiSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M0582: Nonlinear Optimization |
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Courses | ||||||||||||
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Module Responsible | Dr. Christian Jansson |
Admission Requirements | None |
Recommended Previous Knowledge |
Basic knowledge in mathematics |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge | The students have knowledge of the basic principles of numerical nonlinear optimization. In particular, they know the fundamental criteria for optimality as well as optimization algorithms for finite dimensional and infinite dimensional problems. |
Skills | The students have experience in working with software packages in the area of optimization. The are able to model practical problems in optimization in a flexible manner, and they can judge approximately computed solutions according to the problem. |
Personal Competence | |
Social Competence | The students have the skills to solve problems together in small groups and to present the achieved results in an appropriate manner. |
Autonomy | The students are able to use and to retrieve necessary informations from the given literature. They are able to check their knowledge with the exercises. In this way they can control their learning. |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Examination | Oral exam |
Examination duration and scale | 90 minutes |
Assignment for the Following Curricula |
Computer Science: Core qualification: Elective Compulsory Computational Science and Engineering: Core qualification: Elective Compulsory Mechatronics: Specialisation System Design: Elective Compulsory Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory Product Development, Materials and Production: Core qualification: Elective Compulsory |
Course L0228: Nonlinear Optimization |
Typ | Lecture |
Hrs/wk | 3 |
CP | 4 |
Workload in Hours | Independent Study Time 78, Study Time in Lecture 42 |
Lecturer | Dr. Christian Jansson |
Language | DE |
Cycle | SoSe |
Content |
Introduction
Optimality Conditions
Algorithms for Unconstrained Optimisation Problems
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Literature |
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Course L0229: Nonlinear Optimization |
Typ | Recitation Section (small) |
Hrs/wk | 1 |
CP | 2 |
Workload in Hours | Independent Study Time 46, Study Time in Lecture 14 |
Lecturer | Dr. Christian Jansson |
Language | DE |
Cycle | SoSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M0685: Algebraic Methods in Information and Communication Technology |
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Courses | ||||||||||||
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Module Responsible | Prof. Karl-Heinz Zimmermann |
Admission Requirements |
None. |
Recommended Previous Knowledge |
Mathematical Calculus, Linear Algebra, and foundations of Higher Abstract Algebra. |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
The students will get familiar with the following topics: algebraic combinatorics; ideals, local rings, standard bases and systems of polynomial equations; modules, syzygies, and free resolutions; algebraic invariant theory. These are the core topics. Further topics are elliptic curves, homological algebra, knot theory, and sheaves. |
Skills | The students are able to provide computations in the fields given above. |
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 this knowledge with other fields. |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Examination | Oral exam |
Examination duration and scale | Einzelprüfung, 30 min |
Assignment for the Following Curricula |
Computer Science: Core qualification: Elective Compulsory Computational Science and Engineering: Core qualification: Elective Compulsory |
Course L0461: Algebraic Methods in Information and Communication Technology |
Typ | Lecture |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Karl-Heinz Zimmermann |
Language | DE/EN |
Cycle | SoSe |
Content | |
Literature |
Course L0462: Algebraic Methods in Information and Communication Technology |
Typ | Recitation Section (small) |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Karl-Heinz Zimmermann |
Language | DE/EN |
Cycle | SoSe |
Content | |
Literature |
Module M0804: Research Project and Seminar |
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Courses | ||||||||||||
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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 | Independent Study Time 372, Study Time in Lecture 168 |
Credit points | 18 |
Examination | according to Subject Specific Regulations |
Examination duration and scale | Presentation on a current research topic (25-30 min and 5 min discussion). The research work is a project work according to the statutes of the ASPO and FSPO. |
Assignment for the Following Curricula |
Computer Science: Core qualification: Compulsory Computational Science and Engineering: Core qualification: Compulsory Information and Communication Systems: Core qualification: Compulsory |
Course L1761: Project Work |
Typ | Projection Course |
Hrs/wk | 10 |
CP | 16 |
Workload in Hours | Independent Study Time 340, Study Time in Lecture 140 |
Lecturer | Dozenten des SD E |
Language | DE/EN |
Cycle | WiSe |
Content |
Current research topics of the chosen specialization. |
Literature |
Aktuelle Literatur zu Forschungsthemen aus der gewählten Vertiefungsrichtung. |
Course L0817: Seminar |
Typ | Seminar |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Dozenten des SD E |
Language | DE/EN |
Cycle | WiSe |
Content |
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Literature | Wird vom Veranstalter bekanntgegeben. |
Module M0836: Communication Networks I - Analysis and Structure |
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Courses | ||||||||||||||||
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Module Responsible | Prof. Andreas Timm-Giel |
Admission Requirements | None |
Recommended Previous Knowledge |
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Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
Students are able to describe the principles and structures of communication networks in detail. They can explain the formal description methods of communication networks and their protocols. They are able to explain how current and complex communication networks work and describe the current research in these examples. |
Skills |
Students are able to evaluate the performance of communication networks using the learned methods. They are able to work out problems themselves and apply the learned methods. They can apply what they have learned autonomously on further and new communication networks. |
Personal Competence | |
Social Competence |
Students are able to define tasks themselves in small teams and solve these problems together using the learned methods. They can present the obtained results. They are able to discuss and critically analyse the solutions. |
Autonomy |
Students are able to obtain the necessary expert knowledge for understanding the functionality and performance capabilities of new communication networks independently. |
Workload in Hours | Independent Study Time 110, Study Time in Lecture 70 |
Credit points | 6 |
Examination | Colloquium |
Examination duration and scale | 1.5 hours colloquium with three students, therefore about 30 min per student. Topics of the colloquium are the posters from the previous poster session and the topics of the module. |
Assignment for the Following Curricula |
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Electrical Engineering: Specialisation Control and Power Systems: Elective Compulsory Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Networks: Elective Compulsory Mechatronics: Technical Complementary Course: Elective Compulsory Microelectronics and Microsystems: Specialisation Communication and Signal Processing: Elective Compulsory |
Course L0897: Analysis and Structure of Communication Networks |
Typ | Lecture |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Dr. Maciej Mühleisen |
Language | EN |
Cycle | WiSe |
Content | |
Literature |
Further literature is announced at the beginning of the lecture. |
Course L0899: Selected Topics of Communication Networks |
Typ | Problem-based Learning |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Dr. Maciej Mühleisen |
Language | EN |
Cycle | WiSe |
Content | Example networks selected by the students will be researched on in a PBL course by the students in groups and will be presented in a poster session at the end of the term. |
Literature |
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Course L0898: Communication Networks Excercise |
Typ | Problem-based Learning |
Hrs/wk | 1 |
CP | 2 |
Workload in Hours | Independent Study Time 46, Study Time in Lecture 14 |
Lecturer | Dr. Maciej Mühleisen |
Language | EN |
Cycle | WiSe |
Content | Part of the content of the lecture Communication Networks are reflected in computing tasks in groups, others are motivated and addressed in the form of a PBL exercise. |
