module handbook for the master's program

Module Handbookfor the

Master Programme “Computer Science”

at

Rheinischen Friedrich-Wilhelms-Universität Bonn

revised version: September 29, 2015

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 33

3 Information and Communication Management 62

4 Intelligent Systems 104

5 Master Thesis 138

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 for NP-Hard Problems . . . . . . . 6MA-INF 1202 L4E2 9 CP Chip Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7MA-INF 1203 L4E2 9 CP Discrete and Computational Geometry . . . . . . . . . . . . . . . . . . 8MA-INF 1204 Sem2 4 CP Seminar Selected Topics in Information and Learning

Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9MA-INF 1205 Sem4 6 CP Graduate Seminar Discrete Optimization . . . . . . . . . . . . . . . 10MA-INF 1206 Sem2 4 CP Seminar Design and Analysis of Randomized

Approximation Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11MA-INF 1207 Lab4 9 CP Lab Combinatorial Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . 12MA-INF 1209 Sem2 4 CP Seminar Advanced Topics in Cryptography . . . . . . . . . . . . . 13MA-INF 1210 L2E2 6 CP Probabilistic Analysis of Algorithms . . . . . . . . . . . . . . . . . . . 14MA-INF 1211 L4E2 9 CP Parameterized Complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15MA-INF 1212 Sem2 4 CP Seminar Parameterized Complexity . . . . . . . . . . . . . . . . . . . . 16MA-INF 1213 L4E2 9 CP Randomized Algorithms and Probabilistic Analysis . . . . . 17MA-INF 1301 L4E2 9 CP Algorithmic Game Theory and the Internet . . . . . . . . . . . . 18MA-INF 1302 L4E2 9 CP Advanced Topics in Algorithmics . . . . . . . . . . . . . . . . . . . . . . . 19MA-INF 1303 L2E2 6 CP Selected Topics in Algorithmics . . . . . . . . . . . . . . . . . . . . . . . . 20MA-INF 1304 Sem2 4 CP Seminar Geometric Distance Problems . . . . . . . . . . . . . . . . . 21MA-INF 1305 Sem4 6 CP Graduate Seminar Chip Design . . . . . . . . . . . . . . . . . . . . . . . . 22MA-INF 1306 Sem2 4 CP Seminar Combinatorial and Geometric Optimization . . . 23MA-INF 1307 Sem2 4 CP Seminar Advanced Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . 24MA-INF 1308 Lab4 9 CP Lab Algorithms for Chip Design . . . . . . . . . . . . . . . . . . . . . . . 25MA-INF 1309 Lab4 9 CP Lab Efficient Algorithms for Selected Problems: Design,

Analysis and Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . 26MA-INF 1312 L4E2 9 CP The Art of Cryptography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27MA-INF 1313 L4E2 9 CP Topics in Theoretical Cryptography . . . . . . . . . . . . . . . . . . . . 28MA-INF 1314 L4E2 9 CP Online Motion Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29MA-INF 1315 Lab4 9 CP Lab Computational Geometry . . . . . . . . . . . . . . . . . . . . . . . . . 30MA-INF 1317 Lab4 9 CP Lab Parameterized Complexity . . . . . . . . . . . . . . . . . . . . . . . . 31MA-INF 1318 L2E2 6 CP Theoretical Aspects of Intruder Search . . . . . . . . . . . . . . . . . 32


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) Prof. Dr. Jens Vygen, Prof. Dr. Norbert Blum,Prof. Dr. Stefan Hougardy, Prof. Dr. Marek Karpinski,Prof. Dr. Bernhard Korte, Prof. Dr. Stephan Held

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 60 4 60 T / 105 S 5.5Exercises 30 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, 5th edition, 2012• A. Schrijver: Combinatorial Optimization: Polyhedra andEfficiency. Springer 2003• W. Cook, W. Cunningham, W. Pulleyblank, A. Schrijver:Combinatorial Optimization. Wiley 1997


ModuleMA-INF 1103

Cryptography

Workload270 h

Credit points Duration Frequency9 CP 1 semester every year

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.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. Stefan Kratsch

Lecturer(s) Prof. Dr. Stefan Kratsch, Prof. Dr. Heiko Röglin

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 for NP-Hard Problems

Workload270 h


Modulecoordinator

Prof. Dr. Marek Karpinski

Lecturer(s) Prof. Dr. Marek Karpinski, Prof. Dr. Norbert Blum,Prof. Dr. Rolf Klein, Prof. Dr. Bernhard Korte,Prof. Dr. Jens Vygen, Prof. Dr. Stefan Hougardy,Prof. Dr. Stephan Held


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 (5th edition), Springer, 2012• 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


Lecturer(s) All lecturers of Discrete Mathematics


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,clocktree design

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• J. Vygen: Chip Design. Lecture Notes (distributed during thecourse)


ModuleMA-INF 1203

Discrete and Computational Geometry

Workload270 h


Modulecoordinator

Prof. Dr. Rolf Klein

Lecturer(s) Prof. Dr. Rolf Klein, Prof. Dr. Norbert Blum,Prof. Dr. Marek Karpinski, PD Dr. Elmar Langetepe

ClassificationProgramme Mode SemesterM. Sc. Computer Science Optional 1., 2., 3. or 4.

Technical skills To acquire fundamental knowledge on topics and methods inDiscrete and Computational Geometry; to gain experience in,and practice, applying this knowledge autonomously in solvingnew problems, aiming at reliable experience.

Soft skills Sozialkompetenz (Kommunikationsfähigkeit, Präsentationeigener Lösungsansätze und zielorientierte Diskussion imGruppenrahmen, Teamfähigkeit), Methodenkompetenz(Analysefähigkeit, Abstraktes Denken, Führen von Beweisen),Individualkompetenz (Leistungs- und Lernbereitschaft,Kreativität, Ausdauer).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 Geometric distance problems in dimension two and higher,Voronoi diagrams, well-separated pair decomposition, spanner,metric space embedding, dimension reduction, dilation,geometric inequalities, VC-dimension, epsilon-nets, visibility,point location;randomized incremental construction, Chan’s technique.

