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* Electives can be selected from the Computer Science for Digital Media course and seminar offerings, advanced English courses and other courses from the Bauhaus-Universität Weimar.

The Module index and descriptions of the Computer Science for Digital Media Master's program (since summer semester 2017) can be fund here.

Module Index Master PV 11 (from WS 2011/2012)

To Module Index PV 29 (from WS 2009/2010 till SS 2011)
To Module Index PV 25 (from WS 2005/2006 till SS 2009)

Course Modeling Distributed & Secure IS Intelligent IS Interactive IS WS SS
Advanced Analysis        
Advanced HCI: Theory and Methods        
Advanced HCI: Ubiquitous Computing      
Advanced Numerical Mathematics        
Cognitive Systems        
Computer Graphics II: Computer Animation        
Computer Graphics II: Fundamentals of Imaging      
Cryptographic Hash Functions        
Digital Watermarking & Steganography      
Discrete Optimization        
Dynamical Systems        
Geographic Information Systems          
Image Analysis and Object Recognition        
Introduction to Machine Learning        
Mobile Information Systems        
Number Theory        
Online Computation        
Photogrammetric Computer Vision          
Randomized Algorithms        
Real-Time Rendering        
Search Algorithms      
Secure Channels        
Software Development for Safe and Secure Systems      
Usability Engineering        
Virtual Reality        
Web Search and Information Retrieval      

Module Index Master

Modeling

Module codeMO
Instructors in chargeGürlebeck / Rodehorst
LevelElective module, 1st and 2nd semester (Master)
Degree programMaster Computer Science and Media
Workload9 ECTS credits
Teaching SchemeAs courses, each complete with lectures and tutorials on selected theoretical and practical issues, prototypical implementations, and an evaluation of algorithms and applications. Most courses within this module comprise of 2 SWS for lectures + 1 SWS for tutorials (equivalent to 4.5 ECTS credits).
Duration2 semesters
CycleEach semester
PrerequisitesBachelor’s degree
Educational Objectives / CompetencesThis module provides solid foundations of the methodological, mathematical, and algorithmic concepts to develop hardware and software systems in the field of digital media. Since a large part of these models is of a mathematical nature, numerical model formation, solution and optimization methods can be chosen as a special focus. Students will work mostly as small teams (2-3 students) for all tutorial or lab-related activities.
CoursesCourses and seminars cover the numerical solution of ordinary and partial differential equation systems, error and stability analyses, the analysis of measured data and digital signals such as image analysis, but also optimization and conceptual modeling topics. The following courses are part of the modeling module: For the module, students choose courses worth altogether 9 ECTS credits.
GradingThe combined grade for the module is calculated as the mean of the grades obtained in the component courses, weighted by the courses’ ECTS credits.

Distributed & Secure Information Systems

Module codeDSIS
Instructors in chargeLucks/N.N.
LevelElective module, 1st and 2nd semester (Master)
Degree programMaster Computer Science and Media
Workload9 ECTS credits
Teaching SchemeEither as courses or as reading classes. Topics are selected theoretical and practical issues, prototypical implementations, and an evaluation of algorithms, protocols, and applications. A typical course comprises of 2 SWS for lectures + 1 SWS for labs (equivalent to 4.5 ECTS credits), a typical reading class comprises of 2 SWS for a seminar + 1 SWS for a tutorial (also 4.5 ECTS credits).
Duration2 semesters
CycleBi-Annually, beginning either in winter or in summer semester
PrerequisitesBachelor’s degree
Educational Objectives / CompetencesThis module aims at teaching theoretical knowledge and practical skills in understanding, handling and – especially – developing distributed information systems and in understanding secure systems, especially in training the ability to discover possible solutions' security flaws.
In the style of the project centered studies, the teaching staff puts its focus on group work for solving larger exercises and working on assignments.
CoursesThe courses within this module cover implementation concepts, distributed database management systems, techniques and technologies of the ubiquitous computing, software design principles for mobile information systems and for safe and secure systems, and security issues related to distributed environments and insecure communication channels. 
Typical topics for courses and reading classes are:  
GradingBased on the mean of the grades obtained in the component courses of the module, weighted by the courses’ ECTS credits.

