module handbook · 2020. 11. 2. · mastercomputerscience—universitätbonn 4 module ma-inf1103...

Module Handbookfor the

Master Programme “Computer Science”

at

Rheinischen Friedrich-Wilhelms-Universität Bonn

revised version: October 29, 2020

The curriculum of the master programme is divided into four sub-curricula, each corre-sponding to one of the four main areas of competence in research of the Bonn Institute ofComputer Science:

1. Algorithmics2. Graphics, Vision, Audio3. Information and Communication Management4. Intelligent Systems

Module numbers MA-INF ASXY have been assigned according to the following key:vergeben:

• A = number of the area of competence• S = semester within the master curriculum• XY = sequential number within the semester and the respective area of competence(two digits)

According to the curriculum, all modules ought to be taken between the first and the thirdsemester. The fourth semester is reserved for preparing the master thesis.

Contents

1 Algorithmics 2

2 Graphics, Vision, Audio 31

3 Information and Communication Management 69

4 Intelligent Systems 101

5 Master Thesis 143

Master Computer Science — Universität Bonn 2

1 Algorithmics

MA-INF 1102 L4E2 9 CP Combinatorial Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3MA-INF 1103 L4E2 9 CP Cryptography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4MA-INF 1104 L4E2 9 CP Advanced Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5MA-INF 1201 L4E2 9 CP Approximation Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6MA-INF 1202 L4E2 9 CP Chip Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7MA-INF 1203 L4E2 9 CP Discrete and Computational Geometry . . . . . . . . . . . . . . . . . . 8MA-INF 1205 6 CP Graduate Seminar Discrete Optimization . . . . . . . . . . . . . . . . 9MA-INF 1206 Sem2 4 CP Seminar Randomized and Approximation Algorithms . . 10MA-INF 1207 Lab4 9 CP Lab Combinatorial Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . 11MA-INF 1209 Sem2 4 CP Seminar Advanced Topics in Cryptography . . . . . . . . . . . . . 12MA-INF 1213 L4E2 9 CP Randomized Algorithms and Probabilistic Analysis . . . . . 13MA-INF 1217 Sem2 4 CP Seminar Theoretical Foundations of Data Science . . . . . . 14MA-INF 1218 L4E2 9 CP Algorithms and Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15MA-INF 1219 Sem2 4 CP Seminar Algorithmic Game Theory . . . . . . . . . . . . . . . . . . . . 16MA-INF 1220 Sem2 4 CP Seminar Algorithms for Computational Analytics . . . . . . 17MA-INF 1221 Lab4 9 CP Lab Computational Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . 18MA-INF 1222 Lab4 9 CP Lab High Performance Optimization . . . . . . . . . . . . . . . . . . . 19MA-INF 1301 L4E2 9 CP Algorithmic Game Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20MA-INF 1304 Sem2 4 CP Seminar Computational Geometry . . . . . . . . . . . . . . . . . . . . . 21MA-INF 1305 6 CP Graduate Seminar Chip Design . . . . . . . . . . . . . . . . . . . . . . . . 22MA-INF 1307 Sem2 4 CP Seminar Advanced Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . 23MA-INF 1308 Lab4 9 CP Lab Algorithms for Chip Design . . . . . . . . . . . . . . . . . . . . . . . 24MA-INF 1309 Lab4 9 CP Lab Efficient Algorithms: Design, Analysis and

Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25MA-INF 1312 L4E2 9 CP The Art of Cryptography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26MA-INF 1314 L4E2 9 CP Online Motion Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27MA-INF 1315 Lab4 9 CP Lab Computational Geometry . . . . . . . . . . . . . . . . . . . . . . . . . 28MA-INF 1320 Lab4 9 CP Lab Advanced Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29MA-INF 1321 L2E2 6 CP Binary Linear and Quadratic Optimization . . . . . . . . . . . . . 30


ModuleMA-INF 1102

Combinatorial Optimization

Workload270 h

Credit points Duration Frequency9 CP 1 semester at least every year

Modulecoordinator

Prof. Dr. Jens Vygen

Lecturer(s) All lecturers of Discrete Mathematics

ClassificationProgramme Mode SemesterM. Sc. Computer Science Optional 1. or 2.

Technical skills Advanced knowledge of combinatorial optimization. Modellingand development of solution strategies for combinatorialoptimization problems

Soft skills Mathematical modelling of practical problems, abstractthinking, presentation of solutions to exercises

Contents Matchings, b-matchings and T-joins, optimization overmatroids, submodular function minimization, travellingsalesman problem, polyhedral combinatorics, NP-hard problems

Prerequisites none

FormatTeaching format Group size h/week Workload[h] CPLecture 4 60 T / 105 S 5.5Exercises 2 30 T / 75 S 3.5T = face-to-face teaching; S = independent study

Exam achievements Oral exam (graded)Study achievements Successful exercise participation (not graded)Forms of media

Literature

• B. Korte, J. Vygen: Combinatorial Optimization: Theory andAlgorithms. Springer, 6th edition, 2018• A. Schrijver: Combinatorial Optimization: Polyhedra andEfficiency. Springer, 2003• W. Cook, W. Cunningham, W. Pulleyblank, A. Schrijver:Combinatorial Optimization. Wiley, 1997• A. Frank: Connections in Combinatorial Optimization. OxfordUniversity Press, 2011


ModuleMA-INF 1103

Cryptography

Workload270 h

Credit points Duration Frequency9 CP 1 semester every year

Modulecoordinator

Dr. Michael Nüsken

Lecturer(s) Dr. Michael Nüsken


Technical skills Understanding of security concerns and measures, and of theinterplay between computing power and security requirements.Mastery of the basic techniques for cryptosystems andcryptanalysis

Soft skills Oral presentation (in tutorial groups), written presentation (ofexercise solutions), team collaboration in solving homeworkproblems, critical assessment

Contents Basic private-key and public-key cryptosystems: AES, RSA,group-based. Security reductions. Key exchange, cryptographichash functions, signatures, identification; factoring integers anddiscrete logarithms; lower bounds in structured models.

Prerequisites none


Exam achievements Written exam (graded)Study achievements Successful exercise participation (not graded)Forms of media

Literature• Stinson, Cryptography: Theory and Practice, 2nd edition• Course notes


ModuleMA-INF 1104

Advanced Algorithms

Workload270 h


Modulecoordinator

Prof. Dr. Heiko Röglin

Lecturer(s) Prof. Dr. Anne Driemel, Prof. Dr. Thomas Kesselheim,Prof. Dr. Heiko Röglin, PD Dr. Elmar Langetepe,Dr. Herman Haverkort, Senior Prof. Dr. Marek Karpinski

ClassificationProgramme Mode SemesterM. Sc. Computer Science Optional 1.

Technical skills Deeper insights into selected methods and techniques of modernalgorithmics.

Soft skills Presentation of solutions and methods, critical discussion ofapplied methods and techniques.

Contents Advanced algorithmic techniques from e.g. approximation,randomized and exact exponential time algorithms. We will alsorevisit some essential topics such as linear programs and networkflows.

Prerequisites none


Exam achievements Written exam (graded)Study achievements Successful exercise participation (not graded)Forms of mediaLiterature


ModuleMA-INF 1201

Approximation Algorithms

Workload270 h


Modulecoordinator


Lecturer(s) All lecturers of Discrete Mathematics,Senior Prof. Dr. Marek Karpinski


Technical skills Introduction to design and analysis of most importantapproximation algorithms for NP-hard combinatorialoptimization problems, and various techniques for proving lowerand upper bounds, probabilistic methods and applications

Soft skills Presentation of solutions and methods, critical discussion ofapplied methods and techniques

Contents Approximation Algorithms and Approximation Schemes. Designand Analysis of Approximation algorithms for selected NP-hardproblems, like Set-Cover, and Vertex-Cover problems,MAXSAT, TSP, Knapsack, Bin Packing, Network Design,Facility Location. Introduction to various approximationtechniques (like Greedy, LP-Rounding, Primal-Dual, LocalSearch, randomized techniques and Sampling, andMCMC-Methods), and their applications. Analysis ofapproximation hardness and PCP-Systems.

Prerequisites Recommended:Introductory knowledge of foundations of algorithms andcomplexity theory is essential.



Literature

• S. Arora, C. Lund: Hardness of Approximations. In:Approximation Algorithms for NP-Hard Problems (D. S.Hochbaum, ed.), PWS, 1996• M. Karpinski: Randomisierte und approximative Algorithmenfür harte Berechnungsprobleme, Lecture Notes (5th edition),Universität Bonn, 2007• B. Korte, J. Vygen: Combinatorial Optimization: Theory andAlgorithms (6th edition), Springer, 2018• V. V. Vazirani: Approximation Algorithms, Springer, 2001• D. P. Williamson, D. B. Shmoys: The Design ofApproximation Algorithms, Cambridge University Press, 2011


ModuleMA-INF 1202

Chip Design

Workload270 h


Modulecoordinator




Technical skills Knowledge of the central problems and algorithms in chipdesign. Competence to develop and apply algorithms for solvingreal-world problems, also with respect to technical constraints.Techniques to develop and implement efficient algorithms forvery large instances.

Soft skills Mathematical modelling of problems occurring in chip design,development of efficient algorithms, abstract thinking,presentation of solutions to exercises

Contents Problem formulation and design flow for chip design, logicsynthesis, placement, routing, timing analysis and optimization

Prerequisites none



Literature

• C.J. Alpert, D.P. Mehta, S.S. Sapatnekar: The Handbook ofAlgorithms for VLSI Physical Design Automation. CRC Press,New York, 2008.• S. Held, B. Korte, D. Rautenbach, J. Vygen: Combinatorialoptimization in VLSI design. In: "Combinatorial Optimization:Methods and Applications" (V. Chvátal, ed.), IOS Press,Amsterdam 2011, pp. 33-96• S. Held, J. Vygen: Chip Design. Lecture Notes (distributedduring the course)• L. Lavagno, I.L. Markov, G. Martin, and L.K. Scheffer, eds.:Electronic Design Automation for IC Implementation, CircuitDesign, and Process Technology. CRC Press, 2nd edition, 2016


ModuleMA-INF 1203

Discrete and Computational Geometry

Workload270 h


Modulecoordinator

Prof. Dr. Anne Driemel

Lecturer(s) Prof. Dr. Anne Driemel, PD Dr. Elmar Langetepe,Dr. Herman Haverkort

ClassificationProgramme Mode SemesterM. Sc. Computer Science Optional 1-4.

Technical skills Knowledge of fundamental theorems and concepts in the area ofdiscrete and computational geometry; design and analysis ofgeometric algorithms; combinatorial analysis of the complexityof geometric configurations; to apply this knowledgeautonomously in solving new problems.

Soft skills Social competence (communication, presenting one’s ownsolutions, goal-oriented discussions in teams), methodicalcompetence (analysis, abstraction, proofs), individualcompetence (commitment and willingness to learn, creativity,endurance).

Contents Fundamentals of convex sets, Voronoi diagrams, hyperplanearrangements, well-separated pair decomposition, spanners,metric space embedding, dimension reduction, VC-dimension,epsilon-nets, visibility, point location, range searching,randomized incremental construction, geometric distanceproblems in dimension two and higher.

Prerequisites Recommended:BA-INF 114 – Grundlagen der algorithmischen Geometrie



Literature

• Jiri Matousek. Lectures on Discrete Geometry. SpringerGraduate Texts in Mathematics. ISBN 0-387-95374-4.• Mark de Berg, Otfried Cheong, Marc van Kreveld, and MarkOvermars. Computational Geometry — Algorithms andApplications (Third Edition). Springer. ISBN978-3-540-77973-5.• Narasimhan/Smid, Geometric Spanner Networks• Klein, Concrete and Abstract Voronoi Diagrams


ModuleMA-INF 1205

Graduate Seminar Discrete Optimization

Workload180 h


Modulecoordinator




Technical skills Competence to understand new research results based onoriginal literature, to put such results in a broader context andpresent such results and relations.

Soft skills Ability to read and understand research papers, abstractthinking, presentation of mathematical results in a talk

Contents A current research topic in discrete optimization will be choseneach semester and discussed based on original literature.

Prerequisites Recommended:MA-INF 1102 – Combinatorial Optimization

FormatTeaching format Group size h/week Workload[h] CPSeminar 10 4 60 T / 120 S 6T = face-to-face teaching; S = independent study

Exam achievements Oral presentation, written report (graded)Study achievements (not graded)Forms of media

LiteratureThe topics and the relevant literature will be announced towardsthe end of the previous semester.


ModuleMA-INF 1206

Seminar Randomized and ApproximationAlgorithms

Workload120 h


Modulecoordinator


Lecturer(s) Prof. Dr. Anne Driemel, Prof. Dr. Thomas Kesselheim,Prof. Dr. Heiko Röglin, PD Dr. Elmar Langetepe,Dr. Herman Haverkort, Senior Prof. Dr. Marek Karpinski


Technical skills Ability to perform individual literature search, critical reading,understanding, and clear presentation.


Contents Current topics in design and analysis of randomized andapproximation algorithms based on lastest research literature

Prerequisites none


Exam achievements Oral presentation, written report (graded)Study achievements (not graded)Forms of mediaLiterature The relevant literature will be announced in time.


ModuleMA-INF 1207

Lab Combinatorial Algorithms

Workload270 h


Modulecoordinator




Technical skills Competence to implement advanced combinatorial algorithms,handling nontrivial data structures, testing, documentation.Advanced software techniques.

Soft skills Efficient implementation of complex algorithms, abstractthinking, documentation of source code

Contents Certain combinatorial algorithms will be chosen each semester.The precise task will be explained in a meeting in the previoussemester.

Prerequisites Recommended:MA-INF 1102 – Combinatorial Optimization

FormatTeaching format Group size h/week Workload[h] CPLab 8 4 60 T / 210 S 9T = face-to-face teaching; S = independent study


LiteratureThe topics and the relevant literature will be announced towardsthe end of the previous semester


ModuleMA-INF 1209

Seminar Advanced Topics in Cryptography

Workload120 h

Credit points Duration Frequency4 CP 1 semester every semester

Modulecoordinator

Dr. Michael Nüsken



Technical skills Understanding research publications, often written tersely.Distilling this into a presentation. Determination of relevant vs.irrelevant material. Developing a presentation that fascinatesfellow students.

Soft skills Understanding and presenting material both orally and in visualmedia. Motivating other students to participate. Criticalassessment of research results.

Contents A special topic within cryptography, changing from year to year,is studied in depth, based on current research literature

Prerequisites Required:MA-INF 1103 – Cryptographyand one further course in cryptography like The Art ofCryptography or eSecurity.


Exam achievements Oral presentation, written report (graded)Study achievements (not graded)Forms of mediaLiterature Current conference publications, to be announced in time


ModuleMA-INF 1213

Randomized Algorithms and Probabilistic Analysis

Workload270 h


Modulecoordinator


Lecturer(s) Prof. Dr. Heiko Röglin


Technical skills Understanding of models and techniques for the probabilisticanalysis of algorithms as well as for the design and analysis ofrandomized algorithms

Soft skills Oral and written presentation of solutions and methods,abstract thinking

Contents Design and analysis of randomized algorithms• complexity classes• Markov chains and random walks• tail inequalities• probabilistic methodsmoothed and average-case analysis• simplex algorithm• local search algorithms• clustering algorithms• combinatorial optimization problems• multi-objective optimization

Prerequisites none



Literature

• lecture notes• research articles• Motwani, Raghavan, Randomized Algorithms, CambridgeUniversity Press, 1995• Mitzenmacher, Upfal, Probability and Computing, CambridgeUniversity Press, 2nd edition, 2017


ModuleMA-INF 1217

Seminar Theoretical Foundations of Data Science

Workload120 h


Modulecoordinator


Lecturer(s) Prof. Dr. Anne Driemel, Prof. Dr. Thomas Kesselheim,Prof. Dr. Heiko Röglin, PD Dr. Elmar Langetepe,Dr. Herman Haverkort


Technical skills Ability to understand new research results presented in originalscientific papers.

Soft skills Ability to present and to critically discuss these results in theframework of the corresponding area.

Contents Current conference and journal papersPrerequisites none


Exam achievements Oral presentation, written report (graded)Study achievements (not graded)Forms of mediaLiterature


ModuleMA-INF 1218

Algorithms and Uncertainty

Workload270 h

Credit points Duration Frequency9 CP 1 semester at least every 2 years

Modulecoordinator

Prof. Dr. Thomas Kesselheim

Lecturer(s) Prof. Dr. Thomas Kesselheim


Technical skills Understanding approaches for modeling uncertainty inalgorithmic theory. Designing and analyzing algorithms withperformance guarantees in the context of uncertainty.

Soft skills Oral and written presentation of solutions and methodsContents • Advanced Online Algorithms

• Markov Decisions Processes• Stochastic and Robust Optimization• Online Learning Algorithms and Online Convex Optimization• Sample Complexity

Prerequisites none


Exam achievements Oral exam (graded)Study achievements Successful exercise participation (not graded)Forms of mediaLiterature lecture notes, research articles


ModuleMA-INF 1219

Seminar Algorithmic Game Theory

Workload120 h


Modulecoordinator


Lecturer(s) Prof. Dr. Thomas Kesselheim



Soft skills Ability to perform individual literature search, critical reading,and clear didactic presentation

Contents Advanced topics in Algorithmic Game Theory and AlgorithmicMechanism Design based on current conference and journalpapers

Prerequisites none




ModuleMA-INF 1220

Seminar Algorithms for Computational Analytics

Workload120 h


Modulecoordinator

Prof. Dr. Petra Mutzel

Lecturer(s) Prof. Dr. Petra Mutzel


Technical skills Ability to perform individual literature search, critical reading,understanding, and clear didactic presentation.


Contents Current topics in algorithms for computational analytics basedon recent research literature.

Prerequisites Recommended:Interest in Algorithms




ModuleMA-INF 1221

Lab Computational Analytics

Workload270 h


Modulecoordinator


Lecturer(s) Prof. Dr. Petra Mutzel


Technical skills Ability to design, analyze and implement efficient algorithms forcomputational analytics problems. The LAB also includesexperimental evaluation and documentation of the implementedsoftware.

Soft skills Ability to properly present and defend design decisions, toprepare readable documentation of software; skills inconstructively collaborating with others in small teams over alonger period of time; ability to classify ones own results into thestate-of-the-art of the resp. area

Contents Design of efficient exact and approximate algorithms and datastructures for computational analytics problems.