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



Literature• Matousek, Lectures on Discrete Geometry• Narasimhan/Smid, Geometric Spanner Networks• Klein, Concrete and Abstract Voronoi Diagrams


ModuleMA-INF 1204

Seminar Selected Topics in Information andLearning Theory

Workload120 h

Credit points Duration Frequency4 CP 1 semester at least every 2 years

Modulecoordinator

Prof. Dr. Norbert Blum

Lecturer(s) Prof. Dr. Norbert Blum


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

Soft skills Presentation of own and others’ solutions and methods, criticaldiscussion of applied methods, techniques and solutions.

Contents Advanced topics in information and learning theory based onmodern research literature

Prerequisites none

FormatTeaching 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 none (not graded)Forms of media

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


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 Required:MA-INF 1102 – Combinatorial Optimization



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


ModuleMA-INF 1206

Seminar Design and Analysis of RandomizedApproximation Algorithms

Workload120 h


Modulecoordinator


Lecturer(s) Prof. Dr. Marek Karpinski, Prof. Dr. Heiko Röglin


Technical skills Ability to perform individual literature search, critical reading,understanding, and clear didactic 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 none (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 Required: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




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 none (not graded)Forms of mediaLiterature Current conference publications, to be announced in time


ModuleMA-INF 1210

Probabilistic Analysis of Algorithms

Workload180 h


Modulecoordinator

Prof. Dr. Heiko Röglin

Lecturer(s) Prof. Dr. Heiko Röglin


Technical skills understanding of models and techniques for the probabilisticanalysis of algorithms

Soft skills oral and written presentation of solutions and methods, abstractthinking

Contents smoothed and average-case analysis• simplex algorithm• local search algorithms• clustering algorithms• combinatorial optimization problems• multi-objective optimization

Prerequisites Required: None of the following modules have been passed:MA-INF 1213 – Randomized Algorithms and ProbabilisticAnalysis


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


ModuleMA-INF 1211

Parameterized Complexity

Workload270 h


Modulecoordinator


Lecturer(s) Prof. Dr. Stefan Kratsch

ClassificationProgramme Mode SemesterM. Sc. Computer Science Optional 1., 2. or 3.

Technical skills A fundamental understanding of the more fine-grainedcomplexity analysis of NP-hard problems that is provided byparameterized complexity. Learning to employ a rich toolbox oftechniques for upper and lower bounds on the complexity ofparameterized problems.

Soft skills • Social competence: solving exercise tasks in teams, presentingsolutions• methodical competence: analysis, abstraction, proofs• individual competence: learning, reading scientificpapers/book chapters, abstraction

Contents • Parameterized problems• Algorithmic techniques: bounded search trees, kernelization,treewidth, iterative compression, color coding, algebraicalgorithms, etc.• Methods for establishing intractability: parameterizedreductions, hardness under ETH/SETH, lower bounds forkernelization

Prerequisites none



Literature

• Downey/Fellows: Fundamentals of Parameterized Complexity• Flum/Grohe: Parameterized Complexity Theory• Niedermeier: Invitation to Fixed Parameter Algorithms• Cygan et al.: Parameterized Algorithms (to appear)


ModuleMA-INF 1212

Seminar Parameterized Complexity

Workload120 h


Modulecoordinator




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 papers from parameterizedcomplexity.

Prerequisites none


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


ModuleMA-INF 1213

Randomized Algorithms and Probabilistic Analysis

Workload270 h


Modulecoordinator

Prof. Dr. Heiko Röglin

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, abstractthinking

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 Required: None of the following modules have been passed:MA-INF 1210 – Probabilistic Analysis of Algorithms



Literature

• lecture notes• research articles• Motwani, Raghavan: Randomized Algorithms• Mitzenmacher, Upfal: Probability and Computing


ModuleMA-INF 1301

Algorithmic Game Theory and the Internet

Workload270 h

Credit points Duration Frequency9 CP 1 semester every 2 years

Modulecoordinator


Lecturer(s) Prof. Dr. Marek Karpinski, Prof. Dr. Norbert Blum


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.




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 1302

Advanced Topics in Algorithmics

Workload270 h


Modulecoordinator


Lecturer(s) Prof. Dr. Marek Karpinski, Prof. Dr. Norbert Blum,Prof. Dr. Joachim von zur Gathen, Prof. Dr. Rolf Klein


Technical skills Introduction to current advanced research topics in algorithmicresearch


Contents The topic will be announced before the start of the relevantsemester.




LiteratureDepending on the topics varying from semester to semester, therelevant research literature will be announced before the start ofthe resp. semester.


ModuleMA-INF 1303

Selected Topics in Algorithmics

Workload180 h


Modulecoordinator

Prof. Dr. Norbert Blum

Lecturer(s) Prof. Dr. Norbert Blum, Prof. Dr. Rolf Klein,Prof. Dr. Marek Karpinski


Technical skills Introduction to current advanced research topics in algorithmicresearch

Soft skills Presentation of own and others’ solutions and methods, criticaldiscussion of applied methods, techniques and solutions.

Contents The topic will be announced before the start of the resp.semester.

Prerequisites none



LiteratureDepending on the topics varying from semester to semester, therelevant research literature will be announced before the start ofthe resp. semester.


ModuleMA-INF 1304

Seminar Geometric Distance Problems

Workload120 h


Modulecoordinator


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


Technical skills To Independently study problems at research level, based onresearch publications, to prepare a concise summary, topresent the summary in a scientific talk, to lead a criticaldiscussionwith other seminar participants.

