Computational Science and - TUHH€¦ · Module M1552: Mathematics of Neural Networks...

Module Manual

Master of Science (M.Sc.)Computational Science and

Engineering

Cohort: Winter Term 2020Updated: 30th April 2020

23778

111212141618202325252831353939414345485050515252

Table of Contents

Table of ContentsProgram descriptionCore qualification

Module M0523: Business & ManagementModule M0524: Non-technical Courses for MasterModule M1421: Research Project

Specialization I. Computer ScienceModule M0942: Software SecurityModule M0753: Software VerificationModule M1427: Algorithmic Game TheoryModule M1400: Design of Dependable SystemsModule M0836: Communication NetworksModule M0926: Distributed Algorithms

Specialization II. Engineering ScienceModule M0676: Digital CommunicationsModule M0673: Information Theory and CodingModule M0846: Control Systems Theory and DesignModule M0677: Digital Signal Processing and Digital Filters

Specialization III. MathematicsModule M1428: Linear and Nonlinear OptimizationModule M0881: Mathematical Image ProcessingModule M0711: Numerical Mathematics IIModule M1405: Randomised Algorithms and Random GraphsModule M1552: Mathematics of Neural Networks

Specialization IV. Subject Specific FocusModule M1434: Technical Complementary Course I for Computational Science and EngineeringModule M1435: Technical Complementary Course II for Computational Science and Engineering

ThesisModule M-002: Master Thesis

Program description

ContentEngineering disciplines utilize the results of computer science and mathematics research to anever greater extent, both in the development of products and in the products themselves. Thistrend will certainly continue. New results in computer science and mathematics thus become animportant innovation factor in engineering and are therefore central areas of competence for anengineer and a technical university. This has a direct impact on the objectives of the computerscience and engineering course.

The main objective of the course is to provide the knowledge and skills necessary for thesuccessful application of engineering techniques in industry, trade and administration at a veryhigh level, so that the productivity of graduates is promoted in the long term.

The master's degree programme in Computer Science and Engineering provides a broad, well-founded and in-depth basic knowledge in the fields of mathematical modelling in computerscience, IT systems and engineering sciences. In addition, further knowledge in businessadministration and management as well as non-technical subjects is acquired in order to increasethe skills required to master extensive engineering IT projects. The Master's programme preparesstudents for practical professional fields of computer science as well as for a doctorate.

Career prospectsThe master degree course in Computer Science and Engineering offers excellent prospects onboth the industrial and academic job market thanks to its in-depth training in the fields ofinformation and communication technology, systems engineering and scientific computing. TheMaster's degree qualifies graduates for a doctorate.

Learning targetThe desired learning outcomes of the programme are based on the objectives listed above. All thelearning outcomes listed represent competences that are required in both corporate and researchenvironments. To differentiate it from the Computer Science and Engineering Bachelor'sprogramme, the competences listed here refer to complex problems, to the consideration ofuncertainty and to working under under-specified conditions. In the following, the learningobjectives are divided into the categories knowledge, skills, social competence and independence.

Knowledge

Knowledge is composed of facts, principles and theories in the subjects of computer science,mathematics and engineering.

1. Students are able to reproduce, define and explain (syntax, semantics, decision problems)new and advanced representation languages of computer science and mathematicsnecessary for the formal modelling of application problems, so that non-standard applicationcases can also be treated.

2. Students can reproduce advanced data and index structures for sequential and parallelalgorithms and name their advantages and disadvantages for special tasks. Students canspecify optimal algorithms for solving decision problems for formal modelling techniques, sothat (in typical cases) an acceptable runtime behaviour is obtained.

3. Students know how to integrate components so that a desired behaviour is obtained

[3]

Module Manual M.Sc. "Computational Science andEngineering"

(reductionistic and self-organising approach) while taking into account safety, reliability andfault tolerance aspects.

4. Students also know non-classical use cases of computer science and mathematicalmodelling techniques in engineering and can explain them.

5. The graduates are able to reflect research objectives, to explain relevant planning toachieve them, and to name the organisational and personnel structures in researchprojects.

Technical Skills

The ability to apply acquired knowledge in order to master tasks and thus solve problems issupported in many facets in the Computer Science and Engineering degree program.

1. Students can design interfaces that allow large and distributed systems to be built frommodules whose internals can be adapted without changing the interfaces. Students are ableto specify or develop communication structures that have desired properties and connectthe modules in an appropriate way.

2. Students can design and develop formal representational languages to solve complexproblems (syntax, semantics, decision problems), and they can assess and determine theexpressiveness required for specific applications. Students can map decision problems ofdifferent expressive formalisms to each other and thus compare the expressiveness offormalisms.

3. Students can examine algorithms for complex decision problems for completeness andcorrectness or convergence behaviour and approximation quality, and they can demonstratewhether an algorithm is optimal or for which types of inputs the worst case or the typicalcase occurs with respect to the runtime behaviour of an algorithm.

4. The student can use formal modelling techniques for engineering applications to create,verify or evaluate robust systems to solve non-trivial problems from an application context(using simulation, in terms of a data management system, as an application, etc.).

5. Students can demonstrate that desired states of a complex system (in the probable case)are achieved in time (controllability, accessibility with time constraints), and that undesiredstates are never achieved in any case or that their achievement is unlikely (safety andliveliness properties).

Social Competence

The ability and willingness to work together with others in a goal-oriented manner, to understandtheir interests and social situations, to communicate and to help shape the working environmentand life is broken down as follows for the degree course in Computer Science Engineering

1. Students describe scientific questions in a subject area of computer science, engineering ormathematics and explain in a lecture an approach they have developed to solve them,reacting appropriately to questions, additions and comments.

2. Students can form teams to solve non-trivial problems in groups with possibly vague taskdescriptions, define and distribute subtasks, make time arrangements, integrate partialsolutions. They are able to communicate efficiently and interact in a socially appropriatemanner.

3. Students explain the problems described in a scientific essay and the solutions developed inthe essay in a field of computer science or mathematics, evaluate the proposed solutions ina lecture and react to scientific questions, additions and comments.

Competence to work independently

The ability and willingness to act independently and responsibly, to reflect on one's own actionsand the actions of others, and also to further develop one's own ability to act, can be broken downas follows

1. Students independently evaluate the advantages and disadvantages of representationformalisms for specific tasks, compare different algorithms and data structures as well as

[4]


programming languages and programming tools, and independently select the best solutionin each case.

2. The graduates work independently on a scientific subfield, can present scientific approachesand results in a presentation and actively follow the presentations of other students, so thatan interactive discourse on a scientific topic is created.

3. Students integrate themselves independently into a project context and take on tasks in asoftware or hardware development project on their own responsibility.

Program structureThe curriculum of the master's degree program in Computer Science and Engineering is structuredas follows. A minimum number of credits must be earned in each of the three core areas ofcomputer science, engineering and mathematics:

1. Computer Science: 18 credits2. Engineering sciences: 12 credit points3. Mathematics: 12 credit points

To deepen their studies, students can choose lectures from the entire catalogue of technicalcourses offered by TUHH. A total of 24 credit points must be achieved. Practical knowledge andskills are taught in a research project (12 credit points). A further 12 credit points must be earnedin the courses Operation & Management and a non-technical supplementary course. The masterthesis is assessed with 30 credit points. This results in a total effort of 120 credit points. Thecurriculum contains a mobility window in such a way that students can spend the third semesterabroad.

The following three study plans describe special characteristics of the master's programme inComputer Science and Engineering.