Prerequisites Recommended:Interests in algorithms




ModuleMA-INF 1222

Lab High Performance Optimization

Workload270 h


Modulecoordinator


Lecturer(s) Prof. Dr. Petra Mutzel, Dr. Sven Mallach


Technical skills Ability to design, analyze and implement efficient algorithms forcomputational analytics problems. The lab also includesexperimental evaluation and documentation of the implementedsoftware.


ContentsPrerequisites none




ModuleMA-INF 1301

Algorithmic Game Theory

Workload270 h

Credit points Duration Frequency9 CP 1 semester every 2 years

Modulecoordinator


Lecturer(s) Prof. Dr. Thomas Kesselheim,Senior Prof. Dr. Marek Karpinski


Technical skills The goal is to provide basic techniques and methods related tothe Game Theory for analyzing modern Internet-basedcommunication networks and for designing algorithms for theunderlying problems of transmission control, resource allocation,mechanism design, market equilibria, combinatorial auctions,and the network cost allocation


Contents The most defining characteristic of the Internet is that it wasnot designed by a single central entity, but emerged from thecomplex interactions of many individual entities or economicagents, such as network operators, service providers, designers,users, etc. We aim at providing basic framework and basictechniques for analyzing and designing algorithms for thefollowing Internet-related problems and contexts: game theoreticproblems connected to the Internet and other decentralizednetworks, resource allocation, mechanism design, Nash andmarket equilibria, network economics, combinatorial auctions,cost allocations and network design.We will address new broadly applicable and unifying techniquesthat have emerged recently in the above areas and discuss newfundamental paradigms in design of the relevant algorithms.

Prerequisites Recommended:Introductory knowledge of foundations of algorithms andcomplexity theory is essential.



Literature

• D. P. Bertsekas, A. Nedic, A. E. Ozdaglar: Convex Analysisand Optimization, Athena, 2003• M. Karpinski, W. Rytter: Fast Parallel Algorithms for GraphMatching Problems, Oxford Univ. Press, 1998• D. M. Kreps: A Course in Microeconomic Theory, PrincetonUniv. Press, 1990• N. Nisan, T. Roughgarden, E. Tardos, V.V. Vazirani (ed.):Algorithmic Game Theory, Cambridge Univ. Press, 2007• M. J. Osborne, A. Rubinstein: A Course in Game Theory,MIT Press, 2001


ModuleMA-INF 1304

Seminar Computational Geometry

Workload120 h


Modulecoordinator




Technical skills To independently study problems at research level, based onresearch publications, to prepare a concise summary, to presentthe summary in a scientific talk, to lead a critical discussionwith other seminar participants.

Soft skillsContents Current topics in computational geometry.Prerequisites Recommended:

BA-INF 114 – Grundlagen der algorithmischen GeometrieMA-INF 1203 – Discrete and Computational Geometry


Exam achievements Oral presentation, written report (graded)Study achievements (not graded)Forms of media Multimedia projector, black board.Literature The relevant literature will be announced.


ModuleMA-INF 1305

Graduate Seminar Chip Design

Workload180 h


Modulecoordinator




Technical skills Competence to understand new theoretical results and practicalsolutions in VLSI design and related applications, as well aspresentation of such results

Soft skills Ability to read and understand research papers, abstractthinking, presentation of mathematical results in a talk

Contents Current topics in chip design and related applicationsPrerequisites Recommended:

At least 1 of the following:MA-INF 1102 – Combinatorial OptimizationMA-INF 1202 – Chip Design





ModuleMA-INF 1307

Seminar Advanced Algorithms

Workload120 h


Modulecoordinator




Technical skills Presentation of selected advanced topics in algorithm design andvarious applications

Soft skills Ability to perform individual literature search, critical reading,understanding, and clear didactic presentation

Contents Advanced topics in algorithm design based on newest researchliterature

Prerequisites none




ModuleMA-INF 1308

Lab Algorithms for Chip Design

Workload270 h


Modulecoordinator




Technical skills Competence to implement algorithms for VLSI design, efficienthandling of very large instances, testing, documentation.Advanced software techniques.

Soft skills Efficient implementation of complex algorithms, abstractthinking, modelling of optimization problem in VLSI design,documentation of source code

Contents A currently challenging problem will be chosen each semester.The precise task will be explained in a meeting in the previoussemester.

Prerequisites Recommended:At least 3 of the following:MA-INF 1102 – Combinatorial OptimizationMA-INF 1202 – Chip DesignMA-INF 1205 – Graduate Seminar Discrete Optimization





ModuleMA-INF 1309

Lab Efficient Algorithms: Design, Analysis andImplementation

Workload270 h


Modulecoordinator




Technical skills Ability to design, analyze and implement efficient algorithms forselected computational problems.

Soft skills ability to work on advanced algorithmic implementationprojects, to work in small teams, clear didactic presentation andcritical discussion of results

Contents Design of efficient exact and approximate algorithms and datastructures for selected computational problems.

Prerequisites none




ModuleMA-INF 1312

The Art of Cryptography

Workload270 h


Modulecoordinator

Dr. Michael Nüsken



Technical skills Insights into the theoretical foundations behind securityconcerns and measures, and of the interplay between computingpower, and security requirements. Mastery of advancedtechniques for cryptosystems and cryptanalysis.

Soft skills Oral presentation (in tutorial groups), written presentation (ofexercise solutions), team collaboration in solving homeworkproblems, critical assessment

Contents Possible topics are• pseudorandomness and zero-knowledge,• security reductions,• lattices.

Prerequisites Recommended:MA-INF 1103 – Cryptography


Exam achievements Written exam (graded)Study achievements Successful exercise participation (not graded)Forms of mediaLiterature Varying


ModuleMA-INF 1314

Online Motion Planning

Workload270 h


Modulecoordinator

PD Dr. Elmar Langetepe

Lecturer(s) Prof. Dr. Rolf Klein, PD Dr. Elmar Langetepe


Technical skills To acquire fundamental knowledge on topics and methods inonline motion planning

Soft skillsContents Search and exploration in unknown environments (e.g., graphs,

cellular environmwents, polygons, strets), online algorithms,competitive analysis, competitive complexity,functionaloptimization, shortest watchman route, tethered robots, markeralgorithms, spiral search, approximation of optimal search paths.

Prerequisites Recommended:BA-INF 114 – Grundlagen der algorithmischen Geometrie


Exam achievements Oral exam (graded)Study achievements Successful exercise participation (not graded)Forms of media Java applets of geometry labLiterature Scientific research articles will be recommended in the lecture.


ModuleMA-INF 1315

Lab Computational Geometry

Workload270 h


Modulecoordinator




Technical skills Ability to design, analyze, implement and document efficientalgorithms for selected problems in computational geometry.

Soft skills Ability to properly present, defend and discuss design andimplementation decisions, to document software according togiven rules and to collaborate with other students in smallgroups.

Contents Various problems in computational geometry.Prerequisites none




ModuleMA-INF 1320

Lab Advanced Algorithms

Workload270 h


Modulecoordinator


Lecturer(s) Prof. Dr. Thomas Kesselheim, Prof. Dr. Heiko Röglin


Technical skills Implementation of algorithms from advanced algorithmic theory,evaluating these algorithm on suitably chosen instances, anddiscussing how theoretical results transfer to practice.

Soft skills Ability to properly present, defend and discuss design andimplementation decisions and observed conclusions, and tocollaborate with other students in small groups.

Contents Various problems from current research and courses onalgorithmic theory.

Prerequisites none




ModuleMA-INF 1321

Binary Linear and Quadratic Optimization

Workload180 h


Modulecoordinator

Dr. Sven Mallach

Lecturer(s) Dr. Sven Mallach


Technical skills Deeper understanding of computational methods to solvepotentially large-scale mixed-integer programs in practice.Application-specific modelling and reformulation ofcombinatorial optimization problems, handling quadraticobjective functions, algorithm design.

Soft skills Social, methodological, and analytical competences viacommunication, own development, presentation, and criticalassessment of problem formulations, algorithms, and solutionscovered in the course or the excercises. Learning to abstract, butalso learning the limitations of abstraction.

Contents Computational methods in (mixed-)integer programming such ascutting plane separation and branch-and-bound along with ashort and accessible introduction into their theoretical basis.Study of practically relevant binary linear and binary quadraticoptimization problems, e.g., Maximum Cut, Linear Orderingand variants of the Traveling Salesman problem, along with theparticular separation problems arising there. If there is time,linearizations of quadratic objective functions and moresophisticated formulations of binary quadratic problems arediscussed.

Prerequisites none


Exam achievements Oral exam (default, possibly changed to written exam duringthe term) (graded)

Study achievements Successful exercise participation (not graded)Forms of mediaLiterature


2 Graphics, Vision, Audio

MA-INF 2111 L2E2 6 CP Foundations of Graphics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32MA-INF 2113 L2E2 6 CP Foundations of Audio Signal Processing . . . . . . . . . . . . . . . . 33MA-INF 2201 L4E2 9 CP Computer Vision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34MA-INF 2202 L4E2 9 CP Computer Animation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35MA-INF 2203 L4E2 9 CP Selected Topics in Signal Processing . . . . . . . . . . . . . . . . . . . . 36MA-INF 2204 L2E2 6 CP Rendering Techniques I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37MA-INF 2205 L2E2 6 CP Geometry Processing I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38MA-INF 2206 Sem2 4 CP Seminar Vision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39MA-INF 2207 Sem2 4 CP Seminar Graphics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40MA-INF 2208 Sem2 4 CP Seminar Audio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41MA-INF 2209 L4E2 9 CP Advanced Topics in Computer Graphics I . . . . . . . . . . . . . . 42MA-INF 2210 Sem2 4 CP Seminar Computer Animation . . . . . . . . . . . . . . . . . . . . . . . . . 43MA-INF 2212 L2E2 6 CP Pattern Matching and Machine Learning for Audio Signal

Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44MA-INF 2213 L3E1 6 CP Computer Vision II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45MA-INF 2214 L2E2 6 CP Computational Photography . . . . . . . . . . . . . . . . . . . . . . . . . . . 46MA-INF 2215 Sem2 4 CP Seminar Digital Material Appearance . . . . . . . . . . . . . . . . . . 47MA-INF 2216 Lab4 9 CP Lab Visual Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48MA-INF 2217 L2E2 6 CP Advanced Deep Learning for Graphics . . . . . . . . . . . . . . . . . 49MA-INF 2218 L2E2 6 CP Video Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50MA-INF 2219 Sem2 4 CP Seminar Visualization and Medical Image Analysis . . . . . 51MA-INF 2220 Lab4 9 CP Lab Visualization and Medical Image Analysis . . . . . . . . . 52MA-INF 2221 Sem2 4 CP Seminar Visual Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53MA-INF 2222 L4E2 9 CP Visual Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54MA-INF 2302 L2E2 6 CP Physics-based Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55MA-INF 2304 L2E2 6 CP Rendering Techniques II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56MA-INF 2305 L2E2 6 CP Geometry Processing II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57MA-INF 2306 L2E2 6 CP Virtual Reality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58MA-INF 2307 Lab4 9 CP Lab Vision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59MA-INF 2308 Lab4 9 CP Lab Graphics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60MA-INF 2309 Lab4 9 CP Lab Audio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61MA-INF 2310 L4E2 9 CP Advanced Topics in Computer Graphics II . . . . . . . . . . . . . 62MA-INF 2311 Lab4 9 CP Lab Computer Animation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63MA-INF 2312 L3E1 6 CP Image Acquisition and Analysis in Neuroscience . . . . . . . . 64MA-INF 2313 L2E2 6 CP Deep Learning for Visual Recognition . . . . . . . . . . . . . . . . . . 65MA-INF 2314 L4E2 9 CP Image Processing, Search and Analysis I . . . . . . . . . . . . . . . 66MA-INF 2315 L4E2 9 CP Seminar Computational Photography . . . . . . . . . . . . . . . . . . 67MA-INF 2316 L4E2 9 CP Lab Digital Material Appearance . . . . . . . . . . . . . . . . . . . . . . 68


ModuleMA-INF 2111

Foundations of Graphics

Workload180 h


Modulecoordinator

Prof. Dr. Reinhard Klein

Lecturer(s) Prof. Dr. Reinhard Klein, Prof. Dr. Andreas Weber,Prof. Dr. Matthias Hullin


Technical skills Knowledge of basic mathematical techniques commonly used inGraphics with a strong emphasis on their application to realworld problems.

Soft skills Research abilities, information retrieval abilities, collaborationabilities, self management, creativity.

Contents Affine and projective transformations with applications to imageformation (rigid body motion, cinematic chains); Parametriccurves and surfaces with applications to 3D modelling; Ordinarydifferential equations with applications to physical basedmodelling

Prerequisites Required:MA-INF 2101 - Foundations of Graphics, Vision and Audio hasnot been passed.




ModuleMA-INF 2113

Foundations of Audio Signal Processing

Workload180 h


Modulecoordinator

apl. Prof. Dr. Frank Kurth

Lecturer(s) apl. Prof. Dr. Frank Kurth, Prof. Dr. Michael Clausen


Technical skills • Introduction to basic concepts of analog and digital signalprocessing;• Applications in the field of Audio Signal Processing;• Signal Processing Algorithms;• Implementing basic Signal Processing Algorithms

Soft skills Solving basic Signal Processing Problems; Implementing SignalProcessing Algorithms using state-of-the-art softwareframeworks; Capability to analyze; Time management;Presentation skills; Discussing own solutions and solutions ofothers, and working in groups.

Contents Theoretical introduction to analog and digital Signal Processing;Fourier Transforms; Analog to digital Conversion; DigitalFilters; Audio Signal Processing Applications; Filter banks;Windowed Fourier Transform; 2D-Signal Processing

Prerequisites none


Exam achievements Written exam (graded)Study achievements Successful exercise participation (not graded)Forms of media Slides, Blackboard, WhiteboardLiterature


ModuleMA-INF 2201

Computer Vision

Workload270 h


Modulecoordinator

Prof. Dr. Jürgen Gall

Lecturer(s) Prof. Dr. Jürgen Gall


Technical skills Students will learn about various mathematical methods andtheir applications to computer vision problems.

Soft skills Productive work in small teams, development and realization ofindividual approaches and solutions, critical reflection ofcompeting methods, discussion in groups.

Contents The class will cover a number of mathematical methods andtheir applications in computer vision. For example, linear filters,edges, derivatives, Hough transform, segmentation, graph cuts,mean shift, active contours, level sets, MRFs, expectationmaximization, background subtraction, temporal filtering, activeappearance models, shapes, optical flow, 2d tracking, cameras,2d/3d features, stereo, 3d reconstruction, 3d pose estimation,articulated pose estimation, deformable meshes, RGBD vision.

Prerequisites Recommended:Basic knowledge of linear algebra, analysis, probability theory,C++ programming



Literature

• R. Hartley, A. Zisserman: Multiple View Geometry inComputer Vision• R. Szeliski: Computer Vision: Algorithms and Applications• S. Prince: Computer Vision: Models, Learning, and Inference


ModuleMA-INF 2202

Computer Animation

Workload270 h


Modulecoordinator

Prof. Dr. Andreas Weber

Lecturer(s) Prof. Dr. Andreas Weber


Technical skills Students will learn fundamental paradigms used in computeranimation. They will learn to use mathematical models ofmotions to come up with algorithmic solutions of problems ofthe synthesis of motions of virtual characters.

Soft skills Social competences (work in groups), communicative skills(written and oral presentation)

Contents Fundamentals of computer animation; kinematics;representations of motions; motion capturing; motion editing;motion synthesis; facial animations

Prerequisites Recommended:MA-INF 2111 – Foundations of Graphics



Literature

• Dietmar Jackel, Stephan Neunreither, Friedrich Wagner:Methoden der Computeranimation, Springer 2006• Rick Parent: Computer Animation: Algorithms andTechniques, Morgan Kaufman Publishers 2002• Frederic I. Parke , Keith Waters: Computer Facial Animation.A K Peters, Ltd. 1996


ModuleMA-INF 2203

Selected Topics in Signal Processing

Workload270 h


Modulecoordinator




Technical skills Learning advanced as well as state of the art topics andtechniques in digital signal processing. Study examples from thefield of digital audio signal processing with a focus on musicaudio. Develop skills for analysing audio signals and designingaudio features for selected application scenarios. Mathematicalmodelling of signal processing problems in practical applications.Design and implementation of corresponding algorithms anddata structures solving those problems. Efficiency issues.

Soft skills Capability to analyze. Time management. Strength of purpose.Discussing own solutions and solutions of others.

Contents Advanced techniques for filter design, design and extraction offeatures describing multimedia signals, efficient DSP algorithms,general concepts for content-based analysis of multimediasignals. Selected signal processing applications, for examplecontent-based music analysis, signal compression, denoising,source separation.

Prerequisites none



Literature

• Lecture script and selected research publications• Hayes: Statistical Digital Signal Processing and Modelling,John Wiley, 1996• Proakis, Manolakis: Digital Signal Processing, Prentice Hall,1996• Klapuri, Davy: Signal Processing, Methods for MusicTranscription, Springer, 2006


ModuleMA-INF 2204

Rendering Techniques I

Workload180 h


Modulecoordinator


Lecturer(s) Prof. Dr. Reinhard Klein


Technical skills Analytical formulation of problems related to image synthesisand knowledge of techniques and algorithms for the generationof photorealistic image data. Knowledge of the major algorithmsfor the simulation of light distributions in 3D-scences andvolume data sets. Self-dependent implementation of the basicalgorithms.

Soft skills Analytical problem description, creativity, self-dependentsolution of practical problems in the area of rendering,presentation of solution strategies and implementations,self-dependent literature research, collaboration abilities,self-management

Contents Topics among others will be: models for the description ofoptical material properties and light sources; transport, volumevisualization and rendering equation; algorithms and techniquesfor the solution of the volume visualization and renderingequation; advanced methods for photorealistic image generationin real-time applications like 3D games. In addition, results fromstate of the art research will be presented.