Interactive Information Systems

Module codeRIS
Instructors in chargeFröhlich/Wüthrich
Level1st and 2nd semester (Master)
Degree programMaster Computer Science and Media
Workload9 ECTS credits
Teaching SchemeAs courses, each complete with lectures and tutorials on selected theoretical and practical issues, prototypical implementations, and an evaluation of algorithms and applications. Most courses within this module comprise of 2 SWS for lectures + 1 SWS for tutorials (equivalent to 4.5 ECTS credits).
Duration2 semesters 
CycleAnnually, starts in winter semester
PrerequisitesBachelor's degree
Educational Objectives / CompetencesStudents learn the theoretical and practical foundation of interactive systems. They are capable of designing and implementing advanced graphical and vision-based hard- and software systems as well as intelligent and ergonomic user interfaces. The lab classes provide hands-on experiences with such systems and require the implementation of short projects in small teams.
CoursesThe lectures of this module focus on advanced topics in the area of real-time interactive systems.
The following lectures are offered for this module: A total of two lectures can be chosen from the course index of the current semester.
GradingThe combined grade for the module is calculated as the mean of the grades obtained in the component courses, weighted by the courses’ ECTS credits.

Intelligent Information Systems

Module codeIIS
Instructors in chargeStein/Bertel
LevelElective module, 1st and 2nd semester (Master)
Degree programMaster Computer Science and Media
Workload9 ECTS credits
Teaching SchemeAs courses, each complete with lectures and tutorials on selected theoretical and practical issues, prototypical implementations, and an evaluation of algorithms and applications. Most courses within this module comprise of 2 SWS for lectures + 1 SWS for tutorials (equivalent to 4.5 ECTS credits).
Duration2 semesters
CycleEach semester
PrerequisitesBachelor’s degree 
Educational Objectives / CompetencesThis module provides the chance to acquire the relevant theoretical knowledge and practical, hands-on abilities for successfully using, evaluating, and developing various types of intelligent information systems. Students will work mostly as small teams (2-3 students) for all tutorial or lab-related activities.
CoursesCourses and seminars cover conceptual, logical, algorithmic, statistical, and methodological foundations of information processing to tackle tasks whose solutions require “intelligent” problem solving behavior of various kinds. Selected building blocks of intelligent information systems are developed and analyzed within tutorials or labs.
The following courses are part of the IIS module: For the module, students choose courses worth altogether 9 ECTS credits.
GradingThe combined grade for the module is calculated as the mean of the grades obtained in the component courses, weighted by the courses’ ECTS credits.

Electives

Module code MSC-W
Instructors in charge Chair of curricula committee
Level 2. and 3. semester
Degree program Master of Computer Science and Media
Workload 24 ECTS credits
Teaching Scheme Lecture, seminar
Duration 2 semesters
Cycle each semester
Prerequisites BSc in a related study field
Educational Objectives / Competences Deepen or broaden the knowledge in the different disciplines offered at the Faculty of Media: computer science and media, humanities related to media, media management or media arts and design. Provided the approval of the curricula committee, students can choose also courses from other faculties.
Content According to selected courses.
Grading The combined grade for the module is calculated as the mean of the grades obtained in the component courses, weighted by the courses’ ECTS credits. None of the selected courses can be reused in a different module.

Research Project I/II

Module code MSC-P
Instructors in charge Respective professorship(s)
Degree program Master students of Computer Science and Media. Open to other master students of collaborating professorships.
Workload 15 ECTS credits
Teaching Scheme Supervised project work in small groups
Duration 1 semester
Cycle each Semester
Prerequisites BSc in a related study field
Educational Objectives / Competences Within the project, students work on research topics in close collaboration with the supervising professors and their research assistants. In many cases, the projects focus on the design, implementation and evaluation of complex software systems with a particular emphasis on team work. Independent literature research based on current publications and presentations on the various aspects and milestones of the project enables the participants to refine their presentation skills. An evaluation and documentation of the results in the form of a scientific publication completes the project.In contrast to projects offered for the bachelor programme the emphasis on research is significantly stronger, considering the complexity of the subjects covered as well as to the quality of the work (seminar, presentation, software) and the degree of autonomy required.
Content Depends on the individual topic
Grading Specific criteria for evaluation will be announced at the beginning of the individual project. Quality of the presentation, results achieved, autonomy in work and creativity are important factors.