A. Networked Embedded Systems

1. Core subjects computer science- Software security- Design of dependable systems- Communication networks

2. Core subjects engineering sciences- Digital communications- Information theory and coding

3. Core subjects mathematics- Linear and nonlinear optimization- Randomized algorithms and random graphs

4. Supplementary technical courses- Software for embedded systems- Simulation of communication networks- Wireless sensor networks- Network security

B. Dependable and Secure Systems

1. Core subjects computer science- Software security- Software verification- Design of dependable systems

2. Core subjects engineering sciences

[5]


- Digital signal processing and filters- Theory and design of control systems

3. Core subjects mathematics- Linear and nonlinear optimization- Numerical mathematics II

4. Supplementary technical courses- Robotics & navigation- Application safety- Reliability in engineering dynamics- Process automation technology

C. Algorithms for Data Engineering

1. Core subjects computer science- Software verification- Algorithms for networks- Distributed algorithms

2. Core subjects engineering sciences- Information theory and coding- Theory and design of control systems

3. Core subjects mathematics- Mathematical image processing- Hierarchical algorithms

4. Supplementary technical courses- Digital image analysis- Numerical mathematics II- Quantitative methods: statistics & operations research- Algorithmic algebra

[6]


Core qualification

Module M0523: Business & Management

ModuleResponsible Prof. Matthias Meyer

AdmissionRequirements None

RecommendedPrevious

KnowledgeNone

EducationalObjectives After taking part successfully, students have reached the following learning results

ProfessionalCompetence

Knowledge

Students are able to find their way around selected special areas ofmanagement within the scope of business management.Students are able to explain basic theories, categories, and models inselected special areas of business management.Students are able to interrelate technical and management knowledge.

Skills

Students are able to apply basic methods in selected areas of businessmanagement.Students are able to explain and give reasons for decision proposals onpractical issues in areas of business management.

PersonalCompetence

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

AutonomyStudents are capable of acquiring necessary knowledge independently bymeans of research and preparation of material.

Workload in Hours Depends on choice of coursesCredit points 6

CoursesInformation regarding lectures and courses can be found in thecorresponding module handbook published separately.

[7]


Module M0524: Non-technical Courses for Master

ModuleResponsible Dagmar Richter


RecommendedPrevious

KnowledgeNone



Knowledge

The Nontechnical Academic Programms (NTA)

imparts skills that, in view of the TUHH’s training profile, professional engineeringstudies require but are not able to cover fully. Self-reliance, self-management,collaboration and professional and personnel management competences. Thedepartment implements these training objectives in its teaching architecture, inits teaching and learning arrangements, in teaching areas and by means ofteaching offerings in which students can qualify by opting for specificcompetences and a competence level at the Bachelor’s or Master’s level. Theteaching offerings are pooled in two different catalogues for nontechnicalcomplementary courses.

The Learning Architecture

consists of a cross-disciplinarily study offering. The centrally designed teachingoffering ensures that courses in the nontechnical academic programms follow thespecific profiling of TUHH degree courses.

The learning architecture demands and trains independent educational planning asregards the individual development of competences. It also provides orientationknowledge in the form of “profiles”.

The subjects that can be studied in parallel throughout the student’s entire studyprogram - if need be, it can be studied in one to two semesters. In view of theadaptation problems that individuals commonly face in their first semesters aftermaking the transition from school to university and in order to encourageindividually planned semesters abroad, there is no obligation to study thesesubjects 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 eachother across semesters. The challenge of dealing with interdisciplinarity and avariety of stages of learning in courses are part of the learning architecture and aredeliberately encouraged in specific courses.

Fields of Teaching

are based on research findings from the academic disciplines cultural studies, socialstudies, arts, historical studies, communication studies, migration studies andsustainability research, and from engineering didactics. In addition, from the wintersemester 2014/15 students on all Bachelor’s courses will have the opportunity tolearn about business management and start-ups in a goal-oriented way.

The fields of teaching are augmented by soft skills offers and a foreign languageoffer. Here, the focus is on encouraging goal-oriented communication skills, e.g. theskills 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[8]


in the Bachelor’s and Master’s fields. These differences are reflected in the practicalexamples used, in content topics that refer to different professional applicationcontexts, 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 thedifferent team positions and different group leadership functions of Bachelor’s andMaster’s graduates in their future working life.

Specialized Competence (Knowledge)

Students can

explain specialized areas in context of the relevant non-technical disciplines,outline basic theories, categories, terminology, models, concepts or artistictechniques in the disciplines represented in the learning area,different specialist disciplines relate to their own discipline and differentiate itas well as make connections, sketch the basic outlines of how scientific disciplines, paradigms, models,instruments, methods and forms of representation in the specialized sciencesare subject to individual and socio-cultural interpretation and historicity,Can communicate in a foreign language in a manner appropriate to thesubject.

Skills

Professional Competence (Skills)

In selected sub-areas students can

apply basic and specific methods of the said scientific disciplines,aquestion a specific technical phenomena, models, theories from theviewpoint of another, aforementioned specialist discipline,to handle simple and advanced questions in aforementioned scientificdisciplines in a sucsessful manner,justify their decisions on forms of organization and application in practicalquestions in contexts that go beyond the technical relationship to the subject.

PersonalCompetence

Social Competence

Personal Competences (Social Skills)

Students will be able

to learn to collaborate in different manner,to present and analyze problems in the abovementioned fields in a partner orgroup situation in a manner appropriate to the addressees,to express themselves competently, in a culturally appropriate and gender-sensitive manner in the language of the country (as far as this study-focuswould be chosen), to explain nontechnical items to auditorium with technical backgroundknowledge.

Personal Competences (Self-reliance)

Students are able in selected areas

to reflect on their own profession and professionalism in the context of real-life fields of application

[9]


Autonomy

to organize themselves and their own learning processes to reflect and decide questions in front of a broad education backgroundto communicate a nontechnical item in a competent way in writen form orverbalyto organize themselves as an entrepreneurial subject country (as far as thisstudy-focus would be chosen)


CoursesInformation regarding lectures and courses can be found in thecorresponding module handbook published separately.

[10]


Module M1421: Research Project

CoursesTitle Typ Hrs/wk CPResearch Project IIW (L2042) Projection Course 8 12

ModuleResponsible Prof. Volker Turau


RecommendedPrevious

KnowledgeBasic knowledge and techniques in the chosen field of specialization.



KnowledgeStudents are able to acquire advanced knowledge in a specific field of ComputerScience or a closely related subject.

SkillsStudents are able to work self-dependent in a field of Computer Science or a closelyrelated field.

PersonalCompetence

Social CompetenceAutonomy

Workload in Hours Independent Study Time 248, Study Time in Lecture 112Credit points 12

Courseachievement None

Examination Study workExaminationduration and

scalePresentation of a current research topic (25-30 min and 5 min discussion).

Assignment forthe Following

CurriculaComputational Science and Engineering: Core qualification: Compulsory

Course L2042: Research Project IIWTyp Projection Course

Hrs/wk 8CP 12

Workload in Hours Independent Study Time 248, Study Time in Lecture 112Lecturer Prof. Volker Turau (sgwe)

Language DE/ENCycle WiSe/SoSe

Content Current research topics of the chosen specialization.

LiteratureAktuelle Literatur zu Forschungsthemen aus der gewählten Vertiefungsrichtung. /Current literature on research topics of the chosen specialization.

[11]


Specialization I. Computer Science

Module M0942: Software Security

CoursesTitle Typ Hrs/wk CPSoftware Security (L1103) Lecture 2 3Software Security (L1104) Recitation Section

(small) 2 3

ModuleResponsible Prof. Dieter Gollmann


RecommendedPrevious

KnowledgeFamiliarity with C/C++, web programming



Knowledge

Students can

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

Skills

Students are capable of

performing a software vulnerability analysis developing secure code

PersonalCompetence

Social Competence None

AutonomyStudents are capable of acquiring knowledge independently from professionalpublications, technical standards, and other sources, and are capable of applyingnewly acquired knowledge to new problems.