Prerequisites Recommended:Algorithms and data structures, basic knowledge onmultidimensional analysis und linear algebra, basic knowledge instochastics and statistics, numerical analysis and numericallinear algebra, C++



Literature

• L. Szirmay-Kalos: Monte-Carlo Methods in GlobalIllumination, Institute of Computer Graphics, Vienna Universityof Technology, Vienna.URL: citeseer.ist.psu.edu/szirmay-kalos00montecarlo.html,1999/• P. Dutre, K. Bala, P. Bekaert: Advanced Global Illumination,2nd ed., B&T, 2006• M. Pharr, G. Humphreys: Physically Based Rendering,Elsevier, 2004• J. Kautz, J. Lehtinen, P.-P. Sloan: Precomputed RadianceTransfer: Theory and Practice, Siggraph Course Notes, 2005


ModuleMA-INF 2205

Geometry Processing I

Workload180 h


Modulecoordinator




Technical skills Analytical formulation of problems related to geometryprocessing and knowledge of techniques and algorithms tooptimize, process and store geometry data. Especially, learningof techniques to generate highly detailed three-dimensionaldigital models of real objects and to implement currentgeometry processing algorithms.

Soft skills Analytical problem description, creativity, self-dependentsolution of practical problems in the area of mesh processing,presentation of solution strategies and implementations,self-dependent literature research, collaboration abilities,self-management

Contents Topics among other will be: Methods for the generation ofpolygonal meshes (Laser scanning, registration and integrationof single mesh parts, etc.), Point based representations,Reconstruction techniques, Efficient mesh data structures andmesh compression, Optimization: denoising and smoothing,Mesh decimation and refinement, Hierarchical representations:coarse-to-fine und fine-to-coarse, Editing of polygonal meshes. Inaddition results from state of the art research will be presented.




Literature

• R. Scopigno, C. Andujar, M. Goesele, H. Lensch: 3D DataAcquistion, Eurographics Tutorial, 2002• E. Grinspun, M. Desbrun (organizers): Discrete DifferentialGeometry: An Applied Introduction, Siggraph Course Notes,2006• M. Botsch, M. Pauly: Geometric Modeling Based on TriangleMeshes, Siggraph Course Notes, 2006


ModuleMA-INF 2206

Seminar Vision

Workload120 h


Modulecoordinator






Contents Current conference and journal papers.Prerequisites Required:

MA-INF 2201 – Computer Vision




ModuleMA-INF 2207

Seminar Graphics

Workload120 h


Modulecoordinator






Contents Current conference and journal papers.Prerequisites Recommended:

Mathematical background (multidimensional analysis and linearalgebra, basic numerical methods)Basic knowledge in Computer Graphics




ModuleMA-INF 2208

Seminar Audio

Workload120 h


Modulecoordinator


Lecturer(s) apl. Prof. Dr. Frank Kurth, Dr. Michael Clausen




Contents Current conference and journal papers.Prerequisites none




ModuleMA-INF 2209

Advanced Topics in Computer Graphics I

Workload270 h


Modulecoordinator



Classification Programme Mode SemesterM. Sc. Computer Science Optional 2. or 3.

Technical skills Analytical formulation of problems related to geometry processing andrendering. Knowledge of techniques and algorithms to optimize, process,analyze and store geometry and reflectance data as well as knowledge of themajor algorithms for the simulation of light distributions in 3D-scences andvolume data sets. Self-dependent implementation of the basic algorithms.

Soft skills Based on the knowledge and skills acquired students should be able to• read and judge current scientific literature in the area of geometryprocessing and rendering• identify the major literature concerning a given problem in geometryprocessing or rendering and gain an overview of the current state of the art• discuss problems concerning geometry processing or rendering withresearchers from different application fields• present and propose different solutions and work in a team to solve a meshprocessing or rendering problem• and should have acquired key-competences like motivation to deliverresults, flexibility, scientific integrity, ability to adapt to changes and abilityto communicate

Contents Topics among other will be:• methods for the generation of polygonal meshes from point clouds• efficient mesh data structures and mesh compression• mesh optimization techniques: denoising, smoothing, decimation,refinement• mesh editing techniques• optical material properties and light sources• light transport and rendering equation• algorithms and techniques for the solution of the rendering equation• advanced methods for photorealistic image generation.In addition, results from state of the art research will be presented.

Prerequisites Required:Basic knowledge in computer graphics, data structures, multidimensionalanalysis und linear algebra, numerical analysis and numerical linear algebra,C++


Exam achievements Oral presentation, written report (graded)Study achievements Successful exercise participation (not graded)Forms of media

Literature

• M. Botsch, L. Kobbelt, M. Pauly, P. Alliez, B. Levy, Polygon MeshProcessing, A K Peters (7. Oktober 2010)• M. Gross, HP. Pfister, Point-Based Graphics, Morgan Kaufmann (21. Juni2007)• R. Scopigno, C. Andujar, M. Goesele, H. Lensch: 3D Data Acquistion,Eurographics Tutorial, 2002• E. Grinspun, M. Desbrun (organizers): Discrete Differential Geometry: AnApplied Introduction, Siggraph Course Notes, 2006• L. Szirmay-Kalos: Monte-Carlo Methods in Global Illumination, Instituteof Computer Graphics, Vienna University of Technology, Vienna. URL:citeseer.ist.psu.edu/szirmay-kalos00montecarlo.html, 1999/• P. Dutre, K. Bala, P. Bekaert: Advanced Global Illumination, 2nd ed.,B&T, 2006• M. Pharr, G. Humphreys: Physically Based Rendering, Elsevier, 2ndrevised edition. (26. August 2010)


ModuleMA-INF 2210

Seminar Computer Animation

Workload120 h


Modulecoordinator






Contents Current conference and journal papers.Prerequisites Recommended:

At least 1 of the following:MA-INF 2202 – Computer AnimationMA-INF 2311 – Lab Computer Animation




ModuleMA-INF 2212

Pattern Matching and Machine Learning for AudioSignal Processing

Workload180 h


Modulecoordinator




Technical skills • Introduction into selected topics of digital signal processing;• Applications in the field of Audio Signal Processing;• Methods of Automatic Pattern Recognition

Soft skills Audio Signal Processing Applications; Extended programmingskills for signal processing applications; Capability to analyze;Time management; Presentation skills; Discussing own solutionsand solutions of others, and working in groups.

Contents The lecture is presented in modular form, where each module ismotivated from the application side. The presented topics are:Windowed Fourier transforms; Audio Identifications; AudioMatching; Signal Classification; Hidden Markov Models;Support Vector Machines

Prerequisites none


Exam achievements Written exam (graded)Study achievements Successful exercise participation (not graded)Forms of media Slides, Blackboard, WhiteboardLiterature


ModuleMA-INF 2213

Computer Vision II

Workload180 h


Modulecoordinator




Technical skills Students will learn about various learning methods and theirapplications to computer vision problems.


Contents The class will cover a number of learning methods and theirapplications in computer vision. For example, linear methods forclassification and regression, boosting, random forests, neuralnetworks, SVMs, prototype methods, nearest neighbors,Gaussian processes, metric learning, structured learning, imageclassification, object detection, action recognition, poseestimation, face analysis, tracking.

Prerequisites Required:MA-INF 2201 – Computer Vision

FormatTeaching format Group size h/week Workload[h] CPLecture 3 45 T / 45 S 3Exercises 1 15 T / 75 S 3T = face-to-face teaching; S = independent study

Exam achievements Oral exam (graded)Study achievements Successful exercise participation (not graded)Forms of mediaLiterature


ModuleMA-INF 2214

Computational Photography

Workload180 h


Modulecoordinator

Prof. Dr. Matthias Hullin

Lecturer(s) Prof. Dr. Matthias Hullin


Technical skills Foundations in optics and image sensors. Signal processing andinverse problems in imaging. Color spaces and perception.Image alignment and blending. High-dimensionalrepresentations of light transport (light fields, reflectance fields,reflectance distributions). Computational illumination.

Soft skills • to read and understand current literature in the field• to implement standard computational photography techniques• to propose and implement solutions to a given problem• to follow good scientific practice by planning, documentingand communicating their work

Contents • Image sensors• Optics• Panoramas• Light fields• Signal processing and inverse problems• Color, perception and HDR• Reflectance fields and light transport matrices

Prerequisites Required:Basic knowledge in computer graphics, data structures,multidimensional analysis und linear algebra, numerical analysisand numerical linear algebra, C++ or MATLAB




ModuleMA-INF 2215

Seminar Digital Material Appearance

Workload120 h


Modulecoordinator










ModuleMA-INF 2216

Lab Visual Computing

Workload270 h


Modulecoordinator

Jun-Prof. Dr. Angela Yao

Lecturer(s) Jun-Prof. Dr. Angela Yao


Technical skills The students will carry out a practical task (project) in thecontext of computer vision, including test and documentation ofthe implemented software/system.


Contents This lab introduces computer vision methods and applications.You will get a chance to study the methods in depth byimplementing them and running experiments. At the end of thesemester, you will present the method, give a shortdemonstration and hand in a report describing the method andexperimental outcomes.

Prerequisites none




ModuleMA-INF 2217

Advanced Deep Learning for Graphics

Workload180 h


Modulecoordinator


Lecturer(s) Dr. Michael Weinmann


Technical skills Students will be introduced to adapt and apply deep learningtechniques to various applications in computer graphics.


Contents This course focuses on cutting-edge Deep Learning techniquesfor computer graphics. After a brief review of CNNs the focuswill be laid on autoencoders, generative models and theextension of these methods to graph- and manifold-structureddata. Applications discussed will include inverse problems incomputer graphics and the synthesis of models including datacompletion and super-resolution.

Prerequisites Recommended:The course will build upon the basics of machine learning as wellas fundamentals and basic architectures of neural networks.Therefore, it is highly recommended to have taken DeepLearning for Visual Recognition or a similar course as aprerequisite. Exercises will be a mix of theory and practical(Python).


Exam achievements Written exam (graded)Study achievements Successful exercise participation (not graded)Forms of mediaLiterature No required text, supplemental readings will be given in class.


ModuleMA-INF 2218

Video Analytics

Workload180 h


Modulecoordinator




Technical skills Students will learn advanced techniques for analyzing video data.Soft skills Productive work in small teams, development and realization of

a state-of-the-art system for video analysis.Contents The class will discuss state-of-the-art methods for several tasks

of video analysis. For example, video clip classification, temporalvideo segmentation, spatio-temporal action detection, videocontext, spatio-temporal modeling of humans and objects,anticipation, affordance, video summarization, semantic videosegmentation.

Prerequisites Required:MA-INF 2201 – Computer Vision




ModuleMA-INF 2219

Seminar Visualization and Medical Image Analysis

Workload120 h


Modulecoordinator

Prof. Dr. Thomas Schultz

Lecturer(s) Prof. Dr. Thomas Schultz



Soft skills Ability to present and to critically discuss scientific results in thecontext of the current state of the art. Ability to perform anindependent search for relevant scientific literature.

Contents Current conference and journal papersPrerequisites Recommended:

At least one of the following:• MA-INF 2222 – Visual Data Analysis• MA-INF 2312 – Image Acquisition and Analysis inNeuroscience




ModuleMA-INF 2220

Lab Visualization and Medical Image Analysis

Workload270 h


Modulecoordinator




Technical skills The students will carry out a practical task (project) in thecontext of data visualization and visual analytics or medicalimage analysis, including test and documentation of theimplemented software/system.


ContentsPrerequisites Recommended:

At least one of the following:• MA-INF 2222 – Visual Data Analysis• MA-INF 2312 – Image Acquisition and Analysis inNeuroscience




ModuleMA-INF 2221

Seminar Visual Computing

Workload120 h


Modulecoordinator

Jun-Prof. Dr. Angela Yao

Lecturer(s) Jun-Prof. Dr. Angela Yao




Contents Current conference and journal papersPrerequisites Required:

At least 1 of the following:MA-INF 2201 – Computer VisionMA-INF 2217 – Advanced Deep Learning for GraphicsMA-INF 2313 – Deep Learning for Visual RecognitionMA-INF 4315 – Probabilistic Graphical Models




ModuleMA-INF 2222

Visual Data Analysis

Workload270 h


Modulecoordinator


Lecturer(s) Prof. Dr. Thomas Schultz, Prof. Dr. Reinhard Klein


Technical skills Ability to design, implement, and make proper use of systemsfor visual data analysis. Knowledge of algorithms andtechniques for the visualization of multi-dimensional data,graphs, as well as scalar, vector, and tensor fields.

Soft skills Productive work in small teams, self-dependent solution ofpractical problems in the area of visual data analysis, criticalreflection on visualization design, presentation of solutionstrategies and implementations, self management

Contents This class provides a broad overview of principles andalgorithms for data analysis via interactive visualization.Specific topics include perceptual principles, luminance andcolor, visualization analysis and design, integration of visualwith statistical data analysis and machine learning, as well asspecific algorithms and techniques for the display ofmultidimensional data, dimensionality reduction, graphs, directand indirect volume visualization, vector field and flowvisualization, as well as tensor field visualization.

Prerequisites Recommended:Students are recommended to have a basic knowledge in linearalgebra and calculus, as well as proficiency in programming.



Literature

A.C. Telea, Data Visualization: Principles and Practice. CRCPress, Second Edition, 2015M. Ward et al., Interactive Data Visualization: Foundations,Techniques, and Applications. CRC Press, 2010T. Munzner, Visualization Analysis and Design, A K Peters,2015


ModuleMA-INF 2302

Physics-based Modelling

Workload180 h


Modulecoordinator




Technical skills Students learn the fundamental techniques of physics-basedmodelling for computer graphics and computer animation. Thestudents shall be able to choose appropriate mathematicalmodels. Knowing the algorithmic techniques and algorithmicissues, they shall be able to come up with software solutions forspecific problems.

Soft skills Social competences (work in groups), communicative skills(written and oral presentation)

Contents Initial value problems; particle simulation; rigid body simulation;multi-body-systems; collision detection; collisions response; clothmodelling; hair modelling; physics-based motion synthesis

Prerequisites Recommended:MA-INF 2111 – Foundations of Graphics



Literature

• Dietmar Jackel, Stephan Neunreither, Friedrich Wagner:Methoden der Computeranimation, Springer 2006• David M. Bourg: Physics for Game Developers, O’Reilly• Advanced course notes on physics-based modelling


ModuleMA-INF 2304

Rendering Techniques II

Workload180 h


Modulecoordinator




Technical skills Analytical formulation of problems related to image basedrendering and knowledge of advanced techniques in the field ofrendering. Knowledge of methods and models for the acquisitionand description of light sources and optical material propertiesfor Computer Graphics applications. Knowledge of methods andmodels for the acquisition and description of image basedrendering techniques and digital photography. Self-dependentimplementation of the basic algorithms.

Soft skills Analytical problem description, creativity, self-dependentsolution of practical problems in the area of image basedrendering and digital photography, presentation of solutionstrategies and implementations, self-dependent literatureresearch, collaboration abilities, self-management

Contents Topics among others will be: advanced material acquisition andmodelling techniques; algorithms and techniques of image basedrendering; digital photography for image based scene modellingand rendering; computational photography




Literature

• H.P.A. Lensch, M. Goesele (organizers): Realistic Materials inComputer Graphics, Siggraph Course Notes, 2005• P. Debevec, E. Reinhard (organizers): High-Dynamic-RangeImaging: Theory and Applications, Siggraph Course Notes, 2006• N. Hoffman (organizer): Physically Based Reflectance forGames, Siggraph Course Notes, 2006• R. Raskar, J. Tumblin (organizers): ComputationalPhotography, Siggraph Course Notes, 2006


ModuleMA-INF 2305

Geometry Processing II

Workload180 h


Modulecoordinator




Technical skills Analytical formulation of problems related to geometryprocessing, shape analysis and shape retrieval as well asknowledge of advanced algorithms and techniques from thesefields. Self-dependent implementation of the algorithms.

Soft skills Analytical problem description, creativity, self-dependentsolution of practical problems in the area of image basedrendering and digital photography, presentation of solutionstrategies and implementations, self-dependent literatureresearch, collaboration abilities, self-management

Contents This class is focussed on advanced topics in the field of geometryprocessing. Students will get familiar with recent developmentsin the area of shape analysis and shape retrieval. Topics amongothers will be• Parameterization of surfaces• Shape segmentation and shape similarity• Shape classification and content based retrieval• Shape spaces and statistical shape analysis




Literature

• T. Funkhouser, M. Kazhdan, Shape-Based Retrieval andAnalysis of 3D-Models, Siggraph Course Notes, 2004• L. Dryden, K.V. Mardia, Statistical Shape Analysis, JohnWiley & Sons, 1998• H. Krim, Jr, A. Yezzi (editors): Statistics and Analysis ofShapes (Modeling an Simulation in Science, Engineering andTechnology), Birkhäuser Boston, 2006


ModuleMA-INF 2306

Virtual Reality

Workload180 h


Modulecoordinator




Technical skills Basic knowledge of hard- and software components of currentVR-Systems, Broad knowledge of tracking-, collision detection-and real-time rendering algorithms, knowledge of methods tointegrate haptic and sound, knowledge of GPU programmingwith emphasis on special effect generation, ability to implementcomponents of a VR-System

Soft skills Analytical problem description, creativity, self-dependentsolution of practical problems in the area of Virtual Reality,presentation of solution strategies and implementations,self-dependent literature research, collaboration abilities,self-management

Contents Scene Graphs, Stereo Seeing (HW, SW), Tracking (HW, SW),Acceleration Techniques (LOD; Culling), Collision detection,Haptics, Sound, Special effects (GPU-Programming)

Prerequisites Recommended:Mathematical background (multidimensional analysis and linearalgebra, foundations of numerical methods), good knowledge ofthe foundations of computer graphics



Literature

• K. Stanney (ed.): Handbook of Virtual Environments.Lawrence Erlbaum Associates, 2002• W. Sherman, A. Craig: Understanding Virtual Reality.Morgan Kaufman, 2002• D. Pape: Commodity-Based Projection VR, Siggraph CourseNotes, 2006• N. Tatarchuk (organizer): Advanced Real-Time Rendering inğD Graphics and Games, Siggraph Course Notes, 2006


ModuleMA-INF 2307

Lab Vision

Workload270 h


Modulecoordinator




Technical skills The students will carry out a practical task (project) in thecontext of RGB-D cameras.


Contents RGBD cameras: research topics and applicationsPrerequisites Required:

MA-INF 2201 – Computer VisionGood C++ programming skills



LiteratureA. Fossati, J. Gall, H. Grabner, X. Ren, K. Konolige. ConsumerDepth Cameras for Computer Vision: Research Topics andApplications


ModuleMA-INF 2308

Lab Graphics

Workload270 h


Modulecoordinator




Technical skills The students will carry out a practical task (project) in thecontext of geometry processing, rendering, scientific visualizationor human computer interaction, including test anddocumentation of the implemented software/system.