Soft skillsContents Current topics in Computational Geometry.Prerequisites Recommended:

BA-INF 114 – Grundlagen der algorithmischen Geometrie


Exam achievements Oral presentation, written report (graded)Study achievements none (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 Required: At least 1 of the following:

MA-INF 1102 – Combinatorial OptimizationMA-INF 1202 – Chip Design





ModuleMA-INF 1306

Seminar Combinatorial and Geometric Optimization

Workload120 h


Modulecoordinator


Lecturer(s) Prof. Dr. Marek Karpinski, Prof. Dr. Norbert Blum,Prof. Dr. Rolf Klein


Technical skills Presentation of selected topics in the above areaSoft skills Ability to perform individual literature search, critical reading,

understanding, and clear didactic presentationContents Current topics in combinatorial and geometric optimization

based on latest research literaturePrerequisites none




ModuleMA-INF 1307

Seminar Advanced Algorithms

Workload120 h


Modulecoordinator


Lecturer(s) Prof. Dr. Marek Karpinski, Prof. Dr. Norbert Blum,Prof. Dr. Rolf Klein, Prof. Dr. Heiko Röglin


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 Required: At least 3 of the following:MA-INF 1102 – Combinatorial OptimizationMA-INF 1202 – Chip DesignMA-INF 1205 – Graduate Seminar Discrete OptimizationMA-INF 1208 – Applications of Cryptography





ModuleMA-INF 1309

Lab Efficient Algorithms for Selected Problems:Design, Analysis and Implementation

Workload270 h


Modulecoordinator


Lecturer(s) Prof. Dr. Marek Karpinski, Prof. Dr. Norbert Blum,Prof. Dr. Rolf Klein, Prof. Dr. Heiko Röglin


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




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 1313

Topics in Theoretical Cryptography

Workload270 h


Modulecoordinator




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 intheoretical cryptography, e.g.• elliptic curve cryptography, or• quantum cryptography



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


ModuleMA-INF 1314

Online Motion Planning

Workload270 h


Modulecoordinator


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), onlinealgorithms, competitive analysis, competitivecomplexity,functional optimization, shortest watchman route,tethered robots, marker algorithms, spiral search, approximationof 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


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


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 1317

Lab Parameterized Complexity

Workload270 h


Modulecoordinator




Technical skills The ability to turn theoretical results from parameterizedcomplexity into functioning code. Engineering, testing, andevaluation of the achieved performance.

Soft skills Managing projects in small teams over a longer period of time.Presentation and discussion of obtained results.

Contents Implementation of algorithms from parameterized complexity,i.e., both fixed-parameter tractable algorithms as well askernelization algorithms. Testing and engineering of theobtained code.Concrete topics are subject to change.

Prerequisites none




ModuleMA-INF 1318

Theoretical Aspects of Intruder Search

Workload180 h


Modulecoordinator

PD Dr. Elmar Langetepe

Lecturer(s) PD Dr. Elmar Langetepe


Technical skills To acquire fundamental knowledge on topics and methods intheoretical and algorithmic aspects of intruder search ingeometric and discrete environments;

Soft skillsContents Intruder/Evader search in geometric and discrete environments,

Fire-Fighter problem, Fire Control on graphs and in the plane,Man-and-Lion problem, Two-Guards problem, Search Games,Mobile and immobile hiders, Patrolling algorithms.

Prerequisites none


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


2 Graphics, Vision, Audio

MA-INF 2111 L2E2 6 CP Foundations of Graphics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34MA-INF 2113 L2E2 6 CP Foundations of Audio Signal Processing . . . . . . . . . . . . . . . . 35MA-INF 2201 L4E2 9 CP Computer Vision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36MA-INF 2202 L4E2 9 CP Computer Animation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37MA-INF 2203 L4E2 9 CP Selected Topics in Signal Processing . . . . . . . . . . . . . . . . . . . . 38MA-INF 2204 L2E2 6 CP Rendering Techniques I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39MA-INF 2205 L2E2 6 CP Geometry Processing I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40MA-INF 2206 Sem2 4 CP Seminar Vision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41MA-INF 2207 Sem2 4 CP Seminar Graphics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42MA-INF 2208 Sem2 4 CP Seminar Audio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43MA-INF 2209 L4E2 9 CP Advanced Topics in Computer Graphics I . . . . . . . . . . . . . . 44MA-INF 2210 Sem2 4 CP Seminar Computer Animation . . . . . . . . . . . . . . . . . . . . . . . . . 45MA-INF 2212 L2E2 6 CP Selected Topics in Signal Processing . . . . . . . . . . . . . . . . . . . . 46MA-INF 2213 L3E1 6 CP Computer Vision II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47MA-INF 2214 L2E2 6 CP Computational Photography . . . . . . . . . . . . . . . . . . . . . . . . . . . 48MA-INF 2215 Sem2 4 CP Seminar Digital Material Appearance . . . . . . . . . . . . . . . . . . 49MA-INF 2216 Lab4 9 CP Lab Visual Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50MA-INF 2301 L2E2 6 CP Advanced Topics in Computer Vision . . . . . . . . . . . . . . . . . . 51MA-INF 2302 L2E2 6 CP Physics-based Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52MA-INF 2304 L2E2 6 CP Rendering Techniques II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53MA-INF 2305 L2E2 6 CP Geometry Processing II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54MA-INF 2306 L2E2 6 CP Virtual Reality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55MA-INF 2307 Lab4 9 CP Lab Vision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56MA-INF 2308 Lab4 9 CP Lab Graphics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57MA-INF 2309 Lab4 9 CP Lab Audio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58MA-INF 2310 L4E2 9 CP Advanced Topics in Computer Graphics II . . . . . . . . . . . . . 59MA-INF 2311 Lab4 9 CP Lab Computer Animation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60MA-INF 2312 L3E1 6 CP Image Acquisition and Analysis in Neuroscience . . . . . . . . 61


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);Parametric curves and surfaces with applications to 3Dmodelling;Ordinary differential equations with applications to physicalbased modelling

Prerequisites Required: None of the following modules have been passed:MA-INF 2101 – Foundations of Graphics, Vision and Audio




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 of others, and working ingroups.