Literature |
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Module M0753: Software Verification |
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Courses | ||||||||||||||||
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Module Responsible | Prof. Sibylle Schupp |
Admission Requirements | None |
Recommended Previous Knowledge |
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Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
Students apply the major verification techniques in model checking and deductive verification. They explain in formal terms syntax and semantics of the underlying logics, and assess the expressivity of different logics as well as their limitations. They classify formal properties of software systems. They find flaws in formal arguments, arising from modeling artifacts or underspecification. |
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 96, Study Time in Lecture 84 |
Credit points | 6 |
Examination | Written exam |
Examination duration and scale | 90 min |
Assignment for the Following Curricula |
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems, Focus Software: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems: Compulsory International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory |
Course L0629: Software Verification |
Typ | Lecture |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Sibylle Schupp |
Language | EN |
Cycle | WiSe |
Content |
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Literature |
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Course L0630: Software Verification |
Typ | Recitation Section (small) |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Prof. Sibylle Schupp |
Language | EN |
Cycle | WiSe |
Content | See interlocking course |
Literature | See interlocking course |
Course L1622: Software Verification |
Typ | Recitation Section (large) |
Hrs/wk | 2 |
CP | 1 |
Workload in Hours | Independent Study Time 2, Study Time in Lecture 28 |
Lecturer | Prof. Sibylle Schupp |
Language | EN |
Cycle | WiSe |
Content | |
Literature |
Module M0926: Distributed Algorithms |
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Courses | ||||||||||||
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Module Responsible | Prof. Volker Turau |
Admission Requirements | None |
Recommended Previous Knowledge | |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge | 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 |
Examination | Oral exam |
Examination duration and scale | |
Assignment for the Following Curricula |
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Numerics and 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 |
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Literature |
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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 M0556: Computer Graphics and Animation |
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Courses | ||||||||||||
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Module Responsible | Prof. Helmut Weberpals |
Admission Requirements |
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Recommended Previous Knowledge |
Students are expected to have a solid knowledge of object-oriented programming as well as of linear algebra and geometry. |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
Students have acquired a theoretical basis in computer graphics and have a clear understanding of the process of computer animation. |
Skills |
Students have acquired
|
Personal Competence | |
Social Competence |
Students are trained in communicating abstract ideas and are familiar with planning and conducting projects within a small team. |
Autonomy |
Students are able to direct complex computer animation projects. |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Examination | Project |
Examination duration and scale | 90 min |
Assignment for the Following Curricula |
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Computational Science and Engineering: Specialisation Systems 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 and Animation |
Typ | Lecture |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Helmut Weberpals |
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 and Animation |
Typ | Project Seminar |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Helmut Weberpals |
Language | EN |
Cycle | SoSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M0653: High-Performance Computing |
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Courses | ||||||||||||
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Module Responsible | Prof. Thomas Rung |
Admission Requirements |
None |
Recommended Previous Knowledge | Basic knowledge in usage of modern IT environment |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
Students are able to outline the fundamentals of numerical algorithms for high-performance computers by reference to modern hardware examples. Students can explain the relation between hard- and software aspects for the design of algorithms. |
Skills | Student can perform a critical assesment of the computational efficiency of simulation approaches. |
Personal Competence | |
Social Competence | Students are able to develop and code algorithms in a team. |
Autonomy |
|
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Examination | Written exam |
Examination duration and scale | 1.5h |
Assignment for the Following Curricula |
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Computational Science and Engineering: Specialisation Scientific Computing: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Numerics and Computer Science: Elective Compulsory |
Course L0242: Fundamentals of High-Performance Computing |
Typ | Lecture |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Thomas Rung |
Language | DE/EN |
Cycle | SoSe |
Content |
Fundamentals of modern hardware architectur, critical hard- & software aspects for efficient processing of exemplary algorithms, concepts for shared- and distributed-memory systems, implementations for accelerator hardware (GPGPUs) |
Literature |
Course L1416: Fundamentals of High-Performance Computing |
Typ | Problem-based Learning |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Thomas Rung |
Language | DE/EN |
Cycle | SoSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M0673: Information Theory and Coding |
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Courses | ||||||||||||
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Module Responsible | Prof. Gerhard Bauch |
Admission Requirements | None |
Recommended Previous Knowledge |
Probability theory and random processes Basic knowledge of communications engineering is desirable (e.g. from lecture "Fundamentals of Communications and Random Processes") |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge | The students know the basic definitions for quantification of information in the sense of information theory. They know Shannon's source coding theorem and channel coding theorem and are able to determine theoretical limits of data compression and error-free data transmission over noisy channels. They understand the principles of source coding as well as error-detecting and error-correcting channel coding. They are familiar with the principles of decoding, in particular with modern methods of iterative decoding. They know fundamental coding schemes, their properties and decoding algorithms. |
Skills | The students are able to determine the limits of data compression as well as of data transmission through noisy channels and based on those limits to design basic parameters of a transmission scheme. They can estimate the parameters of an error-detecting or error-correcting channel coding scheme for achieving certain performance targets. They are able to compare the properties of basic channel coding and decoding schemes regarding error correction capabilities, decoding delay, decoding complexity and to decide for a suitable method. They are capable of implementing basic coding and decoding schemes in software. |
Personal Competence | |
Social Competence |
The students can jointly solve specific problems. |
Autonomy |
The students are able to acquire relevant information from appropriate literature sources. They can control their level of knowledge during the lecture period by solving tutorial problems, software tools, clicker system. |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Examination | Written exam |
Examination duration and scale | 90 min |
Assignment for the Following Curricula |
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Computational Science and Engineering: Specialisation Engineering: Elective Compulsory Information and Communication Systems: Core qualification: Compulsory Mechatronics: Technical Complementary Course: Elective Compulsory |
Course L0436: Information Theory and Coding |
Typ | Lecture |
Hrs/wk | 3 |
CP | 4 |
Workload in Hours | Independent Study Time 78, Study Time in Lecture 42 |
Lecturer | Prof. Gerhard Bauch |
Language | DE/EN |
Cycle | SoSe |
Content |