Examination Written examExaminationduration and

scale120 minutes


Curricula

Computer Science: Specialisation I. Computer and Software Engineering: ElectiveCompulsoryComputational Science and Engineering: Specialisation I. Computer Science:Elective CompulsoryInformation and Communication Systems: Specialisation Secure and Dependable ITSystems: Elective Compulsory

[12]


Course L1103: Software SecurityTyp Lecture

Hrs/wk 2CP 3

Workload in Hours Independent Study Time 62, Study Time in Lecture 28Lecturer Prof. Dieter Gollmann

Language ENCycle WiSe

Content

Reliabilty and Software SecurityAttacks exploiting character and integer representationsBuffer overrunsVulnerabilities in memory managemet: double free attacksRace conditionsSQL injectionCross-site scripting and cross-site request forgeryTesting for security; taint analysisType safe languagesDevelopment proceses for secure softwareCode-based access control

Literature

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

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

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

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

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

Course L1104: Software SecurityTyp Recitation Section (small)

Hrs/wk 2CP 3

Workload in Hours Independent Study Time 62, Study Time in Lecture 28Lecturer Prof. Dieter Gollmann


Content See interlocking courseLiterature See interlocking course

[13]


Module M0753: Software Verification

CoursesTitle Typ Hrs/wk CPSoftware Verification (L0629) Lecture 2 3Software Verification (L0630) Recitation Section

(small) 2 3

ModuleResponsible Prof. Sibylle Schupp


RecommendedPrevious

Knowledge

Automata theory and formal languagesComputational logicObject-oriented programming, algorithms, and data structuresFunctional programming or procedural programmingConcurrency



Knowledge

Students apply the major verification techniques in model checking and deductiveverification. They explain in formal terms syntax and semantics of the underlyinglogics, and assess the expressivity of different logics as well as their limitations.They classify formal properties of software systems. They find flaws in formalarguments, 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 underverification and, where necessary, adapt model or property. They construct proofsand property checks by hand or using tools for model checking or deductiveverification, and reflect on the scope of the results. Presented with a verificationproblem in natural language, they select the appropriate verification technique andjustify their choice.

PersonalCompetence

Social CompetenceStudents discuss relevant topics in class. They defend their solutions orally. Theycommunicate in English.

Autonomy

Using accompanying on-line material for self study, students can assess their levelof knowledge continuously and adjust it appropriately. Working on exerciseproblems, they receive additional feedback. Within limits, they can set their ownlearning goals. Upon successful completion, students can identify and preciselyformulate new problems in academic or applied research in the field of softwareverification. Within this field, they can conduct independent studies to acquire thenecessary competencies and compile their findings in academic reports. They candevise plans to arrive at new solutions or assess existing ones.


Courseachievement

CompulsoryBonus Form DescriptionYes 15 % Excercises

Examination Written examExaminationduration and 90 min

[14]


scale


Curricula

Computer Science: Specialisation I. Computer and Software Engineering: ElectiveCompulsoryComputational Science and Engineering: Specialisation I. Computer Science:Elective CompulsoryInformation and Communication Systems: Specialisation Communication Systems,Focus Software: Elective CompulsoryInformation and Communication Systems: Specialisation Secure and Dependable ITSystems: CompulsoryInternational Management and Engineering: Specialisation II. InformationTechnology: Elective Compulsory

Course L0629: Software VerificationTyp Lecture

Hrs/wk 2CP 3

Workload in Hours Independent Study Time 62, Study Time in Lecture 28Lecturer Prof. Sibylle Schupp


Content

Syntax and semantics of logic-based systemsDeductive verification

SpecificationProof obligationsProgram propertiesAutomated vs. interactive theorem proving

Model checkingFoundationsProperty languagesTool support

Timed automataRecent developments of verification techniques and applications

LiteratureC. Baier and J-P. Katoen, Principles of Model Checking, MIT Press 2007.M. Huth and M. Bryan, Logic in Computer Science. Modelling and Reasoningabout Systems, 2nd Edition, 2004.Selected Research Papers

Course L0630: Software VerificationTyp Recitation Section (small)

Hrs/wk 2CP 3

Workload in Hours Independent Study Time 62, Study Time in Lecture 28Lecturer Prof. Sibylle Schupp



[15]


Module M1427: Algorithmic Game Theory

CoursesTitle Typ Hrs/wk CPAlgorithmic game theory (L2060) Lecture 2 4Algorithmic game theory (L2061) Recitation Section

(large) 2 2

ModuleResponsible Prof. Matthias Mnich


RecommendedPrevious

Knowledge

Mathematics IMathematics IIAlgorithms and Data Structures



Knowledge

Students can name the basic concepts in algorithmic game theory andmechanism design. They are able to explain them using appropriateexamples.Students can discuss logical connections between these concepts. They arecapable of illustrating these connections with the help of examples.They know game and mechanism design strategies and can reproduce them.

Skills

Students can model strategic interaction systems of agents with the help ofthe concepts studied in this course. Moreover, they are capable of analyzingtheir efficiency and equilibria, by applying established methods.Students are able to discover and verify further logical connections betweenthe concepts studied in the course.For a given problem, the students can develop and execute a suitableapproach, and are able to critically evaluate the results.

PersonalCompetence

Social Competence

Students are able to work together in teams. They are capable to usemathematics as a common language.In doing so, they can communicate new concepts according to the needs oftheir cooperating partners. Moreover, they can design examples to check anddeepen the understanding of their peers.

Autonomy

Students are capable of checking their understanding of complex conceptson their own. They can specify open questions precisely and know where toget help in solving them.Students have developed sufficient persistence to be able to work for longerperiods in a goal-oriented manner on hard problems.



Examination Written examExamination

[16]


duration andscale

90 min


Curricula

Computer Science: Specialisation I. Computer and Software Engineering: ElectiveCompulsoryComputational Science and Engineering: Specialisation I. Computer Science:Elective Compulsory

Course L2060: Algorithmic game theoryTyp Lecture

Hrs/wk 2CP 4

Workload in Hours Independent Study Time 92, Study Time in Lecture 28Lecturer Prof. Matthias Mnich

Language DE/ENCycle SoSe

Content

Algorithmic game theory is a topic at the intersection of economics andcomputation. It deals with analyzing the behavior and interactions of strategicagents, who often try to maximize their incentives. The environment in which thoseagents interact is referred to as a game. We wish to understand if the agents canreach an "equilibrium", or steady state of the game, in which agents have noincentive to deviate from their chosen strategies. The algorithmic part is to designefficient methods to find equilibria in games, and to make recommendations to theagents so that they can quickly reach a state of personal satisfaction.

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

Topics:

basic equilibrium concepts (Nash equilibria, correlated equilibria, ...)strategic actions (best-response dynamics, no-regret dynamics, ...)auction design (revenue-maximizing auctions, Vickrey auctions)stable matching theory (preference aggregations, kidney exchanges, ...)price of anarchy and selfish routing (Braess' paradox, congestion games, ...)

LiteratureT. Roughgarden: Twenty Lectures on Algorithmic Game Theory, CambridgeUniversity Press, 2016.N. Nisan, T. Roughgarden, E. Tardos, V. Vazirani. Algorithmic Game Theory.Cambridge University Press, 2007.

Course L2061: Algorithmic game theoryTyp Recitation Section (large)

Hrs/wk 2CP 2




[17]


Module M1400: Design of Dependable Systems

CoursesTitle Typ Hrs/wk CPDesigning Dependable Systems (L2000) Lecture 2 3Designing Dependable Systems (L2001) Recitation Section

(small) 2 3

ModuleResponsible Prof. Görschwin Fey


RecommendedPrevious

KnowledgeBasic knowledge about data structures and algorithms



Knowledge

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

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

Structural solutions like modular redundancyAlgorithmic solutions like handling byzantine faults or checkpointing

Knowledge about methods for the analysis of dependable systems

Skills

Ability to implement dependable systems using the above approaches.

Ability to analyzs the dependability of systems using the above methods foranalysis.