Contents Varying selected topics close to current research in the area ofgeometry processing, rendering, scientific visualization or humancomputer interaction.

Prerequisites none




ModuleMA-INF 2309

Lab Audio

Workload270 h


Modulecoordinator




Technical skills The students will carry out a practical task (project) in thecontext of audio and music processing, including test anddocumentation of the implemented software/system.

Soft skills Ability to properly present and defend design decisions, toprepare readable documentation of software; skills inconstructively collaborating with others in small teams over alonger period of time; ability to classify ones own results into thestate-of-the-art of the resp. area.





ModuleMA-INF 2310

Advanced Topics in Computer Graphics II

Workload270 h


Modulecoordinator



Classification Programme Mode SemesterM. Sc. Computer Science Optional 3.

Technical skills • apply methods of geometry and digital appearance processing to realworld problems and design and implement novel application softwarein these areas• apply methods of shape segmentation and shape similarity to novelproblems• design novel shape retrieval applications• apply basic concepts of statistical shape analysis and shape spaces toreal world applications• apply geometric and radiometric calibration algorithms to camerabased acquisition systems• select and apply light source and optical material models forcomputer graphics applications• incorporate basic image based algorithms into rendering applications

Soft skills Analytical problem description, creativity, self-dependent solution ofpractical problems, presentation of solution strategies andimplementations, self-dependent literature research, collaborationabilities, self-management.

Contents This class is focused on advanced topics in the field of geometry anddigital appearance processing. Students will get familiar with recentdevelopments in the area of shape analysis, shape retrieval, materialacquisition and modeling techniques. Topics among others will be• Parameterization of surfaces• Shape segmentation and shape similarity• Shape classification and content based retrieval• Shape spaces and statistical shape analysis• Optical material acquisition and modelling techniques• Algorithms and techniques of image based rendering• Digital photography for image based scene modelling and rendering• Basic computational photography

Prerequisites none




ModuleMA-INF 2311

Lab Computer Animation

Workload270 h


Modulecoordinator




Technical skills The students will carry out a practical task (project) in thecontext of computer animation, including test anddocumentation of the implemented software/system.


Contents Varying selected topics close to current research in the area ofcomputer animation.

Prerequisites Recommended:At least 1 of the following:MA-INF 2202 – Computer AnimationMA-INF 2302 – Physics-based Modelling




ModuleMA-INF 2312

Image Acquisition and Analysis in Neuroscience

Workload180 h


Modulecoordinator




Technical skills Students will learn about image acquisition and analysispipelines which are used in neuroscience. They will understandalgorithms for image reconstruction, artifact removal, imageregistration and segmentation, as well as relevant statistical andmachine learning techniques. A particular focus will be on datafrom Magnetic Resonance Imaging and on mathematical modelsfor functional and diffusion MRI data.

Soft skills Productive work in small teams, self-dependent solution ofpractical problems in the area of biomedical image processing,presentation of solution strategies and implementations, selfmanagement, critical reflection of conclusions drawn fromcomplex experimental data.

Contents This course covers the full image formation and analysis pipelinethat is typically used in biomedical studies, from imageacquisition to image processing and statistical analysis.

Prerequisites Recommended:Mathematical background (calculus, linear algebra, statistics);imperative programming.

FormatTeaching format Group size h/week Workload[h] CPLecture 3 45 T / 45 S 3Exercises 1 15 T / 75 S 3T = face-to-face teaching; S = independent study


Literature

• B. Preim, C. Botha: Visual Computing for Medicine: Theory,Algorithms, and Applications. Morgan Kaufmann, 2014• R.A. Poldrack, J.A. Mumford, T.E. Nichols: Handbook ofFunctional MRI Data Analysis. Cambridge University Press,2011• D.K. Jones: Diffusion MRI: Theory, Method, andApplications, Oxford University Press, 2011


ModuleMA-INF 2313

Deep Learning for Visual Recognition

Workload180 h


Modulecoordinator


Lecturer(s) Dr. Michael Weinmann


Technical skills Students will be introduced to the theory of neural networks andstudy various applications in computer vision and other topics inAI.


Contents Deep learning has taken over the machine learning communityby storm, with success both in research and commercially. Deeplearning is applicable over a range of fields such as computervision, speech recognition, natural language processing, robotics,etc. This course will introduce the fundamentals of neuralnetworks and then progress to state-of-the-art convolutional andrecurrent neural networks as well as their use in applications forvisual recognition. Students will get a chance to learn how toimplement and train their own network for visual recognitiontasks such as object recognition, image segmentation andcaption generation.

Prerequisites Recommended:Students are recommended to have a basic knowledge inprobability and statistics and linear algebra as well asproficiency in programming (python or Matlab or C++).



LiteratureNo required text. Supplemental readings will be provided in thelecture.


ModuleMA-INF 2314

Image Processing, Search and Analysis I

Workload270 h


Modulecoordinator

Prof. Dr. Christian Bauckhage

Lecturer(s) Prof. Dr. Christian Bauckhage


Technical skills Upon completion, students should be able to• implement basic and advanced methods for digital imageprocessing• implement simple and advanced algorithms for image filtering• implement algorithms for creating artistic image effects• implement algorithms for image warping• implement algorithms for image morphing• implement algorithms for color and intensity manipulation• design and implement their own algorithms for imageprocessing

Soft skills Students will learn about the mathematical and algorithmicfoundations of digital image processing and raster graphicsediting. They will learn about the basic concepts and proceduresin this area and to implement them on their own.

Contents • technical foundations / hardware aspects of digitalphotography• mathematical representations of digital images• coordinate systems and coordinate transformations• Fourier transforms and convolutions• low- band-, and high pass filtering• mean- and Gaussian filtering• median filtering and morphological operations• efficient implementations of various kinds of filters• interpolation methods• artistic image effects• image warping• image morphing• physiological foundations of color perception• color spaces• color manipulation

Prerequisites none


Exam achievements Written exam (graded)Study achievements Successful exercise participation (not graded)Forms of media • lecture slides are made available online

• lecture notes with programming examples are made availableonline

Literature• Gonzales and Woods, "Digital Image Processing"• Jähne, "Digital Image Processing"


ModuleMA-INF 2315

Seminar Computational Photography

Workload270 h


Modulecoordinator




Technical skillsSoft skillsContentsPrerequisites none


Exam achievements Schriftliche Prüfung (graded)Study achievements Erfolgreiche Übungsteilnahme (not graded)Forms of mediaLiterature


ModuleMA-INF 2316

Lab Digital Material Appearance

Workload270 h


Modulecoordinator




Technical skillsSoft skillsContentsPrerequisites none


Exam achievements Schriftliche Prüfung (graded)Study achievements Erfolgreiche Übungsteilnahme (not graded)Forms of mediaLiterature


3 Information and Communication Management

MA-INF 3106 L2E2 6 CP Privacy in Ubiquitous Computing . . . . . . . . . . . . . . . . . . . . . . 70MA-INF 3201 L2E2 6 CP Network Security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71MA-INF 3202 L2E2 6 CP Mobile Communication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72MA-INF 3207 L2E2 6 CP Advanced Logic Programming . . . . . . . . . . . . . . . . . . . . . . . . . 73MA-INF 3209 Sem2 4 CP Seminar Selected Topics in Communication

Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74MA-INF 3215 Sem2 4 CP Seminar Selected Topics in Malware Analysis and

Computer/Network Security . . . . . . . . . . . . . . . . . . . . . . . . . . . 75MA-INF 3216 Sem2 4 CP Seminar Sensor Data Fusion . . . . . . . . . . . . . . . . . . . . . . . . . . . 76MA-INF 3218 Sem2 4 CP Seminar Model-Driven Software Engineering . . . . . . . . . . . 77MA-INF 3219 Lab4 9 CP Lab Model-Driven Software Engineering . . . . . . . . . . . . . . . 78MA-INF 3222 L4E2 9 CP eSecurity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79MA-INF 3227 Sem2 4 CP Seminar Anonymity and Privacy on the Internet . . . . . . . 80MA-INF 3229 Lab4 9 CP Lab IT-Security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81MA-INF 3233 L2E2 6 CP Advanced Sensor Data Fusion in Distributed Systems . . 82MA-INF 3234 Lab4 9 CP Lab Mobile Sensing Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . 83MA-INF 3235 L2E2 6 CP Usable Security and Privacy . . . . . . . . . . . . . . . . . . . . . . . . . . . 84MA-INF 3236 L2E2 6 CP IT Security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85MA-INF 3237 L2E2 6 CP Array Signal and Multi-channel Processing . . . . . . . . . . . . . 86MA-INF 3304 Lab4 9 CP Lab Communication and Communicating Devices . . . . . . 87MA-INF 3305 Lab4 9 CP Lab Information Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88MA-INF 3309 Lab4 9 CP Lab Malware Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89MA-INF 3310 L2E2 6 CP Introduction to Sensor Data Fusion - Methods and

Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90MA-INF 3311 L4E2 9 CP Topics in Applied Cryptography . . . . . . . . . . . . . . . . . . . . . . . 91MA-INF 3312 Lab4 9 CP Lab Sensor Data Fusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92MA-INF 3317 Sem2 4 CP Seminar Selected Topics in IT Security . . . . . . . . . . . . . . . . . 93MA-INF 3318 Sem2 4 CP Seminar Verification of Complex Systems . . . . . . . . . . . . . . 94MA-INF 3319 Lab4 9 CP Lab Usable Security and Privacy . . . . . . . . . . . . . . . . . . . . . . . 95MA-INF 3320 Lab4 9 CP Lab Security in Distributed Systems . . . . . . . . . . . . . . . . . . . 96MA-INF 3321 Sem2 4 CP Seminar Usable Security and Privacy . . . . . . . . . . . . . . . . . . . 97MA-INF 3322 L2E2 6 CP Program Analysis and Binary Exploitation . . . . . . . . . . . . . 98MA-INF 3323 Lab4 9 CP Lab Fuzzing Bootcamp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99MA-INF 3324 Lab4 9 CP Lab Design of Usable Security Mechanisms . . . . . . . . . . . . 100


ModuleMA-INF 3106

Privacy in Ubiquitous Computing

Workload180 h


Modulecoordinator

Jun.-Prof. Dr. Delphine Christin

Lecturer(s) Jun.-Prof. Dr. Delphine Christin


Technical skills Students gain knowledge about key concepts of privacy(including legal and economical aspects) and field of ubiquitouscomputing. They are able to identify threats to privacy in givenapplication scenarios. They learn fundamental techniques toprotect users’ privacy. Relying on this background, they are ableto understand and analyze cutting-edge solutions.

Soft skills Written and oral communicative skills, critical thinking andproblem solving skills, teamwork, and time management

Contents Introduction to privacy and ubiquitous computing, privacythreats, privacy-enhancing systems in selected scenarios, usableprivacy

Prerequisites Recommended:MA-INF 3202 – Mobile Communication



Literature

John Krumm, Ubiquitous Computing Fundamentals, Crc Pr Inc,2009Alessandro Acquisti, Stefans Gritzalis, Costos Lambrinoudakis,Digital Privacy: Theory, Technologies, and Practices, AuerbachPubn, 2007Mireille Hildebrandt, Kieron O’Hara, Michael Waidner, RobertMadelin, Digital Enlightenment Yearbook 2013: The Value ofPersonal Data, Ios Press, 2013Jan Camenisch, Simone Fischer-Hübner, Kai Rannenberg,Privacy and Identity Management for Life, Springer, 2011Additional research literature will be announced during thelecture


ModuleMA-INF 3201

Network Security

Workload180 h


Modulecoordinator

Prof. Dr. Peter Martini

Lecturer(s) Prof. Dr. Peter Martini, Dr. Dr. habil. Robert Koch


Technical skills The students learn fundamental concepts of network security.This includes risks and vulnerabilities of today’s computernetworks, concepts to increase and testăthe level of security intheseănetworks, a real-life oriented introduction to encryptiontechniques, their applications and their weaknesses and adiscussion of upcoming new technologies.

Soft skills Theoretical exercises to support in-depth understanding oflecture topics and to stimulate discussions, practical exercises inteamwork to support time management, targeted organisation ofpractical work and critical discussion of own and others’ results.

Contents Threats and attack scenarios, cyber kill chain, organizationalaspects, technical aspects: securing networks using differentconcepts like firewalls and IDS (intrusion detection systems),security protocols for different protocol layers, penetrationtesting, high security networks, security aspects of IPv6, privacyprotection, encryption.

Prerequisites Recommended:Bachelor level knowledge of basics of communication systems(e.g. BA-INF 101 "Kommunikation in Verteilten Systemen"(German Bachelor Programme Informatik, English lecture slidesavailable)



Literature

• William Stallings, Lawrie Brown, Computer Security:Principles and Practice (3rd Edition), Pearson• Christoph Busch, Stephen D. Wolthusen: Netzwerksicherheit,Spektrum Akademischer Verlag• Matt Bishop: Introduction to Computer Security, AddisonWesley


ModuleMA-INF 3202

Mobile Communication

Workload180 h


Modulecoordinator


Lecturer(s) Prof. Dr. Peter Martini, Dr. Matthias Frank


Technical skills Knowledge about key concepts of mobile communicationincluding mobility management (both technology independentand technology dependent), knowledge about wirelesstechnologies and their interaction with other protocol layersand/or other network technologies, ability to evaluate and assessscenarios with communication of mobile devices. In-depthunderstanding of communication paradigms of wireless/mobilesystems and network elements, productive work in small groups,strengthening skills on presentation and discussion of solutionsto current challenges

Soft skills Theoretical exercises to support in-depth understanding oflecture topics and to stimulate discussions, practical exercises inteamwork to support time management, targeted organisation ofpractical work and critical discussion of own and others’ results

Contents Mobility Management in the Internet, Wireless CommunicationBasics, Wireless Networking Technologies, Cellular/MobileCommunication Networks (voice and data communication),Ad-hoc and Sensor Networks.

Prerequisites Recommended:Bachelor level knowledge of basics of communication systems(e.g. BA-INF 101 "Kommunikation in Verteilten Systemen"(German Bachelor Programme Informatik, English lecture slidesavailable) and/or MA-INF 3105 – Principles of DistributedSystems



Literature

• Jochen Schiller: Mobile Communications, Addison-Wesley,2003• William Stallings: Wireless Communications and Networking,Prentice Hall, 2002• Further up-to-date literature will be announced in due coursebefore the beginning of the lecture


ModuleMA-INF 3207

Advanced Logic Programming

Workload180 h


Modulecoordinator

Dr. Günter Kniesel

Lecturer(s) Dr. Günter Kniesel


Technical skills Ability to master advanced logic programing techniques and towrite clean but highly efficient Prolog programs using thesetechniques; competence in problem solving using the declarativeparadigm; competence in using the non-logical features ofProlog;

Soft skills Skills in written and oral presentation of the solutions toprogramming assignments, collaboration with other students insmall teams

Contents Quick refresh of logic programming basics and a Prologdevelopment environment, searching, understandingbacktracking and the cut, context arguments, difference lists,data structures, constraint programming, meta-programming,meta-interpreters, partial evaluation, partial evaluation ofmeta-interpreters, efficient Prolog programming, logic programanalysis.

Prerequisites Recommended:Good knowledge of the foundations of Logic Programming



Literature

• W. Clocksin, C. Mellish: Programming in Prolog, Springer.• L. Sterling, E. Shapiro (ed.): The Art of Prolog (2nd ed.) MITPress.• Richard O’Keefe: The Craft of Prolog, MIT Press.


ModuleMA-INF 3209

Seminar Selected Topics in CommunicationManagement

Workload120 h


Modulecoordinator


Lecturer(s) Prof. Dr. Peter Martini, Prof. Dr. Michael Meier




Contents Current conference and journal papers, current standardizationdrafts

Prerequisites Required:Successful completion of at least one of the following lectures:Principles of Distributed Systems (MA-INF3105), NetworkSecurity (MA-INF3201), Mobile Communication (MA-INF3202),IT Security (MA-INF3236)



LiteratureThe relevant literature will be announced towards the end of theprevious semester


ModuleMA-INF 3215

Seminar Selected Topics in Malware Analysis andComputer/Network Security

Workload120 h


Modulecoordinator






Contents Current conference and journal papers, current standardizationdrafts - with a specific topic focus on Malware Analysis,Computer and Network Security





ModuleMA-INF 3216

Seminar Sensor Data Fusion

Workload120 h


Modulecoordinator

P.D. Dr. Wolfgang Koch

Lecturer(s) P.D. Dr. Wolfgang Koch







LiteratureThe relevant literature will be announced at the beginning of theseminar.


ModuleMA-INF 3218

Seminar Model-Driven Software Engineering

Workload120 h


Modulecoordinator

Dr. Günter Kniesel



Technical skills • Understand the differences between model driven and traditionalsoftware development• Describe the common features and peculiarities of different modeldriven development approaches• Assess the suitability of a model driven approach for a given project• Select appropriate tools for model driven development tasks• Explain the individual scientific topic prepared

Soft skills • Refinement of scientific writing and presentation skills• Mine for profound knowledge about a given subject• Distill and communicate the summary of a computer science topicorally• Evaluate the scientific integrity of a written summary• Use modern presentation software

Contents Model driven software development concepts, tools and methods. Inparticular:• Models, meta-models and meta-meta-models (General, MOF,EMOF, ECORE)• Text to model, model to model, model to text transformation• Imperative versus declarative model transformation• Model-driven versus other software development approaches• Best practice and research issues in model based development

Prerequisites Recommended:MA-INF 3207 – Advanced Logic Programming

Format Teaching format Group size h/week Workload[h] CPSeminar 10 2 30 T / 90 S 4T = face-to-face teaching; S = independent study

Exam achievements Oral presentation, written report (graded)Study achievements (not graded)Forms of media • Web page: https://sewiki.iai.uni-bonn.de/teaching/seminars/start

• Slides (Powerpoint/PDF)• Mailing list for students

Literature

• "Model-Driven Software Development: Technology, Engineering,Management". Thomas Stahl, Markus Voelter, Wiley 2006.• "Model-Driven Software Development". Sami Beydeda , MatthiasBook, Volker Gruhn (Eds), ISBN 978-3-540-25613-7, Springer 2005• David S. Frankel: Model Driven Architecture: Applying MDA toEnterprise Computing, John Wiley


ModuleMA-INF 3219

Lab Model-Driven Software Engineering

Workload270 h


Modulecoordinator

Dr. Günter Kniesel



Technical skills On successful completion of this module, students should be able to:• Describe the process of model driven software development (MDSD)and support this description with personal experiences• Connect model driven software development guidelines to concretepractical examples• Be able to use one or several concrete MDSD tools and techniquesand explain their use to others

Soft skills Students should be able to:• Run a software project based on MDSD tools, techniques andmethods• Establish and iteratively evolve a project plan• Collaborate in a team• Estimate the required time and other resources for given tasks• Manage a software development project with time constraints

Contents Model driven software development methods are the key to a new levelof automation and tool integration in software development. Studentswill learn how MDSE concepts, tools an methods boost thedevelopment ofgeneral purpose and domain specific languages, leverage softwarequality analysis tools and foster automated software improvement.