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

Prerequisites none


Exam achievements Oral 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. Juergen Gall

Lecturer(s) Prof. Dr. Juergen 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.

Prerequisites Recommended:Algorithms and data structures or knowledge of basic discretedifferential geometry, knowledge on multidimensional analysisund linear algebra as well as numerical analysis and numericallinear algebra, C++



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

Selected Topics in Signal 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 programmingskillsfor signal processing applications;Capability to analyze; Time management; Presentation skills;Discussing own solutions and solutions of others, and working ingroups.

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

Prerequisites Required: None of the following modules have been passed:MA-INF 2203 – Selected Topics in Signal Processing


Exam achievements Oral 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 andtheir applications to computer vision problems.


Contents The class will cover a number of learning methods andtheir applications in computer vision. For example, linearmethods for classification and regression, boosting, randomforests, neural networks, SVMs, prototype methods, nearestneighbors, Gaussian processes, Dirichlet processes, imageclassification, object detection, action recognition, poseestimation, face analysis.

Prerequisites Required:MA-INF 2201 – Computer Vision

FormatTeaching format Group size h/week Workload[h] CPLecture 60 3 45 T / 45 S 3Exercises 30 1 15 T / 75 S 3T = face-to-face teaching; S = independent study


Literature

• T. Hastie, R. Tibshirani, J. Friedman: The Elements ofStatistical Learning: Data Mining, Inference, and Prediction• C. Bishop: Pattern Recognition and Machine Learning


ModuleMA-INF 2214

Computational Photography

Workload180 h


Modulecoordinator

Prof. Dr. Matthias Hullin

Lecturer(s)


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 Students learn• 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 Topics:• 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


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


ModuleMA-INF 2215

Seminar Digital Material Appearance

Workload120 h


Modulecoordinator

Prof. Dr. Matthias Hullin

Lecturer(s)


Technical skills Ability to understand new research resultspresented in original scientific papers.

Soft skills Ability to present and to critically discussthese results in the framework of the correspondingarea.

Contents Current conference and journal papersPrerequisites none




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.

Soft skills Ability to properly present and defenddesign decisions, to prepare readable documentation of software;skills in constructively collaborating with others in small teamsover a longer period of time; ability to classify ones own resultsinto the state-of-the-art of the resp. area

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 2301

Advanced Topics in Computer Vision

Workload180 h


Modulecoordinator

NN

Lecturer(s)


Technical skills Advanced computer vision methodsSoft skills Productive work in small teams, development and realization of

individual approaches and solutions, critical reflection ofcompeting methods, discussion in groups.

Contents The class focuses on advanced topics in the fields of computervision and image processing. In particular, it will make studentsfamiliar with recent developments in computer vision research.

Prerequisites Recommended:MA-INF 2201 – Computer Vision



LiteratureLatest topic-related research articles and literature will beannounced in advance of the lecture.


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: all of the following: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

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



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

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



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.

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 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 ofgeometry processing, rendering, scientific visualization or humancomputer interaction, including test and documentation of theimplemented software/system.

Soft skills Ability to properly present and defend design decisions, topreparereadable documentation of software; skills in constructivelycollaborating with others in small teams over a longer period oftime; ability to classify ones own results into the state-of-the-artof the resp. area

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 ofaudio and music processing, including test and documentation ofthe implementedsoftware/system.

Soft skills Ability to properly present and defend design decisions, topreparereadable documentation of software; skills in constructivelycollaboratingwith others in small teams over a longer period of time; ability toclassify ones own results into the state-of-the-art of the resp.area.

ContentsPrerequisites none




ModuleMA-INF 2310

Advanced Topics in Computer Graphics II

Workload270 h


Modulecoordinator


Lecturer(s)

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

Technical skills On completion students should be able to• 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 applicationsincorporate basic image based algorithms into rendering applications• and should have acquired soft skills like analytical problemdescription, creativity, self-dependent solution of practical problems,presentation of solution strategies and implementations, self-dependentliterature research, collaboration abilities, self-management.

Soft skillsContents Topics among others will be:

This class is focussed 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, materialacquistion 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 ofcomputer animation, including test and documentation of theimplemented software/system.

Soft skills Ability to properly present and defend design decisions, topreparereadable documentation of software; skills in constructivelycollaboratingwith others in small teams over a longer period of time; ability toclassify ones own results into the state-of-the-art of the resp.area

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

Jun.-Prof. Dr. Thomas Schultz

Lecturer(s) Jun.-Prof. Dr. Thomas Schultz

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

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 60 3 45 T / 45 S 3Exercises 30 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


3 Information and Communication Management

MA-INF 3104 L2E2 6 CP Intelligent Analysis of Data Streams . . . . . . . . . . . . . . . . . . . 63MA-INF 3105 L2E2 6 CP Principles of Distributed Systems . . . . . . . . . . . . . . . . . . . . . . 64MA-INF 3106 L2E2 6 CP Privacy in Ubiquitous Computing . . . . . . . . . . . . . . . . . . . . . . 65MA-INF 3201 L2E2 6 CP Network Security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66MA-INF 3202 L2E2 6 CP Mobile Communication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67MA-INF 3203 L2E2 6 CP Intelligent Information Systems . . . . . . . . . . . . . . . . . . . . . . . . 68MA-INF 3207 L2E2 6 CP Advanced Logic Programming . . . . . . . . . . . . . . . . . . . . . . . . . 69MA-INF 3209 Sem2 4 CP Seminar Selected Topics in Communication

Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70MA-INF 3210 Sem2 4 CP Seminar Intelligent Information Systems . . . . . . . . . . . . . . . 71MA-INF 3213 L2E2 6 CP Advanced Topics in Information Systems . . . . . . . . . . . . . . . 72MA-INF 3214 Sem2 4 CP Seminar Selected Topics in Information Management . . . 73MA-INF 3215 Sem2 4 CP Seminar Selected Topics in Malware Analysis and