PersonalCompetence

Social Competence

Students

discuss relevant topics in class andpresent their solutions orally.

Autonomy Using accompanying material students independently learn in-depth relationsbetween concepts explained in the lecture and additional solution strategies.


Courseachievement

CompulsoryBonus Form Description

Yes None Subject theoretical andpractical work

Die Lösung einer Aufgabe istZuslassungsvoraussetzung fürdie Prüfung. Die Aufgabe wirdin Vorlesung und Übungdefiniert.

Examination Oral examExaminationduration and

scale30 min

Computer Science: Specialisation I. Computer and Software Engineering: ElectiveCompulsory

[18]



Curricula

Computational Science and Engineering: Specialisation I. Computer Science:Elective CompulsoryInformation and Communication Systems: Specialisation Secure and Dependable ITSystems: Elective CompulsoryMechatronics: Specialisation System Design: Elective CompulsoryMicroelectronics and Microsystems: Specialisation Embedded Systems: ElectiveCompulsory

Course L2000: Designing Dependable SystemsTyp Lecture

Hrs/wk 2CP 3

Workload in Hours Independent Study Time 62, Study Time in Lecture 28Lecturer Prof. Görschwin Fey


Content

Description

The term dependability comprises various aspects of a system. These are typically:

ReliabilityAvailabilityMaintainabilitySafetySecurity

This makes dependability a core aspect that has to be considered early in systemdesign, no matter whether software, embedded systems or full scale cyber-physicalsystems are considered. Contents

The module introduces the basic concepts for the design and the analysis ofdependable systems. Design examples for getting practical hands-on-experience independable design techniques. The module focuses towards embedded systems.The following topics are covered:

ModellingFault ToleranceDesign ConceptsAnalysis Techniques

Literature

Course L2001: Designing Dependable SystemsTyp Recitation Section (small)

Hrs/wk 2CP 3

Workload in Hours Independent Study Time 62, Study Time in Lecture 28Lecturer Prof. Görschwin Fey



[19]


Module M0836: Communication Networks

CoursesTitle Typ Hrs/wk CPSelected Topics of Communication Networks (L0899) Project-/problem-

based Learning 2 2Communication Networks (L0897) Lecture 2 2Communication Networks Excercise (L0898) Project-/problem-

based Learning 1 2

ModuleResponsible Prof. Andreas Timm-Giel


RecommendedPrevious

Knowledge

Fundamental stochasticsBasic understanding of computer networks and/or communicationtechnologies is beneficial



Knowledge

Students are able to describe the principles and structures of communicationnetworks in detail. They can explain the formal description methods ofcommunication networks and their protocols. They are able to explain how currentand complex communication networks work and describe the current research inthese examples.

Skills

Students are able to evaluate the performance of communication networks usingthe learned methods. They are able to work out problems themselves and apply thelearned methods. They can apply what they have learned autonomously on furtherand new communication networks.

PersonalCompetence

Social CompetenceStudents are able to define tasks themselves in small teams and solve theseproblems together using the learned methods. They can present the obtainedresults. They are able to discuss and critically analyse the solutions.

AutonomyStudents are able to obtain the necessary expert knowledge for understanding thefunctionality and performance capabilities of new communication networksindependently.



Examination PresentationExaminationduration 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 thetopics of the module.Electrical Engineering: Specialisation Information and Communication Systems:Elective CompulsoryElectrical Engineering: Specialisation Control and Power Systems Engineering:Elective CompulsoryAircraft Systems Engineering: Specialisation Avionic Systems: Elective CompulsoryComputational Science and Engineering: Specialisation I. Computer Science:Elective Compulsory

[20]



Curricula

Information and Communication Systems: Specialisation Secure and Dependable ITSystems, Focus Networks: Elective CompulsoryInformation and Communication Systems: Specialisation Communication Systems:Elective CompulsoryInternational Management and Engineering: Specialisation II. InformationTechnology: Elective CompulsoryMechatronics: Technical Complementary Course: Elective CompulsoryMicroelectronics and Microsystems: Specialisation Communication and SignalProcessing: Elective Compulsory

Course L0899: Selected Topics of Communication NetworksTyp Project-/problem-based Learning

Hrs/wk 2CP 2

Workload in Hours Independent Study Time 32, Study Time in Lecture 28Lecturer Prof. Andreas Timm-Giel


ContentExample networks selected by the students will be researched on in a PBL courseby the students in groups and will be presented in a poster session at the end of theterm.

Literature see lecture

Course L0897: Communication NetworksTyp Lecture

Hrs/wk 2CP 2



Content

Literature

Skript des Instituts für KommunikationsnetzeTannenbaum, Computernetzwerke, Pearson-Studium

Further literature is announced at the beginning of the lecture.

[21]


Course L0898: Communication Networks ExcerciseTyp Project-/problem-based Learning

Hrs/wk 1CP 2



ContentPart of the content of the lecture Communication Networks are reflected incomputing tasks in groups, others are motivated and addressed in the form of a PBLexercise.

Literature announced during lecture

[22]


Module M0926: Distributed Algorithms

CoursesTitle Typ Hrs/wk CPDistributed Algorithms (L1071) Lecture 2 3Distributed Algorithms (L1072) Recitation Section

(large) 2 3



RecommendedPrevious

Knowledge

Algorithms and data structuresDistributed systemsDiscrete mathematicsGraph theory



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 algorithmsfor important problems such as leader election, mutual exclusion, graph coloring,spanning trees. They know the fundamental techniques used for randomizedalgorithms.

SkillsStudents design their own distributed algorithms and analyze their complexity.They make use of known standard algorithms. They compute the complexity ofrandomized algorithms.

PersonalCompetence





scale45 min


Curricula

Computer Science: Specialisation I. Computer and Software Engineering: ElectiveCompulsoryComputational Science and Engineering: Specialisation I. Computer Science:Elective Compulsory

[23]


Course L1071: Distributed AlgorithmsTyp Lecture

Hrs/wk 2CP 3

Workload in Hours Independent Study Time 62, Study Time in Lecture 28Lecturer Prof. Volker Turau

Language DE/ENCycle WiSe

Content

Leader ElectionColorings & Independent SetsTree AlgorithmsMinimal Spanning TreesRandomized Distributed AlgorithmsMutual Exclusion

Literature

1. David Peleg: Distributed Computing - A Locality-Sensitive Approach. SIAMMonograph, 2000

2. Gerard Tel: Introduction to Distributed Algorithms, Cambridge UniversityPress, 2nd edition, 2000

3. Nancy Lynch: Distributed Algorithms. Morgan Kaufmann, 19964. Volker Turau: Algorithmische Graphentheorie. Oldenbourg

Wissenschaftsverlag, 3. Auflage, 2004.

Course L1072: Distributed AlgorithmsTyp Recitation Section (large)

Hrs/wk 2CP 3

Workload in Hours Independent Study Time 62, Study Time in Lecture 28Lecturer Prof. Volker Turau



[24]


Specialization II. Engineering Science

Module M0676: Digital Communications

CoursesTitle Typ Hrs/wk CPDigital Communications (L0444) Lecture 2 3Digital Communications (L0445) Recitation Section

(large) 2 2Laboratory Digital Communications (L0646) Practical Course 1 1

ModuleResponsible Prof. Gerhard Bauch


RecommendedPrevious

Knowledge

Mathematics 1-3Signals and SystemsFundamentals of Communications and Random Processes



Knowledge

The students are able to understand, compare and design modern digitalinformation transmission schemes. They are familiar with the properties of linearand non-linear digital modulation methods. They can describe distortions caused bytransmission channels and design and evaluate detectors including channelestimation and equalization. They know the principles of single carrier transmissionand multi-carrier transmission as well as the fundamentals of basic multiple accessschemes.