Prerequisites Required:MA-INF 3218 – Seminar Model-Driven Software EngineeringThe seminar lays the conceptual foundations for the work in the lab.

Format Teaching format Group size h/week Workload[h] CPLab 8 4 60 T / 210 S 9T = face-to-face teaching; S = independent study

Exam achievements Oral presentation, written report (graded)Study achievements (not graded)Forms of media • Web page: https://sewiki.iai.uni-bonn.de/teaching/labs/start

• Slides (Powerpoint/PDF)• Wiki as a shared knowledge base• Task Tracking System (Electronical or Physical)• Shared repository for source code and development documents• Mailing list

Literature

• "Model-Driven Software Development: Technology, Engineering,Management". Thomas Stahl, Markus Voelter, Wiley 2006.• "Model-Driven Software Development". Sami Beydeda , MatthiasBook, Volker Gruhn (Eds), ISBN 978-3-540-25613-7, Springer 2005• David S. Frankel: Model Driven Architecture: Applying MDA toEnterprise Computing, John Wiley• Modellgetriebene Softwareentwicklung, Techniken, Engineering,Management. dPunkt, 2005


ModuleMA-INF 3222

eSecurity

Workload270 h


Modulecoordinator

Prof. Dr. Joachim von zur Gathen

Lecturer(s) Prof. Dr. Joachim von zur Gathen, Dr. Michael Nüsken


Technical skills Understanding of security concerns and measures, and of theinterplay between computing power and security requirements inthe realm of real-world applications, in particular internet-basedones. Mastery of advanced techniques for the design ofcryptosystems and practical cryptanalysis.

Soft skills Oral presentation (in tutorial groups), written presentation (ofexercise solutions), team collaboration in solving homeworkproblems, critical assessment.

Contents First focus: security on the internet and secure protocols.Furthermore: at least one real world application, for example• electronic health cards,• electronic elections, or• electronic passports.

Prerequisites Required:MA-INF 1103 – Cryptography


Exam achievements Written exam (graded)Study achievements Successful exercise participation (not graded)Forms of mediaLiterature Varying according to the selected topic


ModuleMA-INF 3227

Seminar Anonymity and Privacy on the Internet

Workload120 h


Modulecoordinator

Prof. Dr. Björn Scheuermann

Lecturer(s) Prof. Dr. Björn Scheuermann




Contents Current conference and journal papers.Prerequisites none




ModuleMA-INF 3229

Lab IT-Security

Workload270 h


Modulecoordinator

Prof. Dr. Michael Meier

Lecturer(s) Prof. Dr. Michael Meier


Technical skills The students will carry out a practical task (project) in thecontext of IT Security, including test and documentation of theimplemented software/system.






ModuleMA-INF 3233

Advanced Sensor Data Fusion in DistributedSystems

Workload180 h


Modulecoordinator

PD Dr. Wolfgang Koch

Lecturer(s) Dr. Felix Govaers


Technical skills For challenging state estimation tasks, algorithms which enhancethe situational awareness by fusing sensor information areinevitable. Nowadays it has become very popular to improve theperformance of systems by linking multiple sensors. This impliessome challenges to the sensor data fusion methodologies such assensor registration, communication delays, and correlations ofestimation errors. In particular, if the communication links havelimited bandwidth, data reduction techniques have to be appliedat the sensor sites, that is local tracks have to be computed.Once recieved at a fusion center (FC), the tracks then are fusedto reconstruct a global estimate. In this lecture, methodologiesto a achieve a distributed state estimation are considered.Among these are tracklet fusion, the Bar-Shalom-Campoformula, the Federated Kalman Filter, naive fusion, thedistributed Kalman filter and the least squares estimate.

Soft skills Mathematical derivation of algorithms, application ofmathematical results on estimation theory.

Contents tracklet fusion, the Bar-Shalom-Campo formula, the FederatedKalman Filter, naive fusion, the distributed Kalman filter andthe least squares estimate, Accumulated State Densities,Decorrlated fusion, product representation

Prerequisites Recommended:At least 1 of the following:BA-INF 137 – Einführung in die SensordatenfusionMA-INF 3310 – Introduction to Sensor Data Fusion - Methodsand Applications


Exam achievements Oral exam (graded)Study achievements Successful exercise participation (not graded)Forms of media Power Point

Literature

W. Koch: "Tracking and Sensor Data Fusion: MethodologicalFramework and Selected Applications", Springer, 2014.D. Hall, C.-Y. Chong, J. Llinas, and M. L. II: "Distributed DataFusion for Network-Centric Operations", CRC Press, 2014.


ModuleMA-INF 3234

Lab Mobile Sensing Systems

Workload270 h


Modulecoordinator

Jun.-Prof. Dr. Delphine Christin

Lecturer(s) Jun.-Prof. Dr. Delphine Christin


Technical skills The students will design and implement practical solutionsspecially tailored to the requirements of mobile sensing systems,including programming mobile devices and the correspondinginfrastructure.

Soft skills Organized in small teams, the students will interact andcooperate to fulfill the assignment. They will analyze the designspace and make design decisions based on this analysis. Thedesign decisions and the resulting solution will be documented ina written report and presented to other students.

Contents Mobile sensing systems leverage mobile phones as a newgeneration of sensing platforms. Embedded sensors, such ascameras, microphone, GPS, and accelerometers, are used tocapture contextual information about the users and theirsurrounding environment. Within the scope of this lab, thestudents will explore and contribute to this challenging researchfield by addressing selected topics, such as:• New mobile sensing scenarios and applications• Reputation mechanisms to identify erroneous contributions• Incentive schemes to encourage users’ contributions• Usable privacy interfaces

Prerequisites Recommended:MA-INF 3202 – Mobile Communication



Literature

Burke, J., Estrin, D., Hansen, M., Parker, A., Ramanathan, N.,Reddy, S., Srivastava, M., 2006. Participatory sensing. In:Proceedings of the 1st Workshop on World- Sensor-Web(WSW), pp. 1–5.Campbell, A., Eisenman, S., Lane, N., Miluzzo, E., Peterson, R.,2006. People-centric urban sensing. In: Proceedings of the 2ndAnnual International Wireless Internet Conference (WICON),pp. 18–31.Campbell, A., Eisenman, S., Lane, N., Miluzzo, E., Peterson, R.,Lu, H., Zheng, X., Musolesi, M., Fodor, K., Eisenman, S., Ahn,G., 2008. The rise of people-centric sensing. IEEE InternetComputing 12, 12–21.Christin, D., Reinhardt, A., Kanhere, S., Hollick, M., A surveyon privacy in mobile participatory sensing applications, Journalof Systems and Software, Volume 84, Issue 11, 2011,1928-1946.


ModuleMA-INF 3235

Usable Security and Privacy

Workload180 h


Modulecoordinator

Prof. Dr. Matthew Smith

Lecturer(s) Prof. Dr. Matthew Smith


Technical skills Students will be familiar with usability problems of IT securityand privacy mechanisms, understand methods for exploringusability of IT security and privacy mechanisms as well beingable to design and execute usability studies.

Soft skills • Working with scientific literature• Communication skills• Team working skills

Contents The lecture on Usable Security and Privacy deals with manyaspects of human factors and usability in the context of securityand privacy. The lecture includes both the foundations of usablesecurity and privacy as well as a selection of cutting edgeinternational research in this area. Topics include:• Evaluation of usability issues of existing security & privacymodels or technology• Design and evaluation of new usable security & privacytechnology• Impact of organizational policy on security and privacyinteraction• Lessons learned from designing, deploying, managing orevaluating security & privacy technologies• Foundations of usable security & privacy• Methodology for usable security & privacy research• Ethical, psychological, sociological and economic aspects ofsecurity & privacy technologies

Prerequisites Required:Knowledge about IT Security is advantageous but notmandatory.Recommended:At least 1 of the following:BA-INF 138 – IT-SicherheitBA-INF 136 – Reaktive SicherheitMA-INF 1103 – CryptographyMA-INF 3229 – Lab IT-Security




ModuleMA-INF 3236

IT Security

Workload180 h


Modulecoordinator


Lecturer(s) Prof. Dr. Michael Meier


Technical skills Students are introduced to selected active research fields of ITsecurity and gain deep knowledge of the research literature.Students learn selected aspects of IT security. This includesrisks and vulnerabilities of today’s information technology aswell as concepts to increase the level of IT security, theirapplications and their weaknesses.

Soft skills Theoretical exercises to support in-depth understanding oflecture topics and to stimulate discussions, practical exercises inteamwork to support time management, targeted organization ofpractical work and critical discussion of own and others’ results.

Contents • security threats• advanced network security: internet routing security, networkattack detection, network information hiding• cryptographic key management• building automation security• advanced host security• security patterns• privacy and pseudonymization

Prerequisites Required:Fundamental knowledge in the following areas: operatingsystems, networks, security




ModuleMA-INF 3237

Array Signal and Multi-channel Processing

Workload180 h


Modulecoordinator

Prof. Dr. Wolfgang Koch

Lecturer(s) Dr. Marc Oispuu


Technical skills Localization of multiple sources using passive sensors is afundamental task encountered in various fields like wirelesscommunication, radar, sonar, and seismology. In this lecture, aunified framework for electromagnetic and acoustic signals andsignal processing techniques are presented. Furthermore, thesensor calibration, direction finding, and bearings-onlylocalization problem are considered. Special applications areemphasized, like small airborne arrays for unmanned aerialvehicles (UAVs).

Soft skills Mathematical derivation of algorithms, applications ofmathematical results on estimation theory

Contents Estimation theory, Sensor model, Cramér-Rao analysis,conventional beamforming, Multiple Signal Classification(MUSIC), sensor calibration, Bearings-only localization, DirectPosition Determination (DPD), Applications

Prerequisites Recommended:Recommended: F. Kurth: “Foundations of Audio SignalProcessing” (MA-INF 2113)


Exam achievements Oral Exam (graded)Study achievements Successful exercise participation (not graded)Forms of media Power Point

LiteratureH. L. van Trees, Optimum Array Processing. Part IV ofDetection, Estimation, and Modulation Theory. New York:Wiley-Interscience, 2002.


ModuleMA-INF 3304

Lab Communication and Communicating Devices

Workload270 h


Modulecoordinator




Technical skills The students will carry out a practical task (project) in thecontext of communication systems, including test anddocumentation of the implemented software/system.

Soft skills Work in small teams and cooperate with other teams in a group;ability to make design decisions in a practical task; present anddiscuss (interim and final) results in the team/group and toother students; prepare written documentation of the workcarried out

Contents Selected topics close to current research in the area ofcommunication systems, network security, mobilecommunication and communicating devices.




LiteratureThe relevant literature will be announced towards the end of theprevious semester.


ModuleMA-INF 3305

Lab Information Systems

Workload270 h


Modulecoordinator

Dr. Thomas Bode

Lecturer(s) Dr. Thomas Bode


Technical skills The students will carry out a practical task (project) in thecontext of information systems, including test anddocumentation of the implemented software/system.


Contents Varying selected topics close to current research in the area ofdatabase- and information systems.

Prerequisites none





ModuleMA-INF 3309

Lab Malware Analysis

Workload270 h


Modulecoordinator




Technical skills The students will carry out a practical task (project) in thecontext of communication systems with a specific topic focus onMalware Analysis and Computer/Network Security, includingtest and documentation of the implemented software/system.

Soft skills Work in small teams and cooperate with other teams in a group;ability to make design decisions in a practical task; present anddiscuss (interim and final) results in the team/group and toother students; prepare written documentation of the workcarried out

Contents Selected topics close to current research in the area ofcommunication systems, malware analysis, computer andnetwork security.





ModuleMA-INF 3310

Introduction to Sensor Data Fusion - Methods andApplications

Workload180 h


Modulecoordinator




Technical skills All participants shall get known to the basic theory of sensordata fusion. The lecture starts with preliminaries on how tohandle uncertain data and knowledge within analytical calculus.Then, the fundamental and well-known Kalman filter is derived.Based on this tracking scheme, further approaches to a widespectrum of applications will be shown. All algorithms will bemotivated by examples from ongoing research projects,industrial cooperations, and impressions of currentdemonstration hardware.Because of inherent practical issues, every sensor measurescertain properties up to an error. This lecture shows how tomodel and overcome this error by an application of theoreticaltools such as Bayes’ rule and further derivations. Moreover,solutions to possible false-alarms, miss-detections, maneuveringphases, and much more will be presented.

Soft skills Mathematical derivation of algorithms, application ofmathematical results on estimation theory.

Contents Gaussian probability density functions, Kalman filter,Multi-Hypothesis-Trackier, Interacting Multiple Model Filter,Retrodiction, Smoothing, Maneuver Modeling

Prerequisites none



Literature

W. Koch: "Tracking and Sensor Data Fusion: MethodologicalFramework and Selected Applications", Springer, 2014.Y. Bar-Shalom: "Estimation with Applications to Tracking andNavigation", Wiley-Interscience, 2001.


ModuleMA-INF 3311

Topics in Applied Cryptography

Workload270 h


Modulecoordinator

Prof. Dr. Joachim von zur Gathen

Lecturer(s) Prof. Dr. Joachim von zur Gathen, Dr. Michael Nüsken


Technical skills Gain deeper understanding in a special area of cryptographyclose to current research.

Soft skills Oral presentation (in tutorial groups), written presentation (ofexercise solutions), team collaboration in solving homeworkproblems, critical assessment.

Contents One varying, advanced topic related to current research inapplied cryptography, e.g.• mobile security, or• design and analysis of hash functions.

Prerequisites Required:MA-INF 1103 – Cryptographyand one further course in cryptography like The Art ofCryptography or eSecurity.




ModuleMA-INF 3312

Lab Sensor Data Fusion

Workload270 h


Modulecoordinator




Technical skills The students will work together on a data fusion project usingvarious sensor hardware. Latest algorithms for fusinginformation from several nodes will be implemented.

Soft skills The students shall work together in a team. Everyone isresponsible for a specific part in the context of a main goal.Results will be exchanged and integrated via software interfaces.

Contents Varying selected topics on sensor data fusion.Prerequisites none



LiteratureThe relevant literature will be announced at the beginning of thelab.


ModuleMA-INF 3317

Seminar Selected Topics in IT Security

Workload120 h


Modulecoordinator


Lecturer(s) Prof. Dr. Michael Meier, Prof. Dr. Peter Martini








ModuleMA-INF 3318

Seminar Verification of Complex Systems

Workload120 h


Modulecoordinator

Jun.-Prof. Dr. Janis Voigtländer

Lecturer(s) Jun.-Prof. Dr. Janis Voigtländer


Technical skills Knowledge in topics in the area of specifying and verifyingbehaviour of complex systems such as software. Competence tomine for profound knowledge about a given subject, in particularacquiring and studying original literature. Understandingscientific publications, often written tersely. Distilling this intosuitable presentations; determination of relevant vs. irrelevantmaterial. Presenting research results to others, in writing and inoral presentations, and discussing them with an audience.Ability to discuss and evaluate presentations of fellow students,and to constructively deal with critical feedback by others.

Soft skills Communication skills (preparing and presenting talks, usingvisual media, preparing a structured written document), socialskills (motivating other students, ability to accept and formulatecriticism), self competences (time management withlong-ranging deadlines, self-study, ability to analyse, creativity).

Contents Techniques for analyzing the correctness of complex systemssuch as software. Theoretical foundations for such techniques, aswell as consideration of practical tools. Spectrum ranging fromformal to semi-formal; positioning of techniques within thisspectrum. Specific themes of interest include:• Specification formalisms and languages• Decision problems• Modelling desired properties of a system• Model checking• Theorem proving• Static (flow) analysis, abstract interpretation• Code analysis using heuristics• Testing (approaches, frameworks, coverage criteria)• Runtime verification (instrumentation, monitoring)• Applications and pragmatics of verificationA selection of topics will be made in each semester.

Prerequisites none




ModuleMA-INF 3319

Lab Usable Security and Privacy

Workload270 h


Modulecoordinator




Technical skills The students will carry out a practical task (project) in thecontext of usable security and privacy, including user studies.

Soft skills Ability to create and defend a scientific user studyContents Students have a great degree of freedom to chose their own

topics within the context of human aspects of security andprivacy.

Prerequisites Required:MA-INF 3235 – Usable Security and Privacy




ModuleMA-INF 3320

Lab Security in Distributed Systems

Workload270 h


Modulecoordinator




Technical skills The students will carry out a practical task (project) in thecontext of distributed security, including documentation of theimplemented software/system.Strong programming skills required.


Contents Security in distributed systems, including amongst others:• Secure Messaging• App Security• SSL/HTTPS• API Security• Machine Learning for Security• Passwords• Intrusion Detection Systems• Anomaly Detection• Security Visualisation

Prerequisites none




ModuleMA-INF 3321

Seminar Usable Security and Privacy

Workload120 h


Modulecoordinator










ModuleMA-INF 3322

Program Analysis and Binary Exploitation

Workload180 h


Modulecoordinator


Lecturer(s) Prof. Dr. Peter Martini, Dr. Elmar Padilla


Technical skills Static and dynamic program analysis, Exploitation (Stack-based BufferOverflows, Format String Exploits, Heap Exploitation, Use-After-FreeExploits) and Countermeasures (Stack Cookies, NX, ASLR, RELRO)

Soft skills Vulnerability Discovery in Computer Programs, Application of taughtTechniques, Working with Binary Representations, Assembly

Contents Our computers run a lot of closed source binary programs meaningthat the source code of those programs is not available. Naturally,those programs contain bugs, mistakes that the programmer madeduring the development. Those bugs could (under certaincircumstances) be exploited by attackers and thus may lead toarbitrary code execution. In this lecture we aim to teach you how tofind well known exploitable bugs and how to exploit them.You will first learn about basic binary program analysis such as staticand dynamic analysis. After this introduction we will talk aboutvulnerability discovery in general meaning that you will learn how tofind exploitable bugs by yourself. Next we move on to basicstack-based buffer overflows and add mitigation techniques (stackcookies, NX, ASLR, RELRO, ...) as we progress and exploit them aswell. After we finished the topic of stack-based buffer overflows wemove on to more advanced topics such as format string exploits, heapexploitation, use-after-free exploits and others. The lecture ends witha practical fuzzing example and a vulnerability analysis of anopen-source mail server.