Computer/Network Security . . . . . . . . . . . . . . . . . . . . . . . . . . . 74MA-INF 3216 Sem2 4 CP Seminar Sensor Data Fusion . . . . . . . . . . . . . . . . . . . . . . . . . . . 75MA-INF 3218 Sem2 4 CP Seminar Model-Driven Software Engineering . . . . . . . . . . . 76MA-INF 3219 Lab4 9 CP Lab Model-Driven Software Engineering . . . . . . . . . . . . . . . 77MA-INF 3222 L4E2 9 CP eSecurity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78MA-INF 3228 L2E2 6 CP Foundations of Information Systems Security . . . . . . . . . . . 79MA-INF 3229 Lab4 9 CP Lab IT-Security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80MA-INF 3230 L2E2 6 CP Enterprise Information Systems . . . . . . . . . . . . . . . . . . . . . . . . 81MA-INF 3231 Sem2 4 CP Seminar Enterprise Information Systems . . . . . . . . . . . . . . . 82MA-INF 3232 Lab4 9 CP Lab Enterprise Information Systems . . . . . . . . . . . . . . . . . . . 83MA-INF 3233 L2E2 6 CP Advanced Sensor Data Fusion in Distributed Systems . . 84MA-INF 3234 Lab4 9 CP Lab Mobile Sensing Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . 85MA-INF 3235 L2E2 6 CP Usable Security and Privacy . . . . . . . . . . . . . . . . . . . . . . . . . . . 86MA-INF 3236 L2E2 6 CP IT Security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87MA-INF 3302 L2E2 6 CP Temporal Information Systems . . . . . . . . . . . . . . . . . . . . . . . . . 88MA-INF 3304 Lab4 9 CP Lab Communication and Communicating Devices . . . . . . 89MA-INF 3305 Lab4 9 CP Lab Information Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90MA-INF 3309 Lab4 9 CP Lab Malware Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91MA-INF 3310 L2E2 6 CP Introduction to Sensor Data Fusion - Methods and

Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92MA-INF 3311 L4E2 9 CP Topics in Applied Cryptography . . . . . . . . . . . . . . . . . . . . . . . 93MA-INF 3312 Lab4 9 CP Lab Sensor Data Fusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94MA-INF 3313 Lab4 9 CP Lab Intelligent Information Systems . . . . . . . . . . . . . . . . . . . . 95MA-INF 3314 L2E2 6 CP Advanced Topics in Information Systems Security . . . . . . 96MA-INF 3315 Sem2 4 CP Seminar Advanced Information Systems Security . . . . . . . 97MA-INF 3316 Lab4 9 CP Lab Techniques in Information Systems Security . . . . . . . 98MA-INF 3317 Sem2 4 CP Seminar Selected Topics in IT Security . . . . . . . . . . . . . . . . . 99MA-INF 3318 Sem2 4 CP Seminar Verification of Complex Systems . . . . . . . . . . . . . 100MA-INF 3319 Lab4 9 CP Lab Usable Security and Privacy . . . . . . . . . . . . . . . . . . . . . . 101MA-INF 3320 Lab4 9 CP Lab Security in Distributed Systems . . . . . . . . . . . . . . . . . . 102MA-INF 3321 Sem2 4 CP Seminar Usable Security and Privacy . . . . . . . . . . . . . . . . . 103


ModuleMA-INF 3104

Intelligent Analysis of Data Streams

Workload180 h


Modulecoordinator

PD Dr. Andreas Behrend

Lecturer(s) PD Dr. Andreas Behrend


Technical skillsSoft skillsContentsPrerequisites none




ModuleMA-INF 3105

Principles of Distributed Systems

Workload180 h


Modulecoordinator

Prof. Dr. Peter Martini

Lecturer(s) Dr. Markus Esch


Technical skills The students learn fundamental principles of distributedcomputer systems and learn to apply them in practice. Thisincludes architectures of distributed systems, key concepts likefault tolerance and consistency as well as important algorithmsfor synchronization, distributed mutual exclusion, election etc.Moreover concepts of structured and unstructured overlaynetworks as self-organization, overlay routing, modeling ofcomplex random networks etc. will be taught.

Soft skills Theoretical exercises are given in order to support in-depthunderstanding of the lecture topics. In the course of theseexercises students learn to present their results and discuss theirown and others’ solutions. In the course of practical assignmentsthat need to be solved in small teams the students learnteamwork, time management, targeted organization of practicalwork as well as presentation and discussion of their solutions.

Contents • Architectures of distributed systems• Physical clock synchronization and logical clocks• Distributed termination• Distributed mutual exclusion• Election in distributed systems• Fault tolerance of distributed systems• Consistency in distributed systems• Structured and unstructured overlays• Distributed hash tables• Modeling and characteristics of complex random networks• Overlay routing

Prerequisites Recommended:BA-INF 101 "Kommunikation in Verteilten Systemen", orBachelor-level knowledge of Data Communication and InternetTechnology



Literature

Scientific articles as mentioned on the lecture slidesTanenbaum, van Steen: Distributed Systems: Principles andParadigms (2nd Edition), Prentice Hall, 2007; Steinmetz, Wehrle(Eds.): Peer-to-Peer Systems and Applications, Springer, 2005;Barrat, Barthelemy, Vespignani, Dynamical Processes onComplex Networks, Cambridge University Press, 2008


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


Lecturer(s) Prof. Dr. Peter Martini, Dr. Jens Tölle


Technical skills The students learn fundamental concepts of network security.This includes risks and vulnerabilities of today’s computernetworks, concepts to increase the level of security in thesenetworks, and a real-life oriented introduction to encryptiontechniques, their applications 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 organisation ofpractical work and critical discussion of own and others’ results

Contents Threats and attack scenarios, organizational aspects, technicalaspects: securing networks using different firewall concepts, IDSand IPS (intrusion detection systems and intrusion preventionsystems), security protocols for different protocol layers,integrity protection: hash functions and their weaknesses,certificates, privacy protection, 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) and/or MA-INF 3105 – Principles of DistributedSystems



Literature

• 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 3203

Intelligent Information Systems

Workload180 h


Modulecoordinator

Prof. Dr. Rainer Manthey

Lecturer(s) Prof. Dr. Rainer Manthey


Technical skills Students master the principles of management of derived databoth theoretically and in practical systems development andapplication modeling. They are able to understand and classifythe state-of-the-art in research in deductive databases.