|
Literature |
Bossert, M.: Kanalcodierung. Oldenbourg. Friedrichs, B.: Kanalcodierung. Springer. Lin, S., Costello, D.: Error Control Coding. Prentice Hall. Roth, R.: Introduction to Coding Theory. Johnson, S.: Iterative Error Correction. Cambridge. Richardson, T., Urbanke, R.: Modern Coding Theory. Cambridge University Press. Gallager, R. G.: Information theory and reliable communication. Whiley-VCH Cover, T., Thomas, J.: Elements of information theory. Wiley. |
Course L0438: Information Theory and Coding |
Typ | Recitation Section (large) |
Hrs/wk | 1 |
CP | 2 |
Workload in Hours | Independent Study Time 46, Study Time in Lecture 14 |
Lecturer | Prof. Gerhard Bauch |
Language | DE/EN |
Cycle | SoSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M1318: Wireless Sensor Networks |
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Courses | ||||||||||||||||
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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 | |
Skills | |
Personal Competence | |
Social Competence | |
Autonomy | |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Examination | Oral exam |
Examination duration and scale | 30 min |
Assignment for the Following Curricula |
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory |
Course L1819: Selected Topics of Wireless Sensor Networks |
Typ | Problem-based Learning |
Hrs/wk | 1 |
CP | 2 |
Workload in Hours | Independent Study Time 46, Study Time in Lecture 14 |
Lecturer | Prof. Bernd-Christian Renner |
Language | EN |
Cycle | SoSe |
Content |
Selected topics on sensor network research will be researched in a PBL course by the students in groups and will be presented in a poster session at the end of the term. Topics are:
|
Literature |
Will be provided individually |
Course L1815: Wireless Sensor Networks |
Typ | Lecture |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Prof. Bernd-Christian Renner |
Language | EN |
Cycle | SoSe |
Content | |
Literature |
Course L1816: Wireless Sensor Networks |
Typ | Recitation Section (small) |
Hrs/wk | 1 |
CP | 2 |
Workload in Hours | Independent Study Time 46, Study Time in Lecture 14 |
Lecturer | Prof. Bernd-Christian Renner |
Language | EN |
Cycle | SoSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M1323: Cryptography |
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Courses | ||||||||||||
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Module Responsible | Prof. Chris Brzuska |
Admission Requirements | None |
Recommended Previous Knowledge |
Prerequisites: |
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 |
Examination | Oral exam |
Examination duration and scale | |
Assignment for the Following Curricula |
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems: Elective Compulsory |
Course L1825: Cryptography |
Typ | Recitation Section (small) |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Chris Brzuska |
Language | DE/EN |
Cycle | SoSe |
Content | |
Literature |
Literatur: |
Course L1826: Cryptography |
Typ | Lecture |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Chris Brzuska |
Language | DE/EN |
Cycle | SoSe |
Content |
Content: |
Literature |
Literatur: |
Module M1324: Software Testing |
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Courses | ||||||||||||
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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 | |
Skills | |
Personal Competence | |
Social Competence | |
Autonomy | |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Examination | Written exam |
Examination duration and scale | 90 min |
Assignment for the Following Curricula |
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Computational Science and Engineering: Specialisation Information and Communication Technology: 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 L1827: Software Testing |
Typ | Problem-based Learning |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Dr. Sandro Schulze |
Language | EN |
Cycle | SoSe |
Content | |
Literature |
Course L1828: Software Testing |
Typ | Lecture |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Dr. Sandro Schulze |
Language | EN |
Cycle | SoSe |
Content | |
Literature |
Module M0733: Software Analysis |
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Courses | ||||||||||||
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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 approaches to data-flow analysis, control-flow analysis, and type-based analysis, along with their classification schemes, and employ abstract interpretation. They explain the standard forms of internal representations and models, including their mathematical structure and properties, and evaluate their suitability for a particular analysis. They explain and categorize the major analysis algorithms. They distinguish precise solutions from approximative approaches, and show termination and soundness properties. |
Skills |
Presented with an analytical task for a software artifact, students select appropriate approaches from software analysis, and justify their choice. They design suitable representations by modifying standard representations. They develop customized analyses and devise them as safe overapproximations. They formulate analyses in a formal way and construct arguments for their correctness, behavior, and precision. |
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 analysis. 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 |
Examination | Written exam |
Examination duration and scale | 90 min |
Assignment for the Following Curricula |
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Computational Science and Engineering: Specialisation Information and Communication Technology: 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 International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory |
Course L0631: Software Analysis |
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 L0632: Software Analysis |
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 | SoSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M0837: Communication Networks II - Simulation and Modeling |
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Courses | ||||||||
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Module Responsible | Prof. Andreas Timm-Giel |
Admission Requirements | |
Recommended Previous Knowledge |
|
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
Students are able to explain the necessary stochastics, the discrete event simulation technology and modelling of networks for performance evaluation. |
Skills |
Students are able to apply the method of simulation for performance evaluation to different, also not practiced, problems of communication networks. The students can analyse the obtained results and explain the effects observed in the network. They are able to question their own results. |
Personal Competence | |
Social Competence |
Students are able to acquire expert knowledge in groups, present the results, and discuss solution approaches and results. They are able to work out solutions for new problems in small teams. |
Autonomy |
Students are able to transfer independently and in discussion with others the acquired method and expert knowledge to new problems. They can identify missing knowledge and acquire this knowledge independently. |
Workload in Hours | Independent Study Time 110, Study Time in Lecture 70 |
Credit points | 6 |
Examination | Colloquium |
Examination duration and scale | 45-60 minutes colloquium with two students, therefore about 30 minutes per student. |
Assignment for the Following Curricula |
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Networks: Elective Compulsory |
Course L0887: Simulation and Modelling of Communication Networks |
Typ | Problem-based Learning |
Hrs/wk | 5 |
CP | 6 |
Workload in Hours | Independent Study Time 110, Study Time in Lecture 70 |
Lecturer | Prof. Andreas Timm-Giel |
Language | EN |
Cycle | SoSe |
Content |
In the course necessary basic stochastics and the discrete event simulation are introduced. Also simulation models for communication networks, for example, traffic models, mobility models and radio channel models are presented in the lecture. Students work with a simulation tool, where they can directly try out the acquired skills, algorithms and models. At the end of the course increasingly complex networks and protocols are considered and their performance is determined by simulation. |
Literature |
Further literature is announced at the beginning of the lecture. |
Module M0924: Software for Embedded Systems |
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Courses | ||||||||||||
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Module Responsible | Prof. Volker Turau |
Admission Requirements | None |
Recommended Previous Knowledge | |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge | 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 |
Examination | Written exam |
Examination duration and scale | 90 min |
Assignment for the Following Curricula |