Prerequisites Required:noneRecommended:You should have an equivalent knowledge as presented in the lectures“Kommunikation in verteilten Systemen”, “SystemnaheProgrammierung” (bonus: “Malware Boot Camp” and “ReaktiveSicherheit”)You should also have basic knowledge of the Linux operating system(including Bash) and also know basic Python programming.


Exam achievements Oral or written exam (depending on number of qualified students)(graded)

Study achievements Successful exercise participation (not graded)Forms of media

Literature The relevant literature will be announced at the beginning of thelecture


ModuleMA-INF 3323

Lab Fuzzing Bootcamp

Workload270 h


Modulecoordinator




Technical skills The students will carry out a practical task (project) in thecontext of fuzz testing, including test and documentation of theimplemented software/system.






ModuleMA-INF 3324

Lab Design of Usable Security Mechanisms

Workload270 h


Modulecoordinator


Lecturer(s) Dr. Emmanuel von Zezschwitz


Technical skills The students will carry out a practical task (project) in thecontext of usable security mechanisms, including test anddocumentation of the implemented software/system.






4 Intelligent Systems

MA-INF 4111 L2E2 6 CP Intelligent Learning and Analysis Systems: MachineLearning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102

MA-INF 4112 L2E2 6 CP Intelligent Learning and Analysis Systems: Data Miningand Knowledge Discovery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

MA-INF 4113 L2E2 6 CP Cognitive Robotics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104MA-INF 4114 L2E2 6 CP Robot Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105MA-INF 4201 L2E2 6 CP Artificial Life . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106MA-INF 4203 L2E2 6 CP Autonomous Mobile Systems . . . . . . . . . . . . . . . . . . . . . . . . . . 107MA-INF 4204 L2E2 6 CP Technical Neural Nets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108MA-INF 4207 L2E2 6 CP Dynamically Reconfigurable Systems . . . . . . . . . . . . . . . . . . 109MA-INF 4208 Sem2 4 CP Seminar Vision Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110MA-INF 4209 Sem2 4 CP Seminar Principles of Data Mining and Learning

Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111MA-INF 4210 Sem2 4 CP Seminar Advanced Topics in Technical Informatics . . . . 112MA-INF 4211 Sem2 4 CP Seminar Cognitive Robotics . . . . . . . . . . . . . . . . . . . . . . . . . . . 113MA-INF 4212 L2E2 6 CP Data Science and Big Data . . . . . . . . . . . . . . . . . . . . . . . . . . . 114MA-INF 4213 Sem2 4 CP Seminar Humanoid Robots . . . . . . . . . . . . . . . . . . . . . . . . . . . 115MA-INF 4214 Lab4 9 CP Lab Humanoid Robots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116MA-INF 4215 L2E2 6 CP Humanoid Robotics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117MA-INF 4216 L2E2 6 CP Data Mining and Machine Learning Methods in

Bioinformatics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118MA-INF 4217 Sem2 4 CP Seminar Machine Learning Methods in the Life

Sciences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119MA-INF 4218 Lab4 9 CP Lab Modeling and Simulation . . . . . . . . . . . . . . . . . . . . . . . . . 120MA-INF 4226 Lab4 9 CP Lab Parallel Computing for Mobile Robotics . . . . . . . . . . 121MA-INF 4228 L4E2 9 CP Foundations of Data Science . . . . . . . . . . . . . . . . . . . . . . . . . . 122MA-INF 4229 L4E2 9 CP Pattern Recognition (1) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123MA-INF 4302 L2E2 6 CP Advanced Learning Systems . . . . . . . . . . . . . . . . . . . . . . . . . . 124MA-INF 4303 L2E2 6 CP Learning from Non-Standard Data . . . . . . . . . . . . . . . . . . . . 125MA-INF 4304 Lab4 9 CP Lab Cognitive Robotics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126MA-INF 4306 Lab4 9 CP Lab Development and Application of Data Mining and

Learning Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127MA-INF 4307 Lab4 9 CP Lab Field Programmable Gate Arrays . . . . . . . . . . . . . . . . 128MA-INF 4308 Lab4 9 CP Lab Vision Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129MA-INF 4309 Lab4 9 CP Lab Sensor Data Interpretation . . . . . . . . . . . . . . . . . . . . . . . 130MA-INF 4310 Lab4 9 CP Lab Mobile Robots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131MA-INF 4312 L2E2 6 CP Semantic Data Web Technologies . . . . . . . . . . . . . . . . . . . . . 132MA-INF 4313 Sem2 4 CP Seminar Semantic Data Web Technologies . . . . . . . . . . . . 133MA-INF 4314 Lab4 9 CP Lab Semantic Data Web Technologies . . . . . . . . . . . . . . . . . 134MA-INF 4318 Sem2 4 CP Seminar Representation Learning for Big Data

Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135MA-INF 4319 L4E2 9 CP Game AI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136MA-INF 4320 Lab4 9 CP Lab Representation Learning on Graphs . . . . . . . . . . . . . . 137MA-INF 4321 Sem2 4 CP Seminar Learning from Time Series . . . . . . . . . . . . . . . . . . . 138MA-INF 4323 L4E2 9 CP Pattern Recognition (2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139MA-INF 4324 Sem2 4 CP Seminar Advanced Topics in Data Science . . . . . . . . . . . . 141MA-INF 4325 Lab4 9 CP Lab Data Science in Practice . . . . . . . . . . . . . . . . . . . . . . . . . 142


ModuleMA-INF 4111

Intelligent Learning and Analysis Systems: MachineLearning

Workload180 h


Modulecoordinator

Prof. Dr. Stefan Wrobel

Lecturer(s) Prof. Dr. Stefan Wrobel


Technical skills This module is one of two complementary modules in whichstudents gain an understanding of the most importantparadigms and methods of intelligent learning systems as theyare used in data analysis and/or for implementing adaptivebehaviour (machine learning, data mining, knowledge discoveryin databases). This module concentrates on the core task ofpredictive learning from examples and on agent learning, andteaches the main classes of algorithms for these tasks. At theend of the module, students will be capable of choosingappropriate methods and systems for particular predictivelearning applications and use them to arrive at convincingresults, and will know where to start whenever adaptation orfurther development of algorithms and systems is necessary.This module complements MA-INF 4112 and can be takenbefore or after that module.

Soft skills Communicative skills (oral and written presentation of solutions,discussions in small teams), self competences (ability to acceptand formulate criticism, ability to analyze problems)

Contents Types of learning and analysis tasks, most importantnon-parametric and parametric methods for supervised learning(e.g., decision trees, rules, linear methods, neural networks,neighbourhood methods, kernel methods, probabilisticapproaches), reinforcement learning, evaluation and learningtheory.

Prerequisites Required:MA-INF 4102 - Intelligent Learning and Analysis Systems hasnot been passed.Recommended:Prior knowledge of probability theory, linear algebra, artificialintelligence, information systems and data bases


Exam achievements Written exam (graded)Study achievements Successful exercise participation (not graded)Forms of media Lectures, exercises, software packages

Literature- Tom Mitchell, Machine Learning, McGraw-Hill, 1997- Ian Witten, Eibe Frank, Data Mining, Morgan Kauffmann,2000


ModuleMA-INF 4112

Intelligent Learning and Analysis Systems: DataMining and Knowledge Discovery

Workload180 h


Modulecoordinator


Lecturer(s) Prof. Dr. Wrobel


Technical skills This module is one of two complementary modules in whichstudents gain an understanding of the most importantparadigms and methods of intelligent learning systems as theyare used in data analysis and/or for implementing adaptivebehaviour (machine learning, data mining, knowledge discoveryin databases). This module concentrates on the core tasks ofpattern discovery in databases and teaches the main classes ofalgorithms for this task (subgroups discovery. At the end of themodule, students will be capable of choosing appropriatemethods and systems for particular pattern discoveryapplications and use them to arrive at convincing results, andwill know where to start whenever adaptation or furtherdevelopment of algorithms and systems is necessary. Thismodule complements MA-INF 4111 and can be taken before orafter that module.


Contents Types of learning and analysis tasks, scalability techniques,descriptive data mining methods, association rules, subgroups,clustering, pre- and postprocessing, data storage (datawarehouses, OLAP), special data types (spatial, network, text,multimedia data), interactive and visual systems.

Prerequisites Required:MA-INF 4102 - Intelligent Learning and Analysis Systems hasnot been passed.Recommended:Prior knowledge of probability theory, linear algebra, artificialintelligence, information systems and data bases


Exam achievements Written exam (graded)Study achievements Successful exercise participation (not graded)Forms of media Lectures, exercises, software packages

Literature

- Ian Witten, Eibe Frank, Data Mining, Morgan Kauffmann,2000- Jiawei Han, Micheline Kamber, Data Mining: Concepts andTechniques, Morgan Kaufmann, 2000


ModuleMA-INF 4113

Cognitive Robotics

Workload180 h


Modulecoordinator

Prof. Dr. Sven Behnke

Lecturer(s) Prof. Dr. Sven Behnke


Technical skills This lecture is one of two introductory lectures of the intelligentsystems track. The lecture covers cognitive capabilities ofrobots, like self-localization, mapping, object perception, andaction-planning in complex environments.This module complements MA-INF 4114 and can be takenbefore or after that module.


Contents Probabilistic approaches to state estimation (Bayes Filters,Kalman Filter, Particle Filter), motion models, sensor models,self-localization, mapping with known poses, simultaneousmapping and localization (SLAM), iterated closest-pointmatching, path planning, place- and person recognition, objectrecognition.

Prerequisites Required:MA-INF 4101 - Theory of Sensorimotor Systems has not beenpassed.



Literature

• S. Thrun, W. Burgard and D. Fox: Probabilistic Robotics.MIT Press, 2005.• B. Siciliano, O. Khatib (Eds.): Springer Handbook ofRobotics, 2008.• R. Szeliski: Computer Vision: Algorithms and Applications,Springer 2010.


ModuleMA-INF 4114

Robot Learning

Workload180 h


Modulecoordinator


Lecturer(s) Prof. Dr. Sven Behnke, Dr. Nils Goerke


Technical skills This lecture is one of two introductory lectures of the intelligentsystems track. Creating autonomous robots that can learn toassist humans in situations of daily life is a fascinating challengefor machine learning. The lecture covers key ingredients for ageneral robot learning approach to get closer towards human-likeperformance in robotics, such as reinforcement learning, learningmodels for control, learning motor primitives, learning fromdemonstrations and imitation learning, and interactive learning.This module complements MA-INF 4113 and can be takenbefore or after that module.


Contents Reinforcement learning, Markov decision processes, dynamicprogramming, Monte Carlo methods, temporal-differencemethods, function approximation, liear quadratic regulation,differential dynamic programming, partially observable MDPs,policy gradient methods, inverse reinforcement learning,imitation learning, learning kinematic models, perceiving andhandling of objects.

Prerequisites none



Literature

• R. Sutton and A. Barto: Reinforcement Learning, MIT-Press,1998.• O. Sigaud and J. Peters (Eds.): From Motor Learning toInteraction Learning in Robots. Springer, 2010.


ModuleMA-INF 4201

Artificial Life

Workload180 h


Modulecoordinator




Technical skills Detailed understanding of the most important approaches andprinciples of artificial life. Knowledge and understanding of thecurrent state of research in the field of artificial life

Soft skills Capability to identify the state of the art in artificial life, and topresent and defend the found solutions within the exercises infront of a group of students. Critical discussion of the results ofthe homework.

Contents Foundations of artificial life, cellular automata, Conway’s “Gameof Life”; mechanisms for structural development; foundations ofnonlinear dynamical systems, Lindenmeyer-systems,evolutionary methods and genetic algorithms, reinforcementlearning, artificial immune systems, adaptive behaviour,self-organising criticality, multi-agent systems, and swarmintelligence, particle swarm optimization.

Prerequisites none


Exam achievements Written exam (graded)Study achievements Successful exercise participation (not graded)Forms of media Pencil and paper work, explain solutions in front of athe exercise

group, implementation of small programs, use of simplesimulation tools.

Literature

• Christoph Adami: Introduction to Artificial Life, TheElectronic Library of Science, TELOS, Springer-Verlag• Eric Bonabeau, Marco Dorigo, Guy Theraulaz: SwarmIntelligence: From Natural to Artificial Systems, OxfordUniversity Press, Santa Fe Institute Studies in the Science ofComplexity.• Andrzej Osyczka: Evolutionary Algorithms for Single andMulticriteria Design Optimization, Studies in Fuzzyness andSoft Computing, Physica-Verlag, A Springer-Verlag Company,Heidelberg


ModuleMA-INF 4203

Autonomous Mobile Systems

Workload180 h


Modulecoordinator


Lecturer(s) Dr. Dirk Schulz, Prof. Dr. Sven Behnke


Technical skills Profound knowledge of development and test regarding structureand function of learning, autonomous, mobile systems;Knowledge of the computational, mathematical, and technicalrequirements for the design of autonomous systems for specificapplications and for specific functional environments

Soft skills The students will be capable to assess applications forautonomous mobile systems. They will be capable to identifywhat part of the applications might be improved by using stateof the art developments. The student will learn how to plan andimplement a software project in small working groups.

Contents Requirements for the implementation of autonomous mobilesystems, e.g. for: map making, dead reckoning, localisation,SLAM-methods, various principles of robot path planning;methods for action planning. Comparison of different learningparadigms for specific applications.

Prerequisites Recommended:all of the following:MA-INF 4101 – Theory of Sensorimotor SystemsMA-INF 4113 – Cognitive Robotics



Literature

• J. Buchli: Mobile Robots: Moving Intelligence, Published byAdvanced Robotic Systems and Pro Literatur Verlag• Sebastian Thrun, Wolfram Burgard, Dieter Fox: ProbabilisticRobotics, MIT Press, 2005• Howie Choset et al.: Principles of Robot Motion, MIT-Press,2005


ModuleMA-INF 4204

Technical Neural Nets

Workload180 h


Modulecoordinator

Prof. Dr. Joachim K. Anlauf

Lecturer(s) Prof. Dr. Joachim K. Anlauf, Dr. Nils Goerke


Technical skills Detailed knowledge of the most important neural networkapproaches and learning algorithms and its fields of application.Knowledge and understanding of technical neural networks asNon-Von Neumann computer architectures similar to concepts ofbrain functions at different stages of development

Soft skills The students will be capable to propose several paradigms fromneural networks that are capable to solve a given task. They candiscuss the pro and cons with respect to efficency and risk. Thewill be capable to plan and implement a small project with stateof the art neural network solutions.

Contents Multi-layer perceptron, radial-basis function nets, Hopfield nets,self organizing maps (Kohonen), adaptive resonance theory,learning vector quantization, recurrent networks,back-propagation of error, reinforcement learning, Q-learning,support vector machines, pulse processing neural networks.Exemplary applications of neural nets: function approximation,prediction, quality control, image processing, speech processing,action planning, control of technical processes and robots.Implementation of neural networks in hardware and software:tools, simulators, analog and digital neural hardware.

Prerequisites none



Literature

• Christopher M. Bishop: Neural Networks for PatternRecognition, Oxford University Press, ISBN-10: 0198538642,ISBN-13: 978-0198538646• Ian T. Nabney: NETLAB. Algoriths for Pattern Recognition,Springer, ISBN-10: 1852334401, ISBN-13: 978-1852334406


ModuleMA-INF 4207

Dynamically Reconfigurable Systems

Workload180 h


Modulecoordinator


Lecturer(s) Prof. Dr. Joachim K. Anlauf


Technical skills Knowledge of the most important FPGA architectures, abilityto select appropriate FPGAs for a given application, overview ofprogramming tools

Soft skills Communicative skills (oral and written presentation ofsolutions), social skills (ability to solve problems in small teams,discussions of solution concepts) self competences (ability toaccept and formulate criticism, ability to analyze problems)

Contents Architecture of FPGAs, Configurable Logic Blocks, WiringRessources, Special Blocks, Hardware Description Languages,Synthesis, Technology Mapping, Place and Route, FPGAComputing, Partial Reconfigurability

Prerequisites none


Exam achievements Oral exam (graded)Study achievements Successful exercise participation (not graded)Forms of mediaLiterature Current research papers and technical documentation


ModuleMA-INF 4208

Seminar Vision Systems

Workload120 h


Modulecoordinator


Lecturer(s) Prof. Dr. Sven Behnke, Prof. Dr. Joachim K. Anlauf,Dr. Nils Goerke


Technical skills • Knowledge in advanced topics in the area of technical visionsystems, such as image segmentation, feature extraction, andobject recognition.• Ability to understand new research results presented inoriginal scientific papers and to present them in a research talkas well as in a seminar report.

Soft skills Self-competences (time management, literature search,self-study), communication skills (preparation and clear didacticpresentation of research talk, scientific discussion, structuredwriting of seminar report), social skills (ability to formulate andaccept criticism, critical examination of research results).

Contents Current research papers from conferences and journals in thefield of vision systems covering fundamental techniques andapplications.

Prerequisites Recommended:At least 1 of the following:MA-INF 4111 – Intelligent Learning and Analysis Systems:Machine LearningMA-INF 4204 – Technical Neural Nets



Literature

• R. Szeliski: Computer Vision: Algorithms and Applications,Springer 2010.• C. M. Bishop: Pattern Recognition and Machine Learning,Springer 2006.• D. A. Forsyth and J. Ponce: Computer Vision: A ModernApproach, Prentice Hall, 2003.


ModuleMA-INF 4209

Seminar Principles of Data Mining and LearningAlgorithms

Workload120 h


Modulecoordinator




Technical skills Enhanced and in-depth knowledge in specialized topics in thearea of machine learning and data mining, acquiring thecompetence to independently study scientific literature, presentit to others and discuss it with a knowledgeable scientificauditorium. Learn how to scientifically present prior work byothers, in writing and in presentations.