Soft skills Communicative skills (oral/written presentation, “defending“solutions), self-competence (time management, self-organisation,creativity), social skills (constructive discussion, sharing work insmall teams)

Contents Syntax and semantics of deductive rules (views); efficient queryprocessing in deductive DB; rule-based change management; ISdesign for rule-based applications

Prerequisites Recommended:Good knowledge of the foundations of SQL, predicate logic andset theory



Literature

• C. Zaniolo, S. Ceri et al.: Advanced Database Systems,Morgan Kaufmann, San Francisco/USA, 1997• E. Bertino, G. Zarri, B. Catania: Intelligent DatabaseSystems, Addison Wesley, 2001


ModuleMA-INF 3207

Advanced Logic Programming

Workload180 h


Modulecoordinator

Dr. Günter Kniesel

Lecturer(s) Dr. Günter Kniesel, Jun.-Prof. Dr. Janis Voigtländer


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 3210

Seminar Intelligent Information Systems

Workload120 h


Modulecoordinator




Technical skills Ability to acquire and evaluate advanced scientific literature;skills in didactic preparation as well as oral presentation ofcomplex matters and latest research results; ability to evaluateand discuss presentations of fellow students, and toconstructively deal with critical feedback of others

Soft skillsContents Varying selected topics in intelligent information systems based

on modern research literaturePrerequisites none





ModuleMA-INF 3213

Advanced Topics in Information Systems

Workload180 h


Modulecoordinator

Jun.-Prof. Dr. Alexander Markowetz

Lecturer(s) Jun.-Prof. Dr. Alexander Markowetz


Technical skills An in-depth understanding of the topic under investigation. Acommand of the concepts and terminologies, in order to discussand compare the various systems, algorithms and approaches.The ability to implement the presented systems and algorithms.The ability to dissect (i) the logic of arguments and (ii)experimental setups deployed by publications in this area.

Soft skills • Oral discussion and presentation in classes and tutorials.• Written presentation of exercise solutions.• Team collaboration in solving theoretical and practicalproblems.• Critical assessment of literature, systems, algorithms andapproaches.

Contents In depth coverage of a selected topic in Information Systems, inparticular focusing on recent system implementations and novelalgorithms. Example subjects may consist of: Web InformationSystems, Information Retrieval, Management of Spatial Data,Management of Stream Data, or Data Warehousing.

Prerequisites Required:A thorough understanding of Database Management Systems,such as laid out in the text book by Ramakrishnan and Gehrke.Solid skills in developing OO software. In depth knowledge ofAlgorithms, such as summarized by the introductory book ofCormen et al.



LiteratureRecent scientific publications, and selected chapters of advancedtextbooks.


ModuleMA-INF 3214

Seminar Selected Topics in InformationManagement

Workload120 h


Modulecoordinator

Jun.-Prof. Dr. Alexander Markowetz

Lecturer(s) Jun.-Prof. Dr. Alexander Markowetz


Technical skillsSoft skills Ability to present and to critically discuss these results in the

framework of the corresponding area.Contents Current conference and journal papers.Prerequisites none




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

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)Recommended:




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

Lecturer(s) Dr. Günter Kniesel


Technical skills On successful completion of this module, students should be able to:• 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 On successful completion of this module, students should haverefined their scientific writing and presentation skills andshould be able to:• 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 InhalteModel driven software development concepts, tools and methods.In particular:• 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 none (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

Lecturer(s) 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 levelofautomation and tool integration in software development. Studentswilllearn how MDSE concepts, tools an methods boost the development ofgeneral purpose and domain specific languages, leverage softwarequalityanalysis 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 none (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




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.


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 3228

Foundations of Information Systems Security

Workload180 h


Modulecoordinator

PD Dr. Adrian Spalka

Lecturer(s) PD Dr. Adrian Spalka


Technical skillsSoft skillsContents The security of networked information systems depends on four

factors: authenticity, availability, confidentiality and integrity.This lecture examines their theoretical foundations and practicalimplementation. Along the historical development, the emphasisis put on modern information systems, such as those in thecloud.

Prerequisites none


Exam achievements Written exam (graded)Study achievements Successful exercise participation (not graded)Forms of media Most content will be hand-written on the board with the

supplement of a few slides. There are no handouts.Literature A text-book on cryptography is advisable.


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 3230

Enterprise Information Systems

Workload180 h


Modulecoordinator

Prof. Dr. Sören Auer

Lecturer(s) Prof. Dr. Sören Auer


Technical skills Students acquire knowledge in the design, development and useof information systems in companies and organizations in generalbut also in online communities, and inter-enterprise value chains.

Soft skillsContents • Information systems in the enterprise, in particular Enterprise

Resource Planning (ERP), Customer Relationship Management(CRM), Supply Chain Management (SCM), data warehouse /business intelligence, e-commerce, geographic informationsystems.• technologies for the implementation of modern informationsystems and information system environments: in particular,service-oriented information system architectures, workflowmanagement (BPEL), semantic-based data integration, businessprocess management,• Information systems for the processing of Big Data inparticular transactions (OLTP) and analytical informationsystems (OLAP) for decision support. Data Warehousing andData Mining.

Prerequisites none




ModuleMA-INF 3231

Seminar Enterprise Information Systems

Workload120 h


Modulecoordinator




Technical skills Ability to understand new research results presented in originalscientific papers and technologies in the area of EnterpriseInformation Systems.