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Computational Science and Engineering: Specialisation Information and Communication Technology: 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 Mechatronics: Technical Complementary Course: 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. Volker Turau |
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. Volker Turau |
Language | DE/EN |
Cycle | SoSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M0943: Network Security |
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Courses | ||||||||||||
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Module Responsible | Prof. Dieter Gollmann |
Admission Requirements | None |
Recommended Previous Knowledge | Discrete Mathematics, Computer Networks (TCP/IP) |
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 110, Study Time in Lecture 70 |
Credit points | 6 |
Examination | Written exam |
Examination duration and scale | 120 minutes |
Assignment for the Following Curricula |
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems: Elective Compulsory |
Course L1105: Network Security |
Typ | Lecture |
Hrs/wk | 3 |
CP | 3 |
Workload in Hours | Independent Study Time 48, Study Time in Lecture 42 |
Lecturer | Prof. Dieter Gollmann |
Language | EN |
Cycle | SoSe |
Content |
|
Literature |
W. Stallings: Cryptography and Network Security: Principles and Practice, 6th edition (2013) A. Menezes, P. van Oorschot, S. Vanstone: Handbook of Applied Cryptography, CRC Press (1997) D. Gollmann: Computer Security, 3rd edition, Wiley (2011) V. Niemi, K. Nyberg: UMTS Security, Wiley (2003) |
Course L1106: Network 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 | SoSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M1248: Compilers for Embedded Systems |
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Courses | ||||||||||||
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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 |
Examination | Oral exam |
Examination duration and scale | 30 minutes, contents of course |
Assignment for the Following Curricula |
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Computational Science and Engineering: Specialisation Information and Communication Technology: 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 | Laboratory |
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 M0910: Advanced System-on-Chip Design (Lab) |
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Courses | ||||||||
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Module Responsible | Prof. Heiko Falk |
Admission Requirements | None. |
Recommended Previous Knowledge |
Module "Computer Architecture", in particular having passed the practical labs of "Computer Architecture" |
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 |
Examination | Project |
Examination duration and scale | |
Assignment for the Following Curricula |
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory |
Course L1061: Advanced System-on-Chip Design |
Typ | 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 M0942: Software Security |
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Courses | ||||||||||||
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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 |
Examination | Written exam |
Examination duration and scale | 120 minutes |
Assignment for the Following Curricula |
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Computational Science and Engineering: Specialisation Information and Communication Technology: 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 M0913: CMOS Nanoelectronics with Practice |
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Courses | ||||||||||||||||
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Module Responsible | Prof. Wolfgang Krautschneider |
Admission Requirements | None |
Recommended Previous Knowledge | Fundamentals of MOS devices and electronic circuits |
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 |
Examination | Written exam |
Examination duration and scale | 90 min |
Assignment for the Following Curricula |
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Electrical Engineering: Core qualification: Compulsory Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory International Management and Engineering: Specialisation II. Electrical Engineering: Elective Compulsory Mechanical Engineering and Management: Specialisation Mechatronics: Elective Compulsory Mechatronics: Specialisation System Design: Elective Compulsory Microelectronics and Microsystems: Core qualification: Elective Compulsory |
Course L0764: CMOS Nanoelectronics |
Typ | Lecture |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Wolfgang Krautschneider |
Language | EN |
Cycle | WiSe |
Content |
|
Literature |
|
Course L1063: CMOS Nanoelectronics |
Typ | Laboratory Course |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Prof. Wolfgang Krautschneider |
Language | EN |
Cycle | WiSe |
Content | See interlocking course |
Literature | See interlocking course |
Course L1059: CMOS Nanoelectronics |
Typ | Recitation Section (small) |
Hrs/wk | 1 |
CP | 1 |
Workload in Hours | Independent Study Time 16, Study Time in Lecture 14 |
Lecturer | Prof. Wolfgang Krautschneider |
Language | EN |
Cycle | WiSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M0839: Traffic Engineering |
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Courses | ||||||||||||||||
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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 |
Examination | Oral exam |
Examination duration and scale | 30 min |
Assignment for the Following Curricula |
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Networks: Elective Compulsory |
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 |
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 |
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 M0549: Scientific Computing and Accuracy |
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Courses | ||||||||||||
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Module Responsible | Prof. Siegfried Rump |
Admission Requirements |
None |
Recommended Previous Knowledge |
Basic knowledge in numerics |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
The students have deeper knowledge of numerical and semi-numerical methods with the goal to compute principally exact and accurate error bounds. For several fundamental problems they know algorithms with the verification of the correctness of the computed result. |
Skills |
The students can devise algorithms for several basic problems which compute rigorous error bounds for the solution and analyze the sensitivity with respect to variation of the input data as well. |
Personal Competence | |
Social Competence |
The students have the skills to solve problems together in small groups and to present the achieved results in an appropriate manner. |
Autonomy |
The students are able to retrieve necessary informations from the given literature and to combine them with the topics of the lecture. Throughout the lecture they can check their abilities and knowledge on the basis of given exercises and test questions providing an aid to optimize their learning process. |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Examination | Oral exam |
Examination duration and scale | 30 minutes |
Assignment for the Following Curricula |
Bioprocess Engineering: Specialisation A - General Bioprocess Engineering: Elective Compulsory Computer Science: Specialisation Intelligence Engineering: Elective Compulsory Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Computational Science and Engineering: Specialisation Systems Engineering and Robotics: Elective Compulsory Computational Science and Engineering: Specialisation Scientific Computing: Elective Compulsory Technomathematics: Specialisation II. Informatics: Elective Compulsory Process Engineering: Specialisation Process Engineering: Elective Compulsory Process Engineering: Specialisation Chemical Process Engineering: Elective Compulsory |
Course L0122: Verification Methods |
Typ | Lecture |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Siegfried Rump |
Language | DE |
Cycle | WiSe |
Content |
|
Literature |
Neumaier: Interval Methods for Systems of Equations. In: Encyclopedia of Mathematics and its Applications. Cambridge University Press, 1990 S.M. Rump. Verification methods: Rigorous results using floating-point arithmetic. Acta Numerica, 19:287-449, 2010. |
Course L1208: Verification Methods |
Typ | Recitation Section (small) |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Siegfried Rump |
Language | DE |
Cycle | WiSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M1318: Wireless Sensor Networks |
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Courses | ||||||||||||||||
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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 | |
Skills | |
Personal Competence | |
Social Competence | |
Autonomy | |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Examination | Oral exam |
Examination duration and scale | 30 min |