Skills

The students are able to design and analyse a digital information transmissionscheme including multiple access. They are able to choose a digital modulationscheme taking into account transmission rate, required bandwidth, error probability,and further signal properties. They can design an appropriate detector includingchannel estimation and equalization taking into account performance andcomplexity properties of suboptimum solutions. They are able to set parameters of asingle carrier or multi carrier transmission scheme and trade the properties of bothapproaches against each other.

PersonalCompetence

Social Competence The students can jointly solve specific problems.

AutonomyThe students are able to acquire relevant information from appropriate literaturesources. They can control their level of knowledge during the lecture period bysolving tutorial problems, software tools, clicker system.


Courseachievement

CompulsoryBonus Form DescriptionYes None Written elaboration


scale90 min

Electrical Engineering: Core qualification: Compulsory

[25]



Curricula

Computational Science and Engineering: Specialisation II. Engineering Science:Elective CompulsoryInformation and Communication Systems: Specialisation Communication Systems:CompulsoryInformation and Communication Systems: Specialisation Secure and Dependable ITSystems, Focus Networks: Elective CompulsoryInternational Management and Engineering: Specialisation II. InformationTechnology: Elective CompulsoryInternational Management and Engineering: Specialisation II. Electrical Engineering:Elective CompulsoryMicroelectronics and Microsystems: Core qualification: Elective Compulsory

Course L0444: Digital CommunicationsTyp Lecture

Hrs/wk 2CP 3

Workload in Hours Independent Study Time 62, Study Time in Lecture 28Lecturer Prof. Gerhard Bauch


Content

Digital modulation methods

Coherent and non-coherent detection

Channel estimation and equalization

Single-Carrier- and multi carrier transmission schemes, multiple accessschemes (TDMA, FDMA, CDMA, OFDM)

Literature

K. Kammeyer: Nachrichtenübertragung, Teubner

P.A. Höher: Grundlagen der digitalen Informationsübertragung, Teubner.

J.G. Proakis, M. Salehi: Digital Communications. McGraw-Hill.

S. Haykin: Communication Systems. Wiley

R.G. Gallager: Principles of Digital Communication. Cambridge

A. Goldsmith: Wireless Communication. Cambridge.

D. Tse, P. Viswanath: Fundamentals of Wireless Communication. Cambridge.

Course L0445: Digital CommunicationsTyp Recitation Section (large)

Hrs/wk 2CP 2




[26]


Course L0646: Laboratory Digital CommunicationsTyp Practical Course

Hrs/wk 1CP 1



Content

- DSL transmission

- Random processes

- Digital data transmission

Literature

K. Kammeyer: Nachrichtenübertragung, Teubner

P.A. Höher: Grundlagen der digitalen Informationsübertragung, Teubner.

J.G. Proakis, M. Salehi: Digital Communications. McGraw-Hill.

S. Haykin: Communication Systems. Wiley

R.G. Gallager: Principles of Digital Communication. Cambridge

A. Goldsmith: Wireless Communication. Cambridge.

D. Tse, P. Viswanath: Fundamentals of Wireless Communication. Cambridge.

[27]


Module M0673: Information Theory and Coding

CoursesTitle Typ Hrs/wk CPInformation Theory and Coding (L0436) Lecture 3 4Information Theory and Coding (L0438) Recitation Section

(large) 2 2



RecommendedPrevious

Knowledge

Mathematics 1-3Probability theory and random processesBasic knowledge of communications engineering (e.g. from lecture"Fundamentals of Communications and Random Processes")



Knowledge

The students know the basic definitions for quantification of information in the senseof information theory. They know Shannon's source coding theorem and channelcoding theorem and are able to determine theoretical limits of data compressionand error-free data transmission over noisy channels. They understand theprinciples of source coding as well as error-detecting and error-correcting channelcoding. They are familiar with the principles of decoding, in particular with modernmethods of iterative decoding. They know fundamental coding schemes, theirproperties and decoding algorithms.

Skills

The students are able to determine the limits of data compression as well as of datatransmission through noisy channels and based on those limits to design basicparameters of a transmission scheme. They can estimate the parameters of anerror-detecting or error-correcting channel coding scheme for achieving certainperformance targets. They are able to compare the properties of basic channelcoding and decoding schemes regarding error correction capabilities, decodingdelay, decoding complexity and to decide for a suitable method. They are capableof implementing basic coding and decoding schemes in software.

PersonalCompetence






scale90 min


Curricula

Electrical Engineering: Specialisation Information and Communication Systems:Elective CompulsoryComputational Science and Engineering: Specialisation II. Engineering Science:Elective CompulsoryInformation and Communication Systems: Core qualification: Compulsory

[28]


International Management and Engineering: Specialisation II. Electrical Engineering:Elective CompulsoryMechatronics: Technical Complementary Course: Elective Compulsory

Course L0436: Information Theory and CodingTyp Lecture

Hrs/wk 3CP 4



Content

Fundamentals of information theory

Self information, entropy, mutual information

Source coding theorem, channel coding theorem

Channel capacity of various channels

Fundamental source coding algorithms:

Huffman Code, Lempel Ziv Algorithm

Fundamentals of channel coding

Basic parameters of channel coding and respective bounds

Decoding principles: Maximum-A-Posteriori Decoding, Maximum-Likelihood Decoding, Hard-Decision-Decoding and Soft-Decision-Decoding

Error probability

Block codes

Low Density Parity Check (LDPC) Codes and iterative Ddecoding

Convolutional codes and Viterbi-Decoding

Turbo Codes and iterative decoding

Coded Modulation

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.

[29]


Course L0438: Information Theory and CodingTyp Recitation Section (large)

Hrs/wk 2CP 2




[30]


Module M0846: Control Systems Theory and Design

CoursesTitle Typ Hrs/wk CPControl Systems Theory and Design (L0656) Lecture 2 4Control Systems Theory and Design (L0657) Recitation Section

(small) 2 2

ModuleResponsible Prof. Herbert Werner


RecommendedPrevious

KnowledgeIntroduction to Control Systems



Knowledge

Students can explain how linear dynamic systems are represented as statespace models; they can interpret the system response to initial states orexternal excitation as trajectories in state spaceThey can explain the system properties controllability and observability, andtheir relationship to state feedback and state estimation, respectivelyThey can explain the significance of a minimal realisationThey can explain observer-based state feedback and how it can be used toachieve tracking and disturbance rejectionThey can extend all of the above to multi-input multi-output systemsThey can explain the z-transform and its relationship with the LaplaceTransformThey can explain state space models and transfer function models of discrete-time systemsThey can explain the experimental identification of ARX models of dynamicsystems, and how the identification problem can be solved by solving anormal equationThey can explain how a state space model can be constructed from adiscrete-time impulse response

Skills

Students can transform transfer function models into state space models andvice versaThey can assess controllability and observability and construct minimalrealisationsThey can design LQG controllers for multivariable plants They can carry out a controller design both in continuous-time and discrete-time domain, and decide which is appropriate for a given sampling rateThey can identify transfer function models and state space models ofdynamic systems from experimental dataThey can carry out all these tasks using standard software tools (MatlabControl Toolbox, System Identification Toolbox, Simulink)

PersonalCompetence

Social Competence Students can work in small groups on specific problems to arrive at joint solutions.

Students can obtain information from provided sources (lecture notes, softwaredocumentation, experiment guides) and use it when solving given problems.