Soft skills Communicative skills (preparing and presenting talks, writtenpresentation of contents in a longer document), self competences(time management with long-ranging deadlines, ability to acceptand formulate criticism, ability to analyse, creativity).

Contents Theoretical, statistical and algorithmical principles of datamining and learning algorithms. Search and optimizationalgorithms. Specialized learning algorithms from the frontier ofresearch. Fundamental results from neighbouring areas.

Prerequisites Recommended:At least 1 of the following:MA-INF 4111 – Intelligent Learning and Analysis Systems:Machine LearningMA-INF 4112 – Intelligent Learning and Analysis Systems:Data Mining and Knowledge Discovery


Exam achievements Oral presentation, written report (graded)Study achievements (not graded)Forms of media Scientific papers and websites, interactive presentations.



ModuleMA-INF 4210

Seminar Advanced Topics in Technical Informatics

Workload120 h


Modulecoordinator




Technical skills Current Topics in Technical InformaticsSoft skills Communicative skills (preparing and presenting talks, preparing

a structured written document), social skills (ability to acceptand formulate criticism, discussions of current content) selfcompetences (time management with long-ranging deadlines,understanding of research topics from original literature)

Contents Current topics such as: new architectures of computers orFPGAs (field programmable gate arrays) or new applications ofdynamically reconfigurable systems

Prerequisites none


Exam achievements Oral presentation, written report (graded)Study achievements (not graded)Forms of mediaLiterature Current research papers


ModuleMA-INF 4211

Seminar Cognitive Robotics

Workload120 h


Modulecoordinator




Technical skills Knowledge in advanced topics in the area of cognitive robotics,such as robot perception, action planning, and robot learning.Ability to understand new research results presented in originalscientific papers and to present them in a research talk as well asin a seminar report.

Soft skills Self-competences (time management, literature search,self-study), communication skills (preparation and clear didacticpresentation of research talk, scientific discussion, structuredwriting of seminar report), social skills (ability to formulate andaccept criticism, critical examination of research results).

Contents Current research papers from conferences and journals in thefield of cognitive robotics covering fundamental techniques andapplications.

Prerequisites Recommended:At least 1 of the following:MA-INF 4113 – Cognitive RoboticsMA-INF 4114 – Robot Learning



Literature

• S. Thrun, W. Burgard and D. Fox: Probabilistic Robotics.MIT Press, 2005.• B. Siciliano, O. Khatib (Eds.): Springer Handbook ofRobotics, 2008.• Selected papers.


ModuleMA-INF 4212

Data Science and Big Data

Workload180 h


Modulecoordinator


Lecturer(s) Dr. Tamas Horvath, PD Dr. Michael Mock


Technical skills Participants acquire in-depth knowledge of different aspects of bigdata analytics and systems, including distributed processing systemsand big data databases, as well as algorithmic techniques for analyzingstructured and unstructured data that cannot be stored in a singlecomputer because it has enormous size and/or continuously arriveswith such a high rate that requires immediate processing.

Soft skills Communicative skills (oral and written presentation of solutions,discussions in teams), self-competences (ability to accept and formulatecriticism, ability to analyse, creativity in the context of an "open end"task), social skills (effective team work and project planning).

Contents The module is offered every year, each time concentrating on one ormore specific issues, such as- architectures and procols for big data systems,- distributed batch and stream processing systems,- non-standard databases for big data,- databases for structured data,- similarity search,- synopses for massive data,- classical data mining tasks for massive data and/or data streams,- mining massive graphs,- applications.

Prerequisites Recommended:All of the following:MA-INF 4111 – Intelligent Learning and Analysis Systems: MachineLearningMA-INF 4112 – Intelligent Learning and Analysis Systems: DataMining and Knowledge Discovery


Exam achievements Written exam (graded)Study achievements Successful exercise participation (not graded)Forms of media lectures, exercises, software systems

Literature

- N. Marz and J. Warren: Big Data. Principles and best practices ofscalable realtime data systems. Manning Pubn, 2014.- T. White: Hadoop The Definitive Guide. O’REILLY, 2012.- A. Rajaraman and J.D. Ullman.: Mining of Massive Datasets.Cambridge University Press, 2011.- G. Cormode, M. Garofalakis, P.J. Haas, and C. Jermaine: Synopsesfor Massive Data: Samples, Histograms, Wavelets, Sketches.Foundations and Trends in Databases 4(1-3): 1-294 (2012).


ModuleMA-INF 4213

Seminar Humanoid Robots

Workload120 h


Modulecoordinator

Prof. Dr. Maren Bennewitz

Lecturer(s) Prof. Dr. Maren Bennewitz


Technical skills Knowledge in advanced topics in the area of humanoid robotics,such as environment perception, state estimation, navigation, ormotion planning. Ability to understand new research results ofscientific papers and to present them in a talk as well as in aself-written summary.

Soft skills Self-competences (time management, literature search,self-study), communication skills (preparation of the talk, cleardidactic presentation of techniques and experimental results,scientific discussion, structured writing of summary), social skills(ability to formulate and accept criticism, critical examination ofalgorithms and experimental results).

Contents Current research papers from conferences and journals in thefield of humanoid robotics covering fundamental techniques andapplications.

Prerequisites Recommended:At least 1 of the following:MA-INF 4215 – Humanoid RoboticsMA-INF 4113 – Cognitive Robotics



Literature

- S. Thrun, W. Burgard and D. Fox: Probabilistic Robotics.MIT Press- B. Siciliano, O. Khatib (Eds.): Springer Handbook of Robotics- K. Harada, E. Yoshida, K. Yokoi (Eds.), Motion Planning forHumanoid Robots, Springer- Selected papers.


ModuleMA-INF 4214

Lab Humanoid Robots

Workload270 h


Modulecoordinator




Technical skills Practical experience and in-depth knowledge in the design andimplementation of perception, state estimation, environmentrepresentation, navigation, and motion planning techniques forhumanoid robots. In small groups, the participants analyze aproblem, realize a solution, and perform an experimentalevaluation.

Soft skills Self-competences (time management, goal-oriented work, abilityto analyze problems theoretically and to find practicalsolutions), communication skills (collaboration in small teams,oral and written presentation of solutions, critical examinationof implementations).

Contents Robot middleware (ROS), perception, state estimation,environment representations, navigation, and motion planningfor humanoid robots.

Prerequisites Recommended:At least 1 of the following:MA-INF 4215 – Humanoid RoboticsMA-INF 4113 – Cognitive Robotics



Literature

- S. Thrun, W. Burgard and D. Fox: Probabilistic Robotics.MIT Press- B. Siciliano, O. Khatib (Eds.): Springer Handbook of Robotics- K. Harada, E. Yoshida, K. Yokoi (Eds.), Motion Planning forHumanoid Robots, Springer- Selected papers.


ModuleMA-INF 4215

Humanoid Robotics

Workload180 h


Modulecoordinator




Technical skills This lecture covers techniques for humanoid robots such asperception, navigation, and motion planning.

Soft skills Communicative skills (oral and written presentation of solutions,discussions in small teams), ability to analyze problems.

Contents Self-calibration with least squares, 3D environmentrepresentations, self-localization with particle filters, footstepplanning, inverse kinematics, whole-body motion planning withrapidly exploring random trees, statistical testing.

Prerequisites Recommended:MA-INF 4113 – Cognitive Robotics



Literature

• S. Thrun, W. Burgard and D. Fox: Probabilistic Robotics.MIT Press, 2005.• B. Siciliano, O. Khatib (Eds.): Springer Handbook of Robotics• K. Harada, E. Yoshida, K. Yokoi (Eds.), Motion Planning forHumanoid Robots, Springer• Selected research papers.


ModuleMA-INF 4216

Data Mining and Machine Learning Methods inBioinformatics

Workload180 h


Modulecoordinator

Dr. Holger Fröhlich

Lecturer(s) Dr. Holger Fröhlich


Technical skills - understanding and knowledge of fundamental data mining andmachine learning methods- understanding of their application in bioinformatics

Soft skills - communication: oral and written presentation of solutions toexercises- self-competences: ability to analyze application problems andto formulate possible solutions- practical skills: ability to practically implement solutions- social skills: working in a small team with other students

Contents This lecture gives a broad overview about frequently usedstatistical techniques as well as data mining and machinelearning algorithms. The use of the respective methods to solveproblems in bioinformatics is explained. The goal is tounderstand the explained methods, being able to apply themcorrectly and partially implement them. More detailed, thefollowing topics are covered in the context of their application inbioinformatics:- Short introduction to Bioinformatics and Biomedicine- Statistical Basics: Probability distributions and Bayesianinference, statistical hypothesis testing, linear models, logisticregression, Principal Component Analysis- Clustering- Hidden Markov Models- Principles of Supervised Machine Learning- Elastic Net- Basics of deep learning

Prerequisites none



Literature

T. Hastie, R. Tibshirani, J. Friedman, The Elements ofStatistical Learning, Springer, 2008S.Boslaugh, P. Watters, Statistics in a Nutshell, O’Reilly, 2008N. Jones, P. Pevzner, An Introduction to BioinformaticsAlgorithms, MIT Press, 2004


ModuleMA-INF 4217

Seminar Machine Learning Methods in the LifeSciences

Workload120 h


Modulecoordinator

Dr. Holger Fröhlich

Lecturer(s) Dr. Holger Fröhlich


Technical skills - understanding and knowledge of machine learning methodsand their application in modern life sciences, e.g. biomedicine

Soft skills - communication: oral scientific presentation of a defined topic- self-competences: ability to identify relevant literature for agiven topic; ability to read, understand and analyze scientificpublications- social skills: ability to discuss a scientific topic with otherstudents and the staff

Contents Machine learning techniques play a crucial role in modern lifesciences, including biomedicine. The goal of this seminar is todiscuss a variety of machine learning techniques in the context oftheir application to solve real-world problems in biomedicine.Topics will be selected from the following areas:- Ensemble learning- Survival and disease progression models- Bayesian Networks- Stochastic processes, e.g. Gaussian Proceses, Dirichlet ProcessMixture Models- MCMC methods- Deep learning methods, e.g. DNNs, CNNs, Deep BeliefNetworks- feature selection and non-linear embedding methods- multi-modal data fusion techniquesAttendees will be asked to perform research about their topic ina self-responsible manner.

Prerequisites Recommended:MA-INF 4216 – Data Mining and Machine Learning Methods inBioinformatics


Exam achievements Oral presentation, written report (graded)Study achievements (not graded)Forms of media powerpointLiterature selected journal and conference papers


ModuleMA-INF 4218

Lab Modeling and Simulation

Workload270 h


Modulecoordinator


Lecturer(s) Prof. Dr. Andreas Weber, Prof. Dr. Holger Fröhlich


Technical skills - ability to describe a system via a model- ability to conduct a simulation study, visualize and interpretits results- ability to implement self-written program modules inMATLAB, R or via usage of some other software

Soft skills - ability to communicate effectively in order to implementlearned methods together with a team of other students- ability to present and explain results and to defend designdecisions

Contents Simulation and analysis of complex systems that arise, forexample, in systems biology. Covered modelling approaches are:- Boolean Networks- ODEs

Prerequisites Recommended:MA-INF 4217 – Seminar Machine Learning Methods in the LifeSciences


Exam achievements Oral presentation, written report (graded)Study achievements (not graded)Forms of media powerpoint

Literature- U. Alon, An Introduction to Systems Biology, CRC Press, 2007- E.S. Allman & J.A. Rhodes “Mathematical Models in Biology”Cambr.Univ.Press 2004


ModuleMA-INF 4226

Lab Parallel Computing for Mobile Robotics

Workload270 h


Modulecoordinator




Technical skills Students will make practical experience with the design andimplementation of parallelized algorithms in the context ofmotion planning and navigation.






ModuleMA-INF 4228

Foundations of Data Science

Workload270 h


Modulecoordinator

Prof. Dr. Emmanuel Müller

Lecturer(s) Prof. Dr. Emmanuel Müller, Dr. Michael Nüsken


Technical skills Knowledge: Peculiarities of high dimensional spaces in geometryand probabilities. Singular vector decomposition. Basics inmachine learning and clustering.Skills: Understanding of mathematical tools.

Soft skills Competences: Application to data science problems and abilityto assess similar methods.

Contents Data science aims at making sense of big data. To that end,various tools have to be understood for helping in analyzing thearising structures.Often data comes as a collection of vectors with a large numberof components. To understand their common structure is thefirst main objective of understanding the data. The geometryand the linear algebra behind them becomes relevant andenlightning. Yet, the intuition from low-dimensional space turnsout to be often misleading. We need to be aware of theparticular properties of high-dimensional spaces when workingwith such data. Fruitful methods for the analysis includesingular vector decomposition from linear algebra andsupervised and unsupervised machine learning. If time permits,we also consider random graphs, which are the second most usedmodel for real world phenomena.

Prerequisites none


Exam achievements Schriftliche Prüfung (graded)Study achievements Erfolgreiche Übungsteilnahme (not graded)Forms of media

LiteratureAvrim Blum, John Hopcroft, and Ravindran Kannan (2018+).Foundations of Data Science.


ModuleMA-INF 4229

Pattern Recognition (1)

Workload270 h


Modulecoordinator




Technical skills Upon completion, students should be able to• devise mathematical models for problems in data analysis,clustering, and classification• implement basic and advanced algorithms for model fitting andoptimization• implement basic and advanced algorithms for data clustering andclassification• implement basic and advanced algorithms for pattern recognition

Soft skills Students will learn about the mathematical and algorithmicfoundations of machine learning for pattern recognition. They willlearn about basic and advanced mathematical models in this area, howto implement them on their own, and how to put them into practice.

Contents • fundamental concepts, prerequisites, and procedures in patternrecognition• basic and advanced concepts in linear algebra• basic and advanced concepts in probability theory and statistics• least squares techniques for model fitting• maximum likelihood techniques• maximum a-posteriori techniques• Bayesian inference methods• fundamental aspects of learning theory and the VC dimension• the curse of dimensionality• methods and algorithms for data clustering• Gaussian mixture models• the method of Lagrange multipliers and the KKT conditions• quadratic and linear discriminant analysis• algorithms for constrained optimization• support vector machines• the kernel trick• neural networks• Hebbian learning

Prerequisites Recommended:Students should good working knowledge in linear algebra, probabilitytheory, and statistics as well as programming experience.


Exam achievements Schriftliche Prüfung (graded)Study achievements Erfolgreiche Übungsteilnahme (not graded)Forms of media • lecture slides are made available online

• lecture notes with programming examples are made available online

LiteratureBishop, "Pattern Recognition and Machine Learning"Duda, Stork, Hart, "Pattern Classification"MacKay, "Information Theory, Inference, and Learning Algorithms"


ModuleMA-INF 4302

Advanced Learning Systems

Workload180 h


Modulecoordinator


Lecturer(s) Prof. Dr. Stefan Wrobel, Dr. Thomas Gärtner


Technical skills Participants specialize and require in-depth knowledge of oneparticular class of learning algorithms, they acquire thenecessary knowledge to improve existing algorithms andconstruct their own within the given class, all the way up to theresearch frontier on the topic.

Soft skills In group work, students acquire the necessary social andcommunication skills for effective team work and projectplanning, and learn how to present software projects to others.

Contents The module is offered every year, each time concentrating onone or more specific algorithm classes, e.g.• kernel machines• neural networks• probabilistic and statistical learning approaches• logic-based learning approaches• reinforcement learning

Prerequisites Recommended:all of the following:MA-INF 4111 – Intelligent Learning and Analysis Systems:Machine LearningMA-INF 4112 – Intelligent Learning and Analysis Systems:Data Mining and Knowledge Discovery


Exam achievements Written exam (graded)Study achievements Successful exercise participation (not graded)Forms of media lectures, exercises, software systems

Literature

• B. Schoelkopf, A.J. Smola, Learning with Kernels, The MITPress, 2002, Cambridge, MA• John Shawe-Taylor, Nello Christianini, Kernel Methods forPattern Analysis, CUP, 2004• Christopher Bishop, Pattern Recognition and MachineLearning, The University of Edinburgh, 2006• David MacKay, Information Theory, Inference, and LearningAlgorithms, 2003• Richard Duda, Peter Hart, David Stork, PatternClassification, John Wiley and Sons, 2001


ModuleMA-INF 4303

Learning from Non-Standard Data

Workload180 h


Modulecoordinator


Lecturer(s) Prof. Dr. Stefan Wrobel, Dr. Tamas Horvath


Technical skills Participants deepen their knowledge of learning systems withrespect to one particular non-standard data type, i.e.,non-tabular data, as they are becoming increasingly importantin many applications. Each type of data not only requiresspecialized algorithms but also knowledge of the surroundingpre- and postprocessing operations which is acquired by theparticipants in the module. In group work, students acquire thenecessary social and communication skills for effective teamwork and project planning, and learn how to present softwareprojects to others.

Soft skills Communicative skills (oral and written presentation of solutions,discussions in teams), self-competences (ability to accept andformulate criticism, ability to analyse, creativity in the contextof an "open end" task)

Contents The module will offered every year, concentrating on oneparticular non-standard data type each time, including: TextMining, Multimedia Mining, Graph Mining. Learning fromstructured data, Spatial Data Mining

Prerequisites Recommended:all of the following:MA-INF 4111 – Intelligent Learning and Analysis Systems:Machine LearningMA-INF 4112 – Intelligent Learning and Analysis Systems:Data Mining and Knowledge Discovery


Exam achievements Written exam (graded)Study achievements Successful exercise participation (not graded)Forms of media lectures, exercises, software systems.

Literature

• Gennady Andrienko, Natalia Andrienko, Exploratory Analysisof Spatial and Temporal Data, Springer, 2006• Diane J. Cook, Lawrence B. Holder, Mining Graph Data,Wiley & Sons, 2006• Saso Dzeroski, Nada Lavrac, Relational Data Mining,Springer, 2001• Sholom M. Weiss, Nitin Indurkhya, Tong Zhang, Fred J.Damerau, Text Mining. Predictive Methods for AnalyzingUnstructured Information, Springer, 2004


ModuleMA-INF 4304

Lab Cognitive Robotics

Workload270 h


Modulecoordinator


Lecturer(s) Prof. Dr. Sven Behnke


Technical skills Participants acquire practical experience and in-depthknowledge in the design and implementation of perception andcontrol algorithms for complex robotic systems. In a smallgroup, they analyze a problem, realize a state-of-the-artsolution, and evaluate its performance.