Assignment for the Following Curricula |
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory |
Course L1819: Selected Topics of Wireless Sensor Networks |
Typ | Problem-based Learning |
Hrs/wk | 1 |
CP | 2 |
Workload in Hours | Independent Study Time 46, Study Time in Lecture 14 |
Lecturer | Prof. Bernd-Christian Renner |
Language | EN |
Cycle | SoSe |
Content |
Selected topics on sensor network research will be researched in a PBL course by the students in groups and will be presented in a poster session at the end of the term. Topics are:
|
Literature |
Will be provided individually |
Course L1815: Wireless Sensor Networks |
Typ | Lecture |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Prof. Bernd-Christian Renner |
Language | EN |
Cycle | SoSe |
Content | |
Literature |
Course L1816: Wireless Sensor Networks |
Typ | Recitation Section (small) |
Hrs/wk | 1 |
CP | 2 |
Workload in Hours | Independent Study Time 46, Study Time in Lecture 14 |
Lecturer | Prof. Bernd-Christian Renner |
Language | EN |
Cycle | SoSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M1301: Software Testing |
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Courses | ||||||||||||
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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 |
Examination | Written exam |
Examination duration and scale | 90 min |
Assignment for the Following Curricula |
Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Computational Science and Engineering: Specialisation Information and Communication Technology: Elective Compulsory Computational Science and Engineering: Specialisation Information and Communication Technology: 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 Information and Communication Systems: Specialisation Communication Systems, Focus Software: 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 | Dr. Sandro Schulze |
Language | EN |
Cycle | SoSe |
Content |
|
Literature |
|
Course L1792: Software Testing |
Typ | Problem-based Learning |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Dr. Sandro Schulze |
Language | EN |
Cycle | SoSe |
Content |
|
Literature |
|
Module M0550: Digital Image Analysis |
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Courses | ||||||||
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Module Responsible | Prof. Rolf-Rainer Grigat |
Admission Requirements | |
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 |
|
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 |
Examination | Written exam |
Examination duration and scale | 60 Minutes, Content of Lecture and materials in StudIP |
Assignment for the Following Curricula |
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Electrical Engineering: Specialisation Medical Technology: Elective Compulsory Computational Science and Engineering: Specialisation Systems Engineering: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems, Focus Signal Processing: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Software and Signal Processing: Elective Compulsory International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory Microelectronics and Microsystems: Specialisation Communication and Signal Processing: 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 M0554: Quantitative Methods - Statistics and Operations Research |
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Courses | ||||||||||||
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Module Responsible | Prof. Kathrin Fischer |
Admission Requirements | None. |
Recommended Previous Knowledge |
Knowledge of Mathematics on the Bachelor Level. Relevant previous knowledge is tested by an online module |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
The students know
|
Skills |
Students are able to
|
Personal Competence | |
Social Competence |
Students are able to
|
Autonomy |
Students are able to
|
Workload in Hours | Independent Study Time 110, Study Time in Lecture 70 |
Credit points | 6 |
Examination | Written exam |
Examination duration and scale | 3 hours |
Assignment for the Following Curricula |
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory Global Innovation Management: Core qualification: Elective Compulsory International Management and Engineering: Core qualification: Compulsory |
Course L0127: Quantitative Methods - Statistics and Operations Research |
Typ | Problem-based Learning |
Hrs/wk | 3 |
CP | 4 |
Workload in Hours | Independent Study Time 78, Study Time in Lecture 42 |
Lecturer | Prof. Kathrin Fischer |
Language | EN |
Cycle | WiSe |
Content |
Statistics
|
Literature |
Ausgewählte Bücher: D.R. Anderson / D.J. Sweeney / T.A. Williams / Martin: Quantitative Methods for Business. 11th Edition, Thomson, South Western 2008. Bluman, Alan G.: Elementary Statistics – A brief version. Third Edition, McGrawHill 2006. Domschke, W., Drexl, A.: Einführung in Operations Research, 7. Auflage, Springer, Berlin et al. 2007. Domschke, W. / A. Drexl / R. Klein / A. Scholl / S. Voß: Übungen und Fallbeispiele zum Operations Research, 6. Auflage, Springer, Berlin et al. 2007 Hillier, F.S., Lieberman, G.J.: Introduction to Operations Research. 8th Edition, McGraw-Hill, 2005. Schira, J.: Statistische Methoden der VWL und BWL – Theorie und Praxis. 2. Auflage, Pearson Verlag 2005. Zudem: Skript und Unterlagen, die zur Vorlesung herausgegeben werden. |
Course L0250: Quantitative Methods - Statistics and Operations Research |
Typ | Lecture |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Prof. Kathrin Fischer |
Language | EN |
Cycle | WiSe |
Content |
Statistics
Operations Research
|
Literature |
Ausgewählte Bücher: D.R. Anderson / D.J. Sweeney / T.A. Williams / Martin: Quantitative Methods for Business. 11th Edition, Thomson, South Western 2008. Bluman, Alan G.: Elementary Statistics – A brief version. Third Edition, McGrawHill 2006. Domschke, W., Drexl, A.: Einführung in Operations Research, 7. Auflage, Springer, Berlin et al. 2007. Domschke, W. / A. Drexl / R. Klein / A. Scholl / S. Voß: Übungen und Fallbeispiele zum Operations Research, 6. Auflage, Springer, Berlin et al. 2007 Hillier, F.S., Lieberman, G.J.: Introduction to Operations Research. 8th Edition, McGraw-Hill, 2005. Schira, J.: Statistische Methoden der VWL und BWL – Theorie und Praxis. 2. Auflage, Pearson Verlag 2005. Zudem: Skript und Unterlagen, die zur Vorlesung herausgegeben werden. |
Module M0846: Control Systems Theory and Design |
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Courses | ||||||||||||
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Module Responsible | Prof. Herbert Werner |
Admission Requirements | None |
Recommended Previous Knowledge | Introduction to Control Systems |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
|
Skills |
|
Personal Competence | |
Social Competence |
Students can work in small groups on specific problems to arrive at joint solutions. |
Autonomy |
Students can obtain information from provided sources (lecture notes, software documentation, experiment guides) and use it when solving given problems. They can assess their knowledge in weekly on-line tests and thereby control their learning progress. |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Examination | Written exam |
Examination duration and scale | 120 min |
Assignment for the Following Curricula |
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory Electrical Engineering: Core qualification: Compulsory Energy Systems: Core qualification: Elective Compulsory Aircraft Systems Engineering: Specialisation Aircraft Systems Engineering: Compulsory Computational Science and Engineering: Specialisation Systems Engineering: Elective Compulsory Computational Science and Engineering: Specialisation Systems Engineering: Elective Compulsory International Management and Engineering: Specialisation II. Electrical Engineering: Elective Compulsory International Management and Engineering: Specialisation II. Mechatronics: Elective Compulsory Mechatronics: Core qualification: Compulsory Biomedical Engineering: Specialisation Artificial Organs and Regenerative Medicine: Elective Compulsory Biomedical Engineering: Specialisation Implants and Endoprostheses: Elective Compulsory Biomedical Engineering: Specialisation Medical Technology and Control Theory: Compulsory Biomedical Engineering: Specialisation Management and Business Administration: Elective Compulsory Product Development, Materials and Production: Core qualification: Elective Compulsory Theoretical Mechanical Engineering: Core qualification: Compulsory |
Course L0656: Control Systems Theory and Design |
Typ | Lecture |
Hrs/wk | 2 |
CP | 4 |
Workload in Hours | Independent Study Time 92, Study Time in Lecture 28 |
Lecturer | Prof. Herbert Werner |
Language | EN |
Cycle | WiSe |
Content |
State space methods (single-input single-output) • State space models and transfer functions, state feedback Digital Control System identification and model order reduction Case study |
Literature |
|
Course L0657: Control Systems Theory and Design |
Typ | Recitation Section (small) |