[31]


Autonomy They can assess their knowledge in weekly on-line tests and thereby control theirlearning progress.




scale120 min


Curricula

Electrical Engineering: Core qualification: CompulsoryEnergy Systems: Core qualification: Elective CompulsoryAircraft Systems Engineering: Specialisation Aircraft Systems: CompulsoryAircraft Systems Engineering: Specialisation Avionic Systems: Elective CompulsoryComputational Science and Engineering: Specialisation II. Engineering Science:Elective CompulsoryInternational Management and Engineering: Specialisation II. Electrical Engineering:Elective CompulsoryInternational Management and Engineering: Specialisation II. Mechatronics: ElectiveCompulsoryMechanical Engineering and Management: Specialisation Mechatronics: ElectiveCompulsoryMechatronics: Core qualification: CompulsoryBiomedical Engineering: Specialisation Artificial Organs and Regenerative Medicine:Elective CompulsoryBiomedical Engineering: Specialisation Implants and Endoprostheses: ElectiveCompulsoryBiomedical Engineering: Specialisation Medical Technology and Control Theory:CompulsoryBiomedical Engineering: Specialisation Management and Business Administration:Elective CompulsoryProduct Development, Materials and Production: Core qualification: ElectiveCompulsoryTheoretical Mechanical Engineering: Core qualification: Compulsory

[32]


Course L0656: Control Systems Theory and DesignTyp Lecture

Hrs/wk 2CP 4

Workload in Hours Independent Study Time 92, Study Time in Lecture 28Lecturer Prof. Herbert Werner


Content

State space methods (single-input single-output)

• State space models and transfer functions, state feedback • Coordinate basis, similarity transformations • Solutions of state equations, matrix exponentials, Caley-Hamilton Theorem• Controllability and pole placement • State estimation, observability, Kalman decomposition • Observer-based state feedback control, reference tracking • Transmission zeros• Optimal pole placement, symmetric root locus Multi-input multi-output systems• Transfer function matrices, state space models of multivariable systems, Gilbertrealization • Poles and zeros of multivariable systems, minimal realization • Closed-loop stability• Pole placement for multivariable systems, LQR design, Kalman filter

Digital Control• Discrete-time systems: difference equations and z-transform • Discrete-time state space models, sampled data systems, poles and zeros • Frequency response of sampled data systems, choice of sampling rate

System identification and model order reduction • Least squares estimation, ARX models, persistent excitation • Identification of state space models, subspace identification • Balanced realization and model order reduction

Case study• Modelling and multivariable control of a process evaporator using Matlab andSimulink Software tools• Matlab/Simulink

Literature

Werner, H., Lecture Notes „Control Systems Theory and Design“T. Kailath "Linear Systems", Prentice Hall, 1980K.J. Astrom, B. Wittenmark "Computer Controlled Systems" Prentice Hall,1997L. Ljung "System Identification - Theory for the User", Prentice Hall, 1999

Course L0657: Control Systems Theory and DesignTyp Recitation Section (small)

Hrs/wk 2CP 2

Workload in Hours Independent Study Time 32, Study Time in Lecture 28Lecturer Prof. Herbert Werner



[33]


Module M0677: Digital Signal Processing and Digital Filters

CoursesTitle Typ Hrs/wk CPDigital Signal Processing and Digital Filters (L0446) Lecture 3 4Digital Signal Processing and Digital Filters (L0447) Recitation Section

(large) 2 2



RecommendedPrevious

Knowledge

Mathematics 1-3Signals and SystemsFundamentals of signal and system theory as well as random processes.Fundamentals of spectral transforms (Fourier series, Fourier transform,Laplace transform)



Knowledge

The students know and understand basic algorithms of digital signal processing.They are familiar with the spectral transforms of discrete-time signals and are ableto describe and analyse signals and systems in time and image domain. They knowbasic structures of digital filters and can identify and assess important propertiesincluding stability. They are aware of the effects caused by quantization of filtercoefficients and signals. They are familiar with the basics of adaptive filters. Theycan perform traditional and parametric methods of spectrum estimation, also takinga limited observation window into account.

Skills

The students are able to apply methods of digital signal processing to newproblems. They can choose and parameterize suitable filter striuctures. In particular,the can design adaptive filters according to the minimum mean squared error(MMSE) criterion and develop an efficient implementation, e.g. based on the LMS orRLS algorithm. Furthermore, the students are able to apply methods of spectrumestimation and to take the effects of a limited observation window into account.

PersonalCompetence






scale90 min

Electrical Engineering: Specialisation Control and Power Systems Engineering:Elective CompulsoryComputational Science and Engineering: Specialisation II. Engineering Science:Elective CompulsoryInformation and Communication Systems: Specialisation Communication Systems,Focus Signal Processing: Elective Compulsory

[34]



Curricula

Mechanical Engineering and Management: Specialisation Mechatronics: ElectiveCompulsoryMechatronics: Specialisation Intelligent Systems and Robotics: Elective CompulsoryMicroelectronics and Microsystems: Specialisation Communication and SignalProcessing: Elective CompulsoryMicroelectronics and Microsystems: Specialisation Communication and SignalProcessing: Elective CompulsoryTheoretical Mechanical Engineering: Technical Complementary Course: ElectiveCompulsoryTheoretical Mechanical Engineering: Specialisation Robotics and Computer Science:Elective CompulsoryTheoretical Mechanical Engineering: Specialisation Numerics and Computer Science:Elective Compulsory

[35]


Course L0446: Digital Signal Processing and Digital FiltersTyp Lecture

Hrs/wk 3CP 4



Content

Transforms of discrete-time signals:

Discrete-time Fourier Transform (DTFT)

Discrete Fourier-Transform (DFT), Fast Fourier Transform (FFT)

Z-Transform

Correspondence of continuous-time and discrete-time signals, sampling,sampling theorem

Fast convolution, Overlap-Add-Method, Overlap-Save-Method

Fundamental structures and basic types of digital filters

Characterization of digital filters using pole-zero plots, important properties ofdigital filters

Quantization effects

Design of linear-phase filters

Fundamentals of stochastic signal processing and adaptive filters

MMSE criterion

Wiener Filter

LMS- and RLS-algorithm

Traditional and parametric methods of spectrum estimation

Literature

K.-D. Kammeyer, K. Kroschel: Digitale Signalverarbeitung. Vieweg Teubner.

V. Oppenheim, R. W. Schafer, J. R. Buck: Zeitdiskrete Signalverarbeitung. PearsonStudiumA. V.

W. Hess: Digitale Filter. Teubner.

Oppenheim, R. W. Schafer: Digital signal processing. Prentice Hall.

S. Haykin: Adaptive flter theory.

L. B. Jackson: Digital filters and signal processing. Kluwer.

T.W. Parks, C.S. Burrus: Digital filter design. Wiley.

[36]


Course L0447: Digital Signal Processing and Digital FiltersTyp Recitation Section (large)

Hrs/wk 2CP 2




[37]


Specialization III. Mathematics

Module M1428: Linear and Nonlinear Optimization

CoursesTitle Typ Hrs/wk CPLinear and Nonlinear Optimization (L2062) Lecture 4 4Linear and Nonlinear Optimization (L2063) Recitation Section

(large) 1 2

ModuleResponsible Prof. Matthias Mnich


RecommendedPrevious

Knowledge

Discrete Algebraic StructuresMathematics IGraph Theory and Optimization



Knowledge

Students can name the basic concepts in linear and non-linear optimization.They are able to explain them using appropriate examples.Students can discuss logical connections between these concepts. They arecapable of illustrating these connections with the help of examples.They know proof strategies and can reproduce them.

Skills

Students can model problems in linear and non-linear optimization with thehelp of the concepts studied in this course. Moreover, they are capable ofsolving them by applying established methods.Students are able to discover and verify further logical connections betweenthe concepts studied in the course.For a given problem, the students can develop and execute a suitableapproach, and are able to critically evaluate the results.

PersonalCompetence

Social Competence

Students are able to work together in teams. They are capable to usemathematics as a common language.In doing so, they can communicate new concepts according to the needs oftheir cooperating partners. Moreover, they can design examples to check anddeepen the understanding of their peers.

Autonomy

Students are capable of checking their understanding of complex conceptson their own. They can specify open questions precisely and know where toget help in solving them.Students have developed sufficient persistence to be able to work for longerperiods in a goal-oriented manner on hard problems.