Contents Recent conference and journal papersTechnologies such as ERP, CRM, SCM, database and datawarehousing systems

Prerequisites none




ModuleMA-INF 3232

Lab Enterprise Information Systems

Workload270 h


Modulecoordinator




Technical skills The students will carry out a practical task (project) in thecontext of Enterprise Information Systems, 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 into thestate-of-the-art in the area of





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

FormatTeaching format Group size h/week Workload[h] CPLecture 60 2 30 T / 45 S 2.5Exercises 30 2 30 T / 75 S 3.5Die Übung wird als Blockübung absolviertT = face-to-face teaching; S = independent study



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 3302

Temporal Information Systems

Workload180 h


Modulecoordinator




Technical skillsSoft skills Communicative skills (oral/written presentation, “defending“

solutions), self-competence (time management, self-organisation,creativity), social skills (constructive discussion, sharing work insmall teams)





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.






ModuleMA-INF 3305

Lab Information Systems

Workload270 h


Modulecoordinator


Lecturer(s) Prof. Dr. Rainer Manthey, 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




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


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





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 3313

Lab Intelligent Information Systems

Workload270 h


Modulecoordinator




Technical skillsSoft skillsContentsPrerequisites none




ModuleMA-INF 3314

Advanced Topics in Information Systems Security

Workload180 h


Modulecoordinator




Technical skillsSoft skillsContents The content of the lecture focuses on state-of-the-art findings

and techniques, and on present threats and security problems.Current examples are: an axiomatic view of authentication withapplication to user-centric environments and key managementfor cloud-applications.

Prerequisites none


Exam achievements Oral exam (graded)Study achievements Successful exercise participation (not graded)Forms of media Most content will be hand-written on the board with the

supplement of a few slides. There are no handouts.Literature A text-book on cryptography is advisable.


ModuleMA-INF 3315

Seminar Advanced Information Systems Security

Workload120 h


Modulecoordinator










ModuleMA-INF 3316

Lab Techniques in Information Systems Security

Workload270 h


Modulecoordinator




Technical skills The students will carry out a practical task(project) in the context of xxxxxx, including test anddocumentation of the implementedsoftware/system.

Soft skills Ability to properly present and defenddesign decisions, to prepare readable documentation of software;skills in constructively collaborating with others in small teamsover a longer period of time; ability to classify ones own resultsinto the state-of-the-art of the resp. area





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










4 Intelligent Systems

MA-INF 4111 L2E2 6 CP Intelligent Learning and Analysis Systems: MachineLearning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

MA-INF 4112 L2E2 6 CP Intelligent Learning and Analysis Systems: Data Miningand Knowledge Discovery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

MA-INF 4113 L2E2 6 CP Cognitive Robotics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107MA-INF 4114 L2E2 6 CP Robot Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108MA-INF 4201 L2E2 6 CP Artificial Life . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109MA-INF 4203 L2E2 6 CP Autonomous Mobile Systems . . . . . . . . . . . . . . . . . . . . . . . . . . 110MA-INF 4204 L2E2 6 CP Technical Neural Nets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111MA-INF 4206 L2E2 6 CP Selected Topics in Sensor Data Interpretation . . . . . . . . . 112MA-INF 4207 L2E2 6 CP Dynamically Reconfigurable Systems . . . . . . . . . . . . . . . . . . 113MA-INF 4208 Sem2 4 CP Seminar Vision Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114MA-INF 4209 Sem2 4 CP Seminar Principles of Data Mining and Learning

Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115MA-INF 4210 Sem2 4 CP Seminar Advanced Topics in Technical Informatics . . . . 116MA-INF 4211 Sem2 4 CP Seminar Cognitive Robotics . . . . . . . . . . . . . . . . . . . . . . . . . . . 117MA-INF 4212 L2E2 6 CP Data Science and Big Data . . . . . . . . . . . . . . . . . . . . . . . . . . . 118MA-INF 4213 Sem2 4 CP Seminar Humanoid Robots . . . . . . . . . . . . . . . . . . . . . . . . . . . 119MA-INF 4214 Lab4 9 CP Lab Humanoid Robots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120MA-INF 4215 L2E2 6 CP Humanoid Robotics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121MA-INF 4216 L2E2 6 CP Data Mining and Machine Learning Methods in

Bioinformatics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122MA-INF 4217 Sem2 4 CP Seminar Machine Learning Methods in Systems

Biology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123MA-INF 4218 Lab4 9 CP Lab Modeling and Simulation . . . . . . . . . . . . . . . . . . . . . . . . . 124MA-INF 4302 L2E2 6 CP Advanced Learning Systems . . . . . . . . . . . . . . . . . . . . . . . . . . 125MA-INF 4303 L2E2 6 CP Learning from Non-Standard Data . . . . . . . . . . . . . . . . . . . . 126MA-INF 4304 Lab4 9 CP Lab Cognitive Robotics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127MA-INF 4306 Lab4 9 CP Lab Development and Application of Data Mining and

Learning Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128MA-INF 4307 Lab4 9 CP Lab Field Programmable Gate Arrays . . . . . . . . . . . . . . . . 129MA-INF 4308 Lab4 9 CP Lab Vision Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130MA-INF 4309 Lab4 9 CP Lab Sensor Data Interpretation . . . . . . . . . . . . . . . . . . . . . . . 131MA-INF 4310 Lab4 9 CP Lab Mobile Robots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132MA-INF 4311 Sem2 4 CP Seminar Advanced Topics in Data Analysis . . . . . . . . . . . 133MA-INF 4312 L2E2 6 CP Semantic Data Web Technologies . . . . . . . . . . . . . . . . . . . . . 134MA-INF 4313 Sem2 4 CP Seminar Semantic Data Web Technologies . . . . . . . . . . . . 135MA-INF 4314 Lab4 9 CP Lab Semantic Data Web Technologies . . . . . . . . . . . . . . . . . 136MA-INF 4315 L4E2 9 CP Probabilistic Graphical Models . . . . . . . . . . . . . . . . . . . . . . . 137