Hrs/wk | 2 |
CP | 2 |
Workload in Hours | Independent Study Time 32, Study Time in Lecture 28 |
Lecturer | Prof. Herbert Werner |
Language | EN |
Cycle | WiSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M0629: Intelligent Autonomous Agents and Cognitive Robotics |
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Courses | ||||||||||||
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Module Responsible | Rainer Marrone |
Admission Requirements |
|
Recommended Previous Knowledge | Vectors, matrices, Calculus, propositional Logic, Stochastics (in particular practical representation formalisms such as Bayesian networks, dynamic Bayesian networks, hidden Markov models, Kalman filters) |
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 | |
Autonomy | |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Examination | Written exam |
Examination duration and scale | 90 minutes |
Assignment for the Following Curricula |
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory Computational Science and Engineering: Specialisation Systems Engineering: Elective Compulsory International Production Management: Specialisation Production Technology: Elective Compulsory International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory Mechatronics: Technical Complementary Course: 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 |
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 M0881: Mathematical Image Processing |
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Courses | ||||||||||||
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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 |
Examination | Oral exam |
Examination duration and scale | 30 |
Assignment for the Following Curricula |
Bioprocess Engineering: Specialisation A - General Bioprocess Engineering: Elective Compulsory Computer Science: Specialisation Intelligence Engineering: Elective Compulsory Computational Science and Engineering: Specialisation Systems Engineering: Elective Compulsory Mechatronics: Technical Complementary Course: Elective Compulsory Technomathematics: Specialisation Mathematics: 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 M0551: Pattern Recognition and Data Compression |
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Courses | ||||||||
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Module Responsible | Prof. Rolf-Rainer Grigat |
Admission Requirements | |
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 | |
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 |
Examination | Written exam |
Examination duration and scale | 60 Minutes, Content of Lecture and materials in StudIP |
Assignment for the Following Curricula |
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Computational Science and Engineering: Specialisation Systems Engineering: Elective Compulsory Information and Communication Systems: Specialisation Communication Systems, Focus Signal Processing: Elective Compulsory Information and Communication Systems: Specialisation Secure and Dependable IT Systems, Focus Software and Signal Processing: Elective Compulsory International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Numerics 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 M0558: Operations Research |
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Courses | ||||||||||||
|
Module Responsible | Prof. Kathrin Fischer |
Admission Requirements | None |
Recommended Previous Knowledge | Knowledge from the module "Quantitative Methods": Linear Programming, Network Optimization and basics of Integer Programming. |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
Students have an in-depth knowledge of the following areas: They are able to
|
Skills |
Students have in-depth abilities in the following areas: They are able to
|
Personal Competence | |
Social Competence |
Students are able to
|
Autonomy |
Students are able to
|
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Examination | Homework |
Examination duration and scale | To be announced in Lecture |
Assignment for the Following Curricula |
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory International Management and Engineering: Specialisation I. Electives Management: Elective Compulsory Logistics, Infrastructure and Mobility: Core qualification: Elective Compulsory |
Course L0155: Operations Research |
Typ | Lecture |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Kathrin Fischer |
Language | DE |
Cycle | SoSe |
Content |
|
Literature |
Bücher: Albright, C., Winston, W.: Management Science Modeling. Revised Third Edition, South-Western 2009. Eiselt, H.A., Sandblom, C.-L.: Linear Programming and its Applications, Springer 2007. Eiselt, H.A., Sandblom, C.-L.: Integer Programming and Network Models, Springer 2000. Eiselt, H.A., Sandblom, C.-L.: Decision Analysis, Location Models, and Scheduling Problems, Springer 2004. Suhl, L., Mellouli, T.: Optimierungssysteme. Springer, Berlin et al., 2. Auflage, 2009. Williams, H.P.: Model Building in Mathematical Programming. 5th edition, Wiley & Sons, 2013. Winston, W., Venkataramanan, M.: Mathematical Programming. Operations Research, Volume 1, 4th Edition, Thomson, London et al. 2003. Sowie ein Skript, das zur Vorlesung herausgegeben wird. |
Course L0156: Operations Research - Seminar |
Typ | Seminar |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Kathrin Fischer |
Language | DE |
Cycle | SoSe |
Content |
Special topics from different areas of the lecture are discussed in the seminar. Students are required to use current publications from highly esteemed journals in their assignment and to write an essay on a relevant OR topic. Moreover, they have to prepare and give a talk on that topic. The seminar is research-oriented and focuses on relevant research topics from the field. |
Literature |
Fachartikel (Journal Papers), die zu Beginn des Seminars bekanntgegeben werden. |
Module M0587: Numerical and Seminumerical Programming |
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Courses | ||||||||||||
|
Module Responsible | Prof. Siegfried Rump |
Admission Requirements |
Bachelor IIW or Mathematics
|
Recommended Previous Knowledge |
Lecture numerics and computer algebra Basic knowledge in numerics and discrete mathematics |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge | The students have deeper knowledge of numerical, heuristical and exact methods. For several fundamental problems they know approximative and exact solution possibilities. They can distinguish between exact and heuristical approximative Solutions. |
Skills |
The students are able to analyze in depth complex problems in mathematics and computer science. In particular they can analyze the sensitivity of the solution and can calculate verified error bounds for a solution. For NP-hard problems they can derive efficient and heuristic algorithms. |
Personal Competence | |
Social Competence |
The students have the skills to solve problems together in small groups and to present the achieved results in an appropriate manner. |
Autonomy |
The students are able to retrieve necessary informations from the given literature and to combine them with the topics of the lecture. Throughout the lecture they can check their abilities and knowledge on the basis of given exercises and test questions providing an aid to optimize their learning process. |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Examination | Oral exam |
Examination duration and scale | |
Assignment for the Following Curricula |
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory Computational Science and Engineering: Specialisation Scientific Computing: Elective Compulsory |
Course L0124: Numerical and Seminumerical Programming |
Typ | Lecture |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Siegfried Rump |
Language | DE |
Cycle | SoSe |
Content |
|
Literature |
Golub, G.H. and Van Loan, Ch.: Matrix Computations, John Hopkins University Press, 3rd edition, 1996 Higham, N.J.: Accuracy and stability of numerical algorithms, SIAM Publications, Philadelphia, 2nd edition, 2002 S.M. Rump. Verification methods: Rigorous results using floating-point arithmetic. Acta Numerica, 19:287–449, 2010. |
Course L1210: Numerical and Seminumerical Programming |
Typ | Recitation Section (small) |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Siegfried Rump |
Language | DE |
Cycle | SoSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M0630: Robotics and Navigation in Medicine |
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Courses | ||||||||||||||||
|
Module Responsible | Prof. Alexander Schlaefer |
Admission Requirements |
None |
Recommended Previous Knowledge |
principles of math (algebra, analysis/calculus) |
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 details. 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 |
Examination | Written exam |
Examination duration and scale | 90 minutes |
Assignment for the Following Curricula |