[38]



scale30 min


Curricula

Computer Science: Specialisation III. Mathematics: Elective CompulsoryComputational Science and Engineering: Specialisation III. Mathematics: ElectiveCompulsory

Course L2062: Linear and Nonlinear OptimizationTyp Lecture

Hrs/wk 4CP 4



Content

Modelling linear programming problemsGraphical methodAlgebraic backgroundConvexityPolyhedral theorySimplex methodDegeneracy and convergencedualityinterior-point methodsquadratic optimizationinteger linear programming

Literature

A. Schrijver: Combinatorial Optimization: Polyhedra and Efficiency. Springer,2003B. Korte and T. Vygen: Combinatorial Optimization: Theory and Algorithms.Springer, 2018T. Cormen, Ch. Leiserson, R. Rivest, C. Stein: Introduction to Algorithms. MITPress, 2013

Course L2063: Linear and Nonlinear OptimizationTyp Recitation Section (large)

Hrs/wk 1CP 2




[39]


Module M0881: Mathematical Image Processing

CoursesTitle Typ Hrs/wk CPMathematical Image Processing (L0991) Lecture 3 4Mathematical Image Processing (L0992) Recitation Section

(small) 1 2

ModuleResponsible Prof. Marko Lindner


RecommendedPrevious

KnowledgeAnalysis: partial derivatives, gradient, directional derivativeLinear Algebra: eigenvalues, least squares solution of a linear system



Knowledge

Students are able to

characterize and compare diffusion equationsexplain elementary methods of image processing explain methods of image segmentation and registrationsketch and interrelate basic concepts of functional analysis

Skills


implement and apply elementary methods of image processing explain and apply modern methods of image processing

PersonalCompetence

Social CompetenceStudents are able to work together in heterogeneously composed teams (i.e., teamsfrom different study programs and background knowledge) and to explaintheoretical foundations.

Autonomy

Students are capable of checking their understanding of complex concepts ontheir own. They can specify open questions precisely and know where to gethelp in solving them. Students have developed sufficient persistence to be able to work for longerperiods in a goal-oriented manner on hard problems.




scale20 min

Assignment for

Bioprocess Engineering: Specialisation A - General Bioprocess Engineering: ElectiveCompulsoryComputer Science: Specialisation III. Mathematics: Elective CompulsoryComputational Science and Engineering: Specialisation III. Mathematics: ElectiveCompulsoryMechatronics: Technical Complementary Course: Elective CompulsoryMechatronics: Specialisation Intelligent Systems and Robotics: Elective Compulsory

[40]


the FollowingCurricula

Mechatronics: Specialisation System Design: Elective CompulsoryTechnomathematics: Specialisation I. Mathematics: Elective CompulsoryTheoretical Mechanical Engineering: Technical Complementary Course: ElectiveCompulsoryTheoretical Mechanical Engineering: Specialisation Robotics and Computer Science:Elective CompulsoryTheoretical Mechanical Engineering: Specialisation Numerics and Computer Science:Elective CompulsoryProcess Engineering: Specialisation Process Engineering: Elective Compulsory

Course L0991: Mathematical Image ProcessingTyp Lecture

Hrs/wk 3CP 4

Workload in Hours Independent Study Time 78, Study Time in Lecture 42Lecturer Prof. Marko Lindner


Content

basic methods of image processingsmoothing filtersthe diffusion / heat equationvariational formulations in image processingedge detectionde-convolutioninpaintingimage segmentationimage registration

Literature Bredies/Lorenz: Mathematische Bildverarbeitung

Course L0992: Mathematical Image ProcessingTyp Recitation Section (small)

Hrs/wk 1CP 2

Workload in Hours Independent Study Time 46, Study Time in Lecture 14Lecturer Prof. Marko Lindner



[41]


Module M0711: Numerical Mathematics II

CoursesTitle Typ Hrs/wk CPNumerical Mathematics II (L0568) Lecture 2 3Numerical Mathematics II (L0569) Recitation Section

(small) 2 3

ModuleResponsible Prof. Sabine Le Borne


RecommendedPrevious

KnowledgeNumerical Mathematics IMATLAB knowledge



Knowledge


name advanced numerical methods for interpolation, integration, linear leastsquares problems, eigenvalue problems, nonlinear root finding problems andexplain their core ideas,repeat convergence statements for the numerical methods,sketch convergence proofs,explain practical aspects of numerical methods concerning runtime andstorage needs

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

Skills


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

PersonalCompetence

Social Competence


work together in heterogeneously composed teams (i.e., teams fromdifferent study programs and background knowledge), explain theoreticalfoundations and support each other with practical aspects regarding theimplementation of algorithms.

Autonomy

Students are capable

to assess whether the supporting theoretical and practical excercises arebetter solved individually or in a team,to assess their individual progess and, if necessary, to ask questions and

[42]


seek help.




scale25 min


Curricula

Computer Science: Specialisation III. Mathematics: Elective CompulsoryComputational Science and Engineering: Specialisation III. Mathematics: ElectiveCompulsoryTechnomathematics: Specialisation I. Mathematics: Elective CompulsoryTheoretical Mechanical Engineering: Technical Complementary Course: ElectiveCompulsoryTheoretical Mechanical Engineering: Core qualification: Elective Compulsory

Course L0568: Numerical Mathematics IITyp Lecture

Hrs/wk 2CP 3

Workload in Hours Independent Study Time 62, Study Time in Lecture 28Lecturer Prof. Sabine Le Borne, Dr. Jens-Peter Zemke


Content

1. Error and stability: Notions and estimates2. Interpolation: Rational and trigonometric interpolation3. Quadrature: Gaussian quadrature, orthogonal polynomials4. Linear systems: Perturbation theory of decompositions, structured matrices5. Eigenvalue problems: LR-, QD-, QR-Algorithmus6. Krylov space methods: Arnoldi-, Lanczos methods

LiteratureStoer/Bulirsch: Numerische Mathematik 1, SpringerDahmen, Reusken: Numerik für Ingenieure und Naturwissenschaftler,Springer

Course L0569: Numerical Mathematics IITyp Recitation Section (small)

Hrs/wk 2CP 3

Workload in Hours Independent Study Time 62, Study Time in Lecture 28Lecturer Prof. Sabine Le Borne, Dr. Jens-Peter Zemke



[43]


Module M1405: Randomised Algorithms and Random Graphs

CoursesTitle Typ Hrs/wk CPRandomised Algorithms and Random Graphs (L2010) Lecture 2 3Randomised Algorithms and Random Graphs (L2011) Recitation Section

(large) 2 3

ModuleResponsible Prof. Anusch Taraz


RecommendedPrevious

KnowledgeEducationalObjectives After taking part successfully, students have reached the following learning results


Knowledge

Students can describe basic concepts in the area of Randomized Algorithmsand Random Graphs such as random walks, tail bounds, fingerprinting andalgebraic techniques, first and second moment methods, and various randomgraph models. They are able to explain them using appropriate examples.Students can discuss logical connections between these concepts. They arecapable of illustrating these connections with the help of examples.They know proof strategies and can apply them.

Skills

Students can model problems with the help of the concepts studied in thiscourse. Moreover, they are capable of solving them by applying establishedmethods.Students are able to explore and verify further logical connections betweenthe concepts studied in the course.For a given problem, the students can develop and execute a suitabletechnique, and are able to critically evaluate the results.

PersonalCompetence

Social Competence

Students are able to work together in teams. They are capable to establish acommon language.In doing so, they can communicate new concepts according to the needs oftheir cooperating partners. Moreover, they can design examples to check anddeepen the understanding of their peers.

Autonomy

Students are capable of checking their understanding of complex concepts ontheir own. They can specify open questions precisely and know where to gethelp in solving them.Students have developed sufficient persistence to be able to work for longerperiods in a goal-oriented manner on hard problems.