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 Recommended:Prior knowledge of probability theory, linear algebra, artificialintelligence, information systems and data basesRequired: None of the following modules have been passed:MA-INF 4102 – Intelligent Learning and Analysis Systems


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 Recommended:Prior knowledge of probability theory, linear algebra, artificialintelligence, information systems and data basesRequired: None of the following modules have been passed:MA-INF 4102 – Intelligent Learning and Analysis Systems


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: None of the following modules have been passed:MA-INF 4101 – Theory of Sensorimotor Systems



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 a general robot learningapproach to get closer towards human-like performance inrobotics, such as reinforcement learning, learning models forcontrol, learning motor primitives, learning from demonstrationsand 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 4206

Selected Topics in Sensor Data Interpretation

Workload180 h


Modulecoordinator

PD Dr. Volker Steinhage

Lecturer(s) PD Dr. Volker Steinhage


Technical skills Understanding of important paradigms and methods of sensordata interpretation and ability to implement systems forinterpreting sensor data

Soft skills • Ability to cooparate in small groups on solving given tasks• Ability to put a conceptual solution and its implementiondown on paper• Ability to present and discuss a conceptual solution and itsimplemention in an oral presentation

Contents Approaches to feature extraction and classification of sensordata with applications in scene analysis, object detection andobject tracking

Prerequisites Required: all of the following:MA-INF 2201 – Computer VisionBA-INF 131 – Intelligente SehsystemeModule MA-INF 4206 "Selected Topics in Sensor DataInterpretation" requires knowledge and skills in the foundationsof compuer vision like given in the Bachelor module BA-INF 131"Intelligente Sehsysteme" or in Master module MA-INF 2201"Computer Vision"



Literature

• Simon J.D. Prince: Computer Vision: Models, Learning, andInference. Cambridge University Press, 2012.• Richard Szeliski: Computer Vision: Algorithms andApplications. Springer, 2010.• Selected up-to-date publications.


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 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 and accept criticism, criticalexamination 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 none (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 none (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 and accept criticism, criticalexamination 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

Classification Programme Mode SemesterM. Sc. Computer Science Optional 3. or 4.

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, clear didacticpresentation of techniques and experimental results, scientificdiscussion, structured writing of summary), social skills (abilitytoformulate 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, abilitytoanalyze problems theoretically and to find practical solutions),communication skills (collaboration in small teams, oral andwrittenpresentation of solutions, critical examination ofimplementations).

Contents Robot middleware (ROS), perception, state estimation,environmentrepresentations, navigation, and motion planning for humanoidrobots.

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, motion planning, grasping, and humanmotion analysis.

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 environmentrepresentation,self-localization with particle filters and improved proposals,footstep planning, whole-body motion planning with rapidlyexploringrandom trees, grasping, active perception, human motionanalysis,activity recognition, statistical testing, paper writing.

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 - Introduction: Data Mining and Machine Learning inBioinformatics- Introduction to Statistics: hypothesis tests, (generalized) linearmodels, Bayesian inference- Clustering algorithms- Hidden Markov Models- Support Vector MachinesFor all algorithms the specific context in bioinformatics isdiscussed (e.g. -omics data and sequence analysis)

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 SystemsBiology

Workload120 h


Modulecoordinator

Dr. Holger Fröhlich

Lecturer(s) Dr. Holger Fröhlich


Technical skills - understanding and knowledge of current concepts in systemsbiology- understanding and knowledge of involved computationalmethods, specifically from the field of Machine Learning

Soft skills - communication: oral scientific presentation of a defined topic- self-competences: ability to read, understand and analyzescientific publications- social skills: ability to discuss a scientific topic with otherstudents and the staff

Contents Conference and journal papers covering the areas:- Introduction to Systems Biology- Overview about different modeling concepts and philosophies- Machine Learning based approaches: Gaussian GraphicalModels, Dynamic Bayesian Networks, methods for heterogenousdata integration

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


Exam achievements Oral presentation, written report (graded)Study achievements none (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 SystemsBiology


Exam achievements Oral presentation, written report (graded)Study achievements none (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 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 small group, they analyze a problem, realize astate-of-the-art solution, 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 none (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 none (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 none (not graded)Forms of mediaLiterature Relevant literature will be anounced 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 none (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 4311

Seminar Advanced Topics in Data Analysis

Workload120 h


Modulecoordinator


Lecturer(s)








ModuleMA-INF 4312

Semantic Data Web Technologies

Workload180 h


Modulecoordinator


Lecturer(s) Prof. Dr. Sören Auer, Dr. Christoph Lange


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) Prof. Dr. Christoph Lange


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. Sören Auer, Dr. Christoph Lange


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 4315

Probabilistic Graphical Models

Workload270 h


Modulecoordinator

Jun.-Prof. Dr. Angela Yao

Lecturer(s)


Technical skills Students will be introduced to the theory of probabilisticgraphical models and study various applications of such modelsin image processing, computer vision and other topics in AI.


Contents This course introduces probabilistic graphical models and theiruse in solving problems in computer vision and machinelearning. Graphical models offer a probabilistic framework formodelling and making decisions in complex scenarios withlimited and noisy data. We will cover topics such as Markov andBayesian networks, parameter learning, and inferencetechniques. The theory will be demonstrated in computer visionapplications such as human pose estimation, object tracking,image de-noising and semantic segmentation.

Prerequisites Recommended:No prior knowledge of statistics is required to follow the course.Exercises will be both theory and programming (Matlab /Python) based.




5 Master Thesis

MA-INF 0401 30 CP Master Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139MA-INF 0402 Sem2 2 CP Master Seminar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140


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

900 S 30

T = face-to-face teaching; S = independent studyExam achievements Master Thesis (graded)Study achievements none (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 none (not graded)Forms of media

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

module handbook for the master's program

Documents