Soft skills Self-competences (time management, goal-oriented work, abilityto analyze problems and to find practical solutions),communication skills (Work together in small teams, oral andwritten presentation of solutions, critical examination ofimplementations)

Contents Robot middleware (ROS), simultaneous localization andmapping (SLAM), 3D representations of objects andenvironments, object detection and recognition, person detectionand tracking, action recognition, action planning and control,mobile manipulation, human-robot interaction.

Prerequisites Recommended:At least 1 of the following:MA-INF 4113 – Cognitive RoboticsMA-INF 4114 – Robot Learning



Literature

• S. Thrun, W. Burgard and D. Fox: Probabilistic Robotics.MIT Press, 2005.• B. Siciliano, O. Khatib (Eds.): Springer Handbook ofRobotics, 2008.• Selected research papers.


ModuleMA-INF 4306

Lab Development and Application of Data Miningand Learning Systems

Workload270 h


Modulecoordinator




Technical skills Students will acquire in-depth knowledge in the constructionand development of intelligent learning systems for machinelearning and data mining. They learn how to work with existingstate-of-the-art systems and apply them to applicationproblems, usually extending them for the requirements of theirparticular task.

Soft skills Communicative skills (appropriate oral presentation and writtendocumentation of project results), social skills (ability to work inteams), self-competences (time management, aiming atlong-range goals under limited ressources, ability to work underpressure, ability to accept/formulate ciriticsm)

Contents Data storage and process models of data analysis. Commonopen source frameworks for the construction of data analysissystems, specialized statistical packages. Pre-processing tools.Mathematical libraries for numerical computation. Search andoptimization methods. User interfaces and visualization foranalysis systems. Data analysis algorithms for embedded anddistributed systems. Ubiquitous discovery systems.

Prerequisites Recommended:At least 1 of the following:MA-INF 4111 – Intelligent Learning and Analysis Systems:Machine LearningMA-INF 4112 – Intelligent Learning and Analysis Systems:Data Mining and Knowledge Discovery


Exam achievements Oral presentation, written report (graded)Study achievements (not graded)Forms of media Computer Software, Documentation, Research Papers.



ModuleMA-INF 4307

Lab Field Programmable Gate Arrays

Workload270 h


Modulecoordinator




Technical skills Development and simulation of digital circuits in VHDL andSystemC, experience with synthesizable subsets, knowledge ofthe design path from the idea to a realized circuit implementedin an FPGA (field programmable gate array)

Soft skills Communicative skills (oral and written presentation of results),social skills (ability to cooperate in small teams, discussions ofsolution concepts) self competences (ability to accept andformulate criticism, ability to analyze and find practicalsolutions to problems)

Contents VHDL for Hardware Description, Simulation, and Synthesis,SystemC for Hardware Description, Simulation, and Synthesis,Synthesizable Subsets, Test of Implementations on FPGAEvaluation Boards

Prerequisites Recommended:MA-INF 4207 - Dynamically Reconfigurable Systems


Exam achievements Oral presentation, written report (graded)Study achievements (not graded)Forms of mediaLiterature Technical documentation


ModuleMA-INF 4308

Lab Vision Systems

Workload270 h


Modulecoordinator


Lecturer(s) Dr. Nils Goerke


Technical skills Students will acquire knowledge of the design andimplementation of parallel algorithms on GPUs. They will applythese techniques to accelerate standard machine learningalgorithms for data-intensive computer vision tasks.


Contents Basic matrix and vector computations with GPUs (CUDA).Classification algorithms, such as multi-layer perceptrons,support-vector machines, k-nearest neighbors,linear-discriminant analysis. Image preprocessing and datahandling. Quantitative performance evaluation of learningalgorithms for segmentation and categorization.

Prerequisites Recommended:At least 1 of the following:MA-INF 4111 – Intelligent Learning and Analysis Systems:Machine LearningMA-INF 4204 – Technical Neural Nets



Literature

• R. Szeliski: Computer Vision: Algorithms and Applications,Springer 2010.• C. M. Bishop: Pattern Recognition and Machine Learning,Springer 2006.• NVidia CUDA Programming Guide, Version 4.0, 2011.


ModuleMA-INF 4309

Lab Sensor Data Interpretation

Workload270 h


Modulecoordinator

PD. Dr. Volker Steinhage

Lecturer(s) PD. Dr. Volker Steinhage


Technical skills Competence to implement algorithms for sensor datainterpretation, efficient handling and testing, documentation.

Soft skills Efficient implementation of complex algorithms, abstractthinking, documentation of source code.

Contents Varying selected up-to-date topics on sensor data interpretationPrerequisites Required:

All of the following:MA-INF 2201 – Computer VisionMA-INF 4206 – Selected Topics in Sensor Data Interpretation


Exam achievements Oral presentation, written report (graded)Study achievements (not graded)Forms of mediaLiterature Relevant literature will be announced at start of the lab.


ModuleMA-INF 4310

Lab Mobile Robots

Workload270 h


Modulecoordinator




Technical skills Participants acquire basic knowledge and practical experience inthe design and implementation of control algorithms for simplestructured robotic systems using real mobile robots.Fundamental paradigms for mobile robots will be identified andimplemented in 2 person groups.


Contents Robot middleware (e.g. ROS), robot simulation tools, basiccapabilities for mobile robots: reactive control, SMPAarchitecture, navigation, path planning, localisation,simultaneous localization and mapping (SLAM), visual basedobject detection, learning robot control.

Prerequisites Recommended:At least 1 of the following:BA-INF 132 – Grundlagen der RobotikBA-INF 131 – Intelligente SehsystemeMA-INF 1314 – Online Motion PlanningMA-INF 2201 – Computer VisionMA-INF 4113 – Cognitive RoboticsMA-INF 4114 – Robot LearningMA-INF 4203 – Autonomous Mobile Systems


Exam achievements Oral presentation, written report (graded)Study achievements (not graded)Forms of media Robots simulation environments, robot control middleware,

computer vision libraries, programming, demonstration of robotcapabilities (real robotic systems), presentation and writtenreport of approach and results.

Literature

• S. Thrun, W. Burgard and D. Fox: Probabilistic Robotics.MIT Press, 2005.• J. Buchli: Mobile Robots: Moving Intelligence, Published byAdvanced Robotic Systems and Pro Literatur Verlag• B. Siciliano, O. Khatib (Eds.): Springer Handbook ofRobotics, 2008.• Additional State-of-the-art publications.


ModuleMA-INF 4312

Semantic Data Web Technologies

Workload180 h


Modulecoordinator

Prof. Dr. Jens Lehmann

Lecturer(s) Prof. Dr. Jens Lehmann, Dr. Christoph Lange,Dr. Maria Maleschkova


Technical skills The goal of this lecture is to impart knowledge on thefundamentals, technologies and applications of the SemanticWeb and information retrieval. As part of the lecture the basicconcepts and standards for semantic technologies are explained.

Soft skillsContents As part of the W3C Semantic Web initiative standards and

technologies have been developed for machine-readable exchangeof data, information and knowledge on the Web. Thesestandards and technologies are increasingly being used inapplications and have already led to a number of excitingprojects (e.g. DBpedia, semantic wiki or commercialapplications such as schema.org, OpenCalais, or Google’sFreebase). The module provides a theoretically grounded andpractically oriented introduction to this area. The topicsdiscussed within the lecture include:• RDF syntax and data model• RDF Schema and formal semantics of RDF (S)• ontologies in OWL and formal semantics of OWL• RDF databases, triple and knowledge stores, query languages• Linked Data Web and Semantic Web applications• Semantic text analysis and information retrieval systems

Prerequisites none




ModuleMA-INF 4313

Seminar Semantic Data Web Technologies

Workload120 h


Modulecoordinator


Lecturer(s) Dr. Christoph Lange, Dr. Maria Maleshkova


Technical skills Through the seminar, students will learn to work with tools andtechnologies of the Semantic Web as well as assess theircapabilities for given problems. They will gain the ability tounderstand new research results presented in original scientificpapers.

Soft skills Ability to present and to critically discuss technologies andresearch results in the framework of Semantic Web technologies.

Contents • technologies such as triple stores, link discovery frameworks,NLP pipelines.• recent conference and journal papers

Prerequisites none




ModuleMA-INF 4314

Lab Semantic Data Web Technologies

Workload270 h


Modulecoordinator


Lecturer(s) Prof. Dr. Jens Lehmann, Dr. Maria Maleschkova


Technical skills The students will carry out a practical task (project) in thecontext of Semantic Web technologies, including test anddocumentation of the implemented software/system.

Soft skills Ability to properly present and defend design decisions, toprepare readable documentation of software; skills inconstructively collaborating with others in small teams over alonger period of time; ability to classify own results with regardto the state-of-the-art





ModuleMA-INF 4318

Seminar Representation Learning for Big DataAnalytics

Workload120 h


Modulecoordinator


Lecturer(s) Prof. Dr. Emmanuel Müller




Contents Smart representations (such as embeddings, kernels, anddimensionality reduction methods) are useful models that allow theabstraction of data within a well-defined mathematical formalism. Therepresentations we aim at are conceptual abstractions of real worldphenomena (such as social interactions, chemical reactions andbiological processes) into the world of statistics and discretemathematics in such a way that the powerful tools developed in thoseareas are available for complex analyses in a simple and elegantmanner.The focus will be the understanding and comparison of smartrepresentations and their explicit/implicit data transformation models.We will study limitations and advantages of different techniques, andhow the data representation changes the problem setup, reducescomplexity, introduces robustness, or other valuable properties for bigdata analytics.

Prerequisites Recommended:Open-minded for new problem settings, Programming in differentlanguages (C++, Python, Java), Critical approach to existingsolutions, Research curiosity



Literature

[1] Sergey Ivanov, Evgeny Burnaev. "Anonymous Walk Embeddings"ICML, 2018.[2] Tsitsulin, Anton, Davide Mottin, Panagiotis Karras, and EmmanuelMüller "VERSE: Versatile Graph Embeddings from SimilarityMeasures." WWW, 2018.[3] Yanardag, Pinar, and S. V. N. Vishwanathan. "Deep graphkernels." KDD, 2015.[4] Holger Dell, Martin Grohe, Gaurav Rattan "Lovász MeetsWeisfeiler and Leman". ICALP, 2018[5] Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Alexander M.Bronstein, Emmanuel Müller "NetLSD: Hearing the Shape of aGraph". KDD, 2018[6] Nino Shervashidze, Pascal Schweitzer, Erik Jan van Leeuwen, KurtMehlhorn, Karsten M. Borgwardt "Weisfeiler-Lehman Graph Kernels".JMLR, 2011[7] Haochen Chen, Bryan Perozzi, Yifan Hu, Steven Skiena "HARP:Hierarchical Representation Learning for Networks". AAAI, 2018.


ModuleMA-INF 4319

Game AI

Workload270 h


Modulecoordinator




Technical skills Upon completion, students should be able to• know about fundamental concepts of artificial intelligence and howthey apply to computer games• know about basic and advanced methods for planning, problemsolving, and behavior modelling• implement basic and advanced algorithms for planning, problemsolving, and behavior modelling• implement numerically robust data clustering and classification

Soft skills Students will learn about mathematical and algorithmic foundations ofartificial intelligence. They will learn about basic and more advancedtechniques for planning, problem solving, and behavior modelling, howto implement them on their own, and how to put them into practiceespecially in the context of artificial computer game agents.

Contents • historical overview of game AI• basic terms and definitions for AI in games• backward induction and the minmax algorithm• alpha-beta pruning, depth restircted searches, features, andevaluation functions• (traditional, uninformed) tree search algorithms• Monte Carlo tree search• algorithms for path- and motion planning, A* search• mathematical models and computer algorithms for data clustering• self organizing maps• finite state machines for behavior modeling / programming• fuzzy logic / fuzzy control for behavior modeling / programming• probability theory and Bayesian networks• Markov chains / Markov models• hidden Markov models for behavior modeling and analysis• Markov decision processes and reinforcement learning• the Bellman equations for reinforcement learning• temporal difference learning• Q learning• genetic algorithms and genetic programming

Prerequisites Recommended:Students should good working knowledge in linear algebra, probabilitytheory, and statistics as well as programming experience.


Exam achievements Oral exam (graded)Study achievements Successful exercise participation (not graded)Forms of media • lecture slides are made available online


LiteratureRussell and Norvig, "Artificial Intelligence: A Modern Approach"Millington, "Artificial Intelligence For Games"MacKay, "Information Theory, Inference, and Learning Algorithms"


ModuleMA-INF 4320

Lab Representation Learning on Graphs

Workload270 h


Modulecoordinator




Technical skills The students will carry out a practical task (project) in thecontext of representation learning on graphs, including test anddocumentation of the implemented software/system.






ModuleMA-INF 4321

Seminar Learning from Time Series

Workload120 h


Modulecoordinator










ModuleMA-INF 4323

Pattern Recognition (2)

Workload270 h


Modulecoordinator




Technical skills Upon completion, students should be able to• know about aspects of numerical computing and how these mayaffect practical implementationsof machine learning / pattern recognition algorithms• know about iterative algorithms for machine learning / patternrecognition with large data sets• implement numerically robust algorithms for data dimensionalityreduction• implement numerically robust data clustering and classification

Soft skills Students will learn about mathematical and algorithmic foundations ofrobust implementations of machine learning algorithms for dataanalysis and pattern recognition. They will learn about iterativealgorithms and dynamical systems approaches in this area, how toimplement them on their own, and how to put them into practice.

Contents • advanced concepts from linear algebra• QR-, spectral-, and singular value decompositions• iterative algorithms for least squares optimization• iterative algorithms for principal component analysis• Hebbian learning and Oja’s rule for principal compoentn analysis• auto-encoder networks• associative memory networks• Hopfield networks• Hopfield networks for pattern recognition• Hopfield networks for problem solving• energy minimization methods in machine learning and patternrecognition• latent factor models for data analysis• data matrix factorization techniques• multidimensional scaling• manifold learning• basic graph theory• graph cuts and graph clustering• graph diffusion processes• radial basis functions for interpolation• radial basis functions for classification• radial basis functions for density estimation

Prerequisites Recommended:Students should good working knowledge in linear algebra, probabilitytheory, and statistics. Ideally, they will have attended the lecturePattern Recognition (1).


Exam achievements Schriftliche Prüfung (graded)Study achievements Erfolgreiche Übungsteilnahme (not graded)Forms of media • lecture slides are made available online


Literature

MacKay, "Information Theory, Inference, and Learning Algorithms"Haykin, "Neural Networks and Learning Machines"Bishop, "Neural Networks for Pattern Recognition"Elden, "Matrix Methods in Data Mining and Pattern Recognition"Skillicorn, "Understanding Complex Datasets"Kirby, "Geometric Data Analysis"


ModuleMA-INF 4324

Seminar Advanced Topics in Data Science

Workload120 h


Modulecoordinator

Prof. Dr. Elena Demidova

Lecturer(s) Prof. Dr. Elena Demidova


Technical skills This module concentrates on specialized topics in data science.The students obtain skills in the independent, in-depth study ofstate-of-the-art scientific literature on specific topics, discussionwith their peers and presentation to the scientific audience.

Soft skills • Communication skills: oral and written presentation ofscientific content.• Self-competences: the ability to analyze problems, timemanagement, creativity.

Contents Statistical and machine learning-based methods of dataanalytics, including typical steps of the data science process:data generation, integration, cleaning, exploration, modellingand evaluation. Specialized data representation and analyticsmethods for selected data types and applications in specificdomains.

Prerequisites Recommended:BA-INF 150 - Einführung in die Data Science


Exam achievements Oral presentation, written report (graded)Study achievements None (not graded)Forms of media

LiteratureRelevant literature will be announced at the beginning of theseminar


ModuleMA-INF 4325

Lab Data Science in Practice

Workload270 h


Modulecoordinator

Prof. Dr. Elena Demidova

Lecturer(s) Prof. Dr. Elena Demidova


Technical skills This module concentrates on practical experience in dataanalytics. Participants acquire basic knowledge and practicalexperience in the design and implementation of data scienceworkflows for specific data types and applications.

Soft skills • Communication skills: the ability to work in teams.• Self-competences: the ability to analyse problems and findpractical solutions. Time management, creativity, presentationof results.

Contents Practical application of statistical and machine learning-basedmethods to solve data analytics problems on real-world datasetsand evaluate proposed solutions.

Prerequisites Recommended:BA-INF 150 - Einführung in die Data ScienceMA-INF 4324 - Seminar Advanced Topics in Data Science


Exam achievements Oral presentation, written report (graded)Study achievements None (not graded)Forms of mediaLiterature


5 Master Thesis

MA-INF 0401 30 CP Master Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144MA-INF 0402 2 CP Master Seminar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145


ModuleMA-INF 0401

Master Thesis

Workload900 h


ModulecoordinatorLecturer(s) All lecturers of computer science

ClassificationProgramme Mode SemesterM. Sc. Computer Science Compulsory 4.

Technical skills Ability to solve a well-defined, significant research problemunder supervision, but in principle independently

Soft skills Ability to write a scientific documentation of considerable lengthaccording to established scientific principles of form and style, inparticular reflecting solid knowledge about the state-of-the-art inthe field

Contents Topics of the thesis may be chosen from any of the areas ofcomputer science represented in the curriculum

Prerequisites none

Format

Teaching format Group size h/week Workload[h] CPIndependentpreparation of ascientific thesis withindividual coaching

0 900 S 30

T = face-to-face teaching; S = independent studyExam achievements Master Thesis (graded)Study achievements (not graded)Forms of media

LiteratureIndividual bibliographic research required for identifyingrelevant literature (depending on the topic of the thesis)


ModuleMA-INF 0402

Master Seminar

Workload60 h


ModulecoordinatorLecturer(s) All lecturers of computer science

ClassificationProgramme Mode SemesterM. Sc. Computer Science Compulsory 4.

Technical skills Ability to document and defend the results of the thesis work ina scientifically appropriate style, taking into consideration thestate-of-the-art in research in the resp. area

Soft skillsContents Topic, scientific context, and results of the master thesisPrerequisites none

FormatTeaching format Group size h/week Workload[h] CPSeminar 2 30 T / 30 S 2T = face-to-face teaching; S = independent study

Exam achievements Oral presentation of final results (graded)Study achievements (not graded)Forms of media

LiteratureIndividual bibliographic research required for identifyingrelevant literature (depending on the topic of the thesis)

module handbook · 2020. 11. 2. · mastercomputerscience—universitätbonn 4 module ma-inf1103...

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