Examination Oral exam

[44]


Examinationduration and

scale30 min


Curricula

Computer Science: Specialisation III. Mathematics: Elective CompulsoryComputational Science and Engineering: Specialisation III. Mathematics: ElectiveCompulsoryMathematical Modelling in Engineering: Theory, Numerics, Applications:Specialisation l. Numerics (TUHH): Elective Compulsory

Course L2010: Randomised Algorithms and Random GraphsTyp Lecture

Hrs/wk 2CP 3

Workload in Hours Independent Study Time 62, Study Time in Lecture 28Lecturer Prof. Anusch Taraz, Prof. Volker Turau


Content

Randomized Algorithms:

introduction and recalling basic tools from probabilityrandomized searchrandom walkstext search with fingerprintingparallel and distributed algorithmsonline algorithms

Random Graphs:

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

Literature

Motwani, Raghavan: Randomized AlgorithmsWorsch: Randomisierte AlgorithmenDietzfelbinger: Randomisierte AlgorithmenBollobas: Random GraphsAlon, Spencer: The Probabilistic MethodFrieze, Karonski: Random Graphsvan der Hofstad: Random Graphs and Complex Networks

[45]


Course L2011: Randomised Algorithms and Random GraphsTyp Recitation Section (large)

Hrs/wk 2CP 3

Workload in Hours Independent Study Time 62, Study Time in Lecture 28Lecturer Prof. Anusch Taraz, Prof. Volker Turau



[46]


Module M1552: Mathematics of Neural Networks

CoursesTitle Typ Hrs/wk CPMathematics of Neural Networks (L2322) Lecture 2 3Mathematics of Neural Networks (L2323) Recitation Section

(small) 2 3

ModuleResponsible Dr. Jens-Peter Zemke


RecommendedPrevious

Knowledge

1. Mathematics I-III2. Numerical Mathematics 1/ Numerics3. Programming skills, preferably in Python



KnowledgeStudents are able to name, state and classify state-of-the-art neural networks andtheir corresponding mathematical basics. They can assess the difficulties ofdifferent neural networks.

Skills Students are able to implement, understand, and, tailored to the field of application,apply neural networks.

PersonalCompetence

Social Competence

Students can

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

Autonomy


correctly assess the time and effort of self-defined work;assess whether the supporting theoretical and practical excercises are bettersolved individually or in a team;define test problems for testing and expanding the methods;assess their individual progess and, if necessary, to ask questions and seekhelp.




scale25 min


Curricula

Computer Science: Specialisation Intelligence Engineering: Elective CompulsoryComputer Science: Specialisation III. Mathematics: Elective CompulsoryComputational Science and Engineering: Specialisation III. Mathematics: ElectiveCompulsoryTechnomathematics: Specialisation I. Mathematics: Elective CompulsoryTheoretical Mechanical Engineering: Specialisation Robotics and Computer Science:Elective Compulsory

[47]


Course L2322: Mathematics of Neural NetworksTyp Lecture

Hrs/wk 2CP 3

Workload in Hours Independent Study Time 62, Study Time in Lecture 28Lecturer Dr. Jens-Peter Zemke


Content

1. Basics: analogy; layout of neural nets, universal approximation, NP-completeness

2. Feedforward nets: backpropagation, variants of Stochastistic Gradients3. Deep Learning: problems and solution strategies4. Deep Belief Networks: energy based models, Contrastive Divergence5. CNN: idea, layout, FFT and Winograds algorithms, implementation details6. RNN: idea, dynamical systems, training, LSTM7. ResNN: idea, relation to neural ODEs8. Standard libraries: Tensorflow, Keras, PyTorch9. Recent trends

Literature

1. Skript2. Online-Werke:

http://neuralnetworksanddeeplearning.com/https://www.deeplearningbook.org/

Course L2323: Mathematics of Neural NetworksTyp Recitation Section (small)

Hrs/wk 2CP 3

Workload in Hours Independent Study Time 62, Study Time in Lecture 28Lecturer Dr. Jens-Peter Zemke



[48]


Specialization IV. Subject Specific Focus

Module M1434: Technical Complementary Course I for ComputationalScience and Engineering

CoursesTitle Typ Hrs/wk CP



RecommendedPrevious



KnowledgeSkills

PersonalCompetence




CurriculaComputational Science and Engineering: Specialisation IV. Subject Specific Focus:Elective Compulsory

[49]


Module M1435: Technical Complementary Course II for ComputationalScience and Engineering




RecommendedPrevious



KnowledgeSkills

PersonalCompetence




CurriculaComputational Science and Engineering: Specialisation IV. Subject Specific Focus:Elective Compulsory

[50]


Thesis

Module M-002: Master Thesis


ModuleResponsible Professoren der TUHH

AdmissionRequirements

According to General Regulations §21 (1):

At least 60 credit points have to be achieved in study programme. Theexaminations board decides on exceptions.

RecommendedPrevious



Knowledge

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

Skills

The students are able:

To select, apply and, if necessary, develop further methods that are suitablefor solving the specialized problem in question.To apply knowledge they have acquired and methods they have learnt in thecourse of their studies to complex and/or incompletely defined problems in asolution-oriented way.To develop new scientific findings in their subject area and subject them to acritical assessment.

PersonalCompetence

Social Competence

Students can

Both in writing and orally outline a scientific issue for an expert audienceaccurately, understandably and in a structured way.Deal with issues competently in an expert discussion and answer them in amanner that is appropriate to the addressees while upholding their ownassessments and viewpoints convincingly.

Autonomy

Students are able:

To structure a project of their own in work packages and to work them offaccordingly.To work their way in depth into a largely unknown subject and to access theinformation required for them to do so.

[51]


To apply the techniques of scientific work comprehensively in research oftheir own.



Examination ThesisExaminationduration and

scaleAccording to General Regulations


Curricula

Civil Engineering: Thesis: CompulsoryBioprocess Engineering: Thesis: CompulsoryChemical and Bioprocess Engineering: Thesis: CompulsoryComputer Science: Thesis: CompulsoryElectrical Engineering: Thesis: CompulsoryEnergy and Environmental Engineering: Thesis: CompulsoryEnergy Systems: Thesis: CompulsoryEnvironmental Engineering: Thesis: CompulsoryAircraft Systems Engineering: Thesis: CompulsoryGlobal Innovation Management: Thesis: CompulsoryComputational Science and Engineering: Thesis: CompulsoryInformation and Communication Systems: Thesis: CompulsoryInternational Management and Engineering: Thesis: CompulsoryJoint European Master in Environmental Studies - Cities and Sustainability: Thesis:CompulsoryLogistics, Infrastructure and Mobility: Thesis: CompulsoryMaterials Science: Thesis: CompulsoryMathematical Modelling in Engineering: Theory, Numerics, Applications: Thesis:CompulsoryMechanical Engineering and Management: Thesis: CompulsoryMechatronics: Thesis: CompulsoryBiomedical Engineering: Thesis: CompulsoryMicroelectronics and Microsystems: Thesis: CompulsoryProduct Development, Materials and Production: Thesis: CompulsoryRenewable Energies: Thesis: CompulsoryNaval Architecture and Ocean Engineering: Thesis: CompulsoryShip and Offshore Technology: Thesis: CompulsoryTeilstudiengang Lehramt Metalltechnik: Thesis: CompulsoryTheoretical Mechanical Engineering: Thesis: CompulsoryProcess Engineering: Thesis: CompulsoryWater and Environmental Engineering: Thesis: CompulsoryCertification in Engineering & Advisory in Aviation: Thesis: Compulsory

[52]


Computational Science and - TUHH€¦ · Module M1552: Mathematics of Neural Networks...

Documents

Transcript of Computational Science and - TUHH€¦ · Module M1552: Mathematics of Neural Networks...