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory Electrical Engineering: Specialisation Medical Technology: Elective Compulsory Computational Science and Engineering: Specialisation Systems Engineering: 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: 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 M0627: Machine Learning and Data Mining |
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Courses | ||||||||||||
|
Module Responsible | NN |
Admission Requirements |
|
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 |
Examination | Written exam |
Examination duration and scale | 90 minutes |
Assignment for the Following Curricula |
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory Computational Science and Engineering: Specialisation Systems Engineering: Elective Compulsory International Management and Engineering: Specialisation II. Information Technology: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Numerics 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 | NN |
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 | NN |
Language | EN |
Cycle | SoSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M0552: 3D Computer Vision |
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Courses | ||||||||||||
|
Module Responsible | Prof. Rolf-Rainer Grigat |
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 the field of projective geometry. |
Skills |
Students are capable of
With assistance from the teacher students are able to link the contents of the three subject areas (modules)
in practical assignments. |
Personal Competence | |
Social Competence |
Students can collaborate in a small team on the practical realization and testing of a system to reconstruct a three-dimensional scene or to evaluate volume data sets. |
Autonomy |
Students are able to solve simple tasks independently with reference to the contents of the lectures and the exercise sets. Students are able to solve detailed problems independently with the aid of the tutorial’s programming task. |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Examination | Written exam |
Examination duration and scale | 60 Minutes, Content of Lecture and materials in StudIP |
Assignment for the Following Curricula |
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory Computational Science and Engineering: Specialisation Systems Engineering and Robotics: 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 Mechanical Engineering and Management: Specialisation Mechatronics: Elective Compulsory Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory Microelectronics and Microsystems: Specialisation Communication and Signal Processing: Elective Compulsory |
Course L0129: 3D Computer Vision |
Typ | Lecture |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Rolf-Rainer Grigat |
Language | EN |
Cycle | WiSe |
Content |
|
Literature |
|
Course L0130: 3D Computer Vision |
Typ | Recitation Section (small) |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Rolf-Rainer Grigat |
Language | EN |
Cycle | WiSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M1249: Numerical Methods for Medical Imaging |
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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 | |
Skills | |
Personal Competence | |
Social Competence | |
Autonomy | |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Examination | Written exam |
Examination duration and scale | 90 min |
Assignment for the Following Curricula |
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory Electrical Engineering: Specialisation Modeling and Simulation: Elective Compulsory Electrical Engineering: Specialisation Medical Technology: Elective Compulsory Electrical Engineering: Specialisation Medical Technology: Elective Compulsory Computational Science and Engineering: Specialisation Systems Engineering and Robotics: Elective Compulsory |
Course L1694: Numerical Methods for 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 |
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: Numerical Methods for 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 |
Cycle | WiSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M0623: Intelligent Systems in Medicine |
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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 |
Examination | Written exam |
Examination duration and scale | 90 minutes |
Assignment for the Following Curricula |
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory Electrical Engineering: Specialisation Medical Technology: Elective Compulsory Computational Science and Engineering: Specialisation Systems Engineering and Robotics: 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: Specialisation Bio- and Medical Technology: Elective Compulsory Theoretical Mechanical Engineering: Technical Complementary Course: 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 M0633: Industrial Process Automation |
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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 disctrete 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. |
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 |
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 Intelligence Engineering: Elective Compulsory Electrical Engineering: Specialisation Control and Power Systems: Elective Compulsory Computational Science and Engineering: Specialisation Scientific Computing: Elective Compulsory Computational Science and Engineering: Specialisation Systems Engineering and Robotics: Elective Compulsory International Production Management: Specialisation Production Technology: Elective Compulsory International Management and Engineering: Specialisation II. Mechatronics: Elective Compulsory Mechanical Engineering and Management: Specialisation Mechatronics: Elective Compulsory Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory Theoretical Mechanical Engineering: Specialisation Numerics and Computer Science: Elective Compulsory Theoretical Mechanical Engineering: Technical Complementary Course: 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 M0738: Digital Audio Signal Processing |
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Courses | ||||||||||||
|
Module Responsible | Prof. Udo Zölzer |
Admission Requirements |
None |
Recommended Previous Knowledge | |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
Die Studierenden können die grundlegenden Verfahren und Methoden der digitalen Audiosignalverarbeitung erklären. Sie können die wesentlichen physikalischen Effekte bei der Sprach- und Audiosignalverarbeitung erläutern und in Kategorien einordnen. Sie können einen Überblick der numerischen Methoden und messtechnischen Charakterisierung von Algorithmen zur Audiosignalverarbeitung geben. Sie können die erarbeiteten Algorithmen auf weitere Anwendungen im Bereich der Informationstechnik und Informatik abstrahieren. |
Skills |
The students will be able to apply methods and techniques from audio signal processing in the fields of mobile and internet communication. They can rely on elementary algorithms of audio signal processing in form of Matlab code and interactive JAVA applets. They can study parameter modifications and evaluate the influence on human perception and technical applications in a variety of applications beyond audio signal processing. Students can perform measurements in time and frequency domain in order to give objective and subjective quality measures with respect to the methods and applications. |
Personal Competence | |
Social Competence |
The students can work in small groups to study special tasks and problems and will be enforced to present their results with adequate methods during the exercise. |
Autonomy |
The students will be able to retrieve information out of the relevant literature in the field and putt hem into the context of the lecture. They can relate their gathered knowledge and relate them to other lectures (signals and systems, digital communication systems, image and video processing, and pattern recognition). They will be prepared to understand and communicate problems and effects in the field audio signal processing. |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Examination | Written exam |
Examination duration and scale | 45 min |
Assignment for the Following Curricula |
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory Electrical Engineering: Specialisation Information and Communication Systems: Elective Compulsory Computational Science and Engineering: Specialisation Information and Communication Technology: 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 Microelectronics and Microsystems: Specialisation Communication and Signal Processing: Elective Compulsory |
Course L0650: Digital Audio Signal Processing |
Typ | Lecture |
Hrs/wk | 3 |
CP | 4 |
Workload in Hours | Independent Study Time 78, Study Time in Lecture 42 |
Lecturer | Prof. Udo Zölzer |
Language | EN |
Cycle | WiSe |
Content |
|
Literature |
- U. Zölzer, Digitale Audiosignalverarbeitung, 3. Aufl., B.G. Teubner, 2005. - U. Zölzer, Digitale Audio Signal Processing, 2nd Edition, J. Wiley & Sons, 2005. - U. Zölzer (Ed), Digital Audio Effects, 2nd Edition, J. Wiley & Sons, 2011.
|
Course L0651: Digital Audio Signal Processing |
Typ | Recitation Section (large) |
Hrs/wk | 1 |
CP | 2 |
Workload in Hours | Independent Study Time 46, Study Time in Lecture 14 |
Lecturer | Prof. Udo Zölzer |
Language | EN |
Cycle | WiSe |
Content | |
Literature |
Module M0549: Scientific Computing and Accuracy |
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Courses | ||||||||||||
|
Module Responsible | Prof. Siegfried Rump |
Admission Requirements |
None |
Recommended Previous Knowledge |
Basic knowledge in numerics |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
The students have deeper knowledge of numerical and semi-numerical methods with the goal to compute principally exact and accurate error bounds. For several fundamental problems they know algorithms with the verification of the correctness of the computed result. |
Skills |
The students can devise algorithms for several basic problems which compute rigorous error bounds for the solution and analyze the sensitivity with respect to variation of the input data as well. |
Personal Competence | |
Social Competence |
The students have the skills to solve problems together in small groups and to present the achieved results in an appropriate manner. |
Autonomy |
The students are able to retrieve necessary informations from the given literature and to combine them with the topics of the lecture. Throughout the lecture they can check their abilities and knowledge on the basis of given exercises and test questions providing an aid to optimize their learning process. |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Examination | Oral exam |
Examination duration and scale | 30 minutes |
Assignment for the Following Curricula |
Bioprocess Engineering: Specialisation A - General Bioprocess Engineering: Elective Compulsory Computer Science: Specialisation Intelligence Engineering: Elective Compulsory Computer Science: Specialisation Computer and Software Engineering: Elective Compulsory Computational Science and Engineering: Specialisation Systems Engineering and Robotics: Elective Compulsory Computational Science and Engineering: Specialisation Scientific Computing: Elective Compulsory Technomathematics: Specialisation II. Informatics: Elective Compulsory Process Engineering: Specialisation Process Engineering: Elective Compulsory Process Engineering: Specialisation Chemical Process Engineering: Elective Compulsory |
Course L0122: Verification Methods |
Typ | Lecture |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Siegfried Rump |
Language | DE |
Cycle | WiSe |
Content |
|
Literature |
Neumaier: Interval Methods for Systems of Equations. In: Encyclopedia of Mathematics and its Applications. Cambridge University Press, 1990 S.M. Rump. Verification methods: Rigorous results using floating-point arithmetic. Acta Numerica, 19:287-449, 2010. |
Course L1208: Verification Methods |
Typ | Recitation Section (small) |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Siegfried Rump |
Language | DE |
Cycle | WiSe |
Content | See interlocking course |
Literature | See interlocking course |
Module M0832: Advanced Topics in Control |
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Courses | ||||||||||||
|
Module Responsible | Prof. Herbert Werner |
Admission Requirements | Optimal and Robust Control |
Recommended Previous Knowledge | H-infinity optimal control, mixed-sensitivity design, linear matrix inequalities |
Educational Objectives | After taking part successfully, students have reached the following learning results |
Professional Competence | |
Knowledge |
|
Skills |
|
Personal Competence | |
Social Competence | Students can work in small groups and arrive at joint results. |
Autonomy |
Students are able to find required information in sources provided (lecture notes, literature, software documentation) and use it to solve given problems. |
Workload in Hours | Independent Study Time 124, Study Time in Lecture 56 |
Credit points | 6 |
Examination | Oral exam |
Examination duration and scale | 30 min |
Assignment for the Following Curricula |
Computer Science: Specialisation Intelligence Engineering: Elective Compulsory Electrical Engineering: Specialisation Control and Power Systems: Elective Compulsory Aircraft Systems Engineering: Specialisation Aircraft Systems: Elective Compulsory Computational Science and Engineering: Specialisation Systems Engineering and Robotics: Elective Compulsory International Management and Engineering: Specialisation II. Mechatronics: Elective Compulsory Mechatronics: Specialisation System Design: Elective Compulsory Mechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory Theoretical Mechanical Engineering: Core qualification: Elective Compulsory Theoretical Mechanical Engineering: Technical Complementary Course: Elective Compulsory |
Course L0661: Advanced Topics in Control |
Typ | Lecture |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Herbert Werner |
Language | EN |
Cycle | WiSe |
Content |
|
Literature |
|
Course L0662: Advanced Topics in Control |
Typ | Recitation Section (small) |
Hrs/wk | 2 |
CP | 3 |
Workload in Hours | Independent Study Time 62, Study Time in Lecture 28 |
Lecturer | Prof. Herbert Werner |
Language | EN |
Cycle | WiSe |
Content | See interlocking course |
Literature | See interlocking course |
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 |
Examination | according to Subject Specific Regulations |
Examination duration and scale | see FSPO |
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 International Production Management: Thesis: Compulsory International Management and Engineering: Thesis: Compulsory Joint European Master in Environmental Studies - Cities and Sustainability: Thesis: Compulsory Logistics, Infrastructure and Mobility: Thesis: Compulsory Materials Science: Thesis: Compulsory Mechanical Engineering and Management: Thesis: Compulsory Mechatronics: Thesis: Compulsory Biomedical Engineering: Thesis: Compulsory Microelectronics and Microsystems: Thesis: Compulsory Product Development, Materials and Production: Thesis: Compulsory Renewable Energies: Thesis: Compulsory Naval Architecture and Ocean Engineering: Thesis: Compulsory Ship and Offshore Technology: Thesis: Compulsory Theoretical Mechanical Engineering: Thesis: Compulsory Process Engineering: Thesis: Compulsory Water and Environmental Engineering: Thesis: Compulsory |