Master Module

Module code MSC-T
Instructors in charge Respective professorship
Degree program Master Computer Science and Media
Workload 30 ECTS credits
Teaching Scheme Largely independent research with regular intermediate reporting and consulting with the supervisor.
Duration 5 months
Cycle each Semester
Prerequisites BSc in a related study field
Educational Objectives / Competences In the thesis, the students prove their ability to perform independent scientific work on an adequately challenging topic. They are given the opportunity to develop, refine and realize their own ideas.
Content Depends on the specific topic
Grading The final thesis is the most important part of the module and describes the results as well as the path that led to these results. The thesis should be written in the style of a scientific publication, whereby the students own contribution to the results should be clearly evident. The evaluation of the thesis comprises a grade for the written thesis (weight 75%) and a combined grade for the presentation and the related defence (weight 25%).

Course Index Master

Advanced Analysis

Lecturer Gürlebeck
Modules Modeling
ECTS / SWS 4.5 ECTS/2+1 SWS (Lecture+Lab)
Teaching concept The lecture introduces concepts, algorithms, and theoretical background of the theory of Partial Differential Equations. The accompanying exercise classes are concerned with theoretical as well as applied tasks to deepen the understanding of the field.
Cycle Alternating with other courses of Prof. Gürlebeck.
Requirement BSc in a relevant study field
Objective Many real-world problems lead to mathematical models in the form of partial differential equations. These models can be transformed into numerical models and used for physically correct simulations, optimisations or parameter identifications. Students will be provided with the necessary tools to model and solve linear problems.
The course will deal with and understand the following topics:
• basics of ordinary differential equations
• classification of partial differential equations
• partial differential equations and coordinate transforms
• solution of partial differential equation in unbounded domains, initial value problems
• solutions to boundary value problems in bounded domains by series expansions
• error estimates
• integral representation formulas
• concrete models and their simulations.
Students should be able to apply the above tools and the theory to solve concrete problems. Furthermore, they should be able to create computer simulations with computer algebra systems.
Students should be able to understand
• the idea of mathematical modelling
• the mathematical assumptions and the resulting restrictions
• how to evaluate and check the correctness of a model or of a solution
in order to solve problems from mathematical physics, mechanics and image processing and create accurate simulations. They should be able to identify a suitable mathematical model and to adapt it to the given situation if necessary.
Students should understand
Content • Classification of Partial Differential Equations
• Coordinate Transforms, Canonical Forms
• Analytical Solution Methods
• Modelling of Real-World Problems
Grading Written exam.
Literature  
  • Folland: Introduction to PDE`s
  • Taylor: PDE I / Basic Theory
  • Taylor: PDE II / Qualitative Studies of Linear Equations
  • Zill/Cullen: Advanced Engineering Math.
  • Burg/Haf/Wille: Höhere Mathematik für Ingenieure
Comments -

Advanced HCI: Theory and Methods

Lecturer Hornecker
ECTS / SWS 4.5 ECTS / 2+1 SWS (Lecture+Lab)
Teaching concept The course consists of lectures and accompanying lab sessions. Assignments consist of group hand-ins and individual assignments. 
Frequency Annual
Requirement BSc in a relevant study field
Content

The course will explore advanced topics in HCI, providing an overview of the different perspectives within and the interdisciplinary nature of this area. It will introduce students to the different types of research methods commonly used within HCI research, ranging from quantitative experimental studies to qualitative research methods and mixed method strategies, and present case studies as examples illustrating the use of these methods. You will gain practical experience in utilizing a selection of these methods through practical assignments and mini-projects, and will work with the research literature.

The course will furthermore provide an overview of how the role of theory in HCI has expanded from the early days of human factors and mathematical modelling of behaviour to include explanatory and generative theories, which reflect influences from fields such as design, sociology, and ethnography.

Successful students should be able to

  • appreciate the diversity of research methods and relate them to research paradigms and theory
  • select research methods appropriate to the domain and research question, based on an understanding of the characteristics, strengths and weaknesses, and practical demands of methods 
  • utilize a range of HCI research methods and approaches to investigate a research question 
  • design, plan, and organize experimental (comparative) user studies and interpret the data
  • report and present user studies and findings properly
  • relate the role of theory in HCI to the expanding range of methodical approaches utilized for HCI research
Grading Via practical assignments, individual and in group work.
Literature

Main texts (in excerpts):

  • Lazar et al. Research Methods in Human-Computer Interaction
  • Cairns & Cox. Research Methods for Human-Computer Interaction. Cambridge Univ Press
  • Yvonne Rogers, HCI Theory. Morgan & Claypool

 Additional readings:

  • Andy Field and Graham Hole. How to Design and Report Experiments. Sage
  • Uwe Flick. Introduction to Qualitative research. Sage 2014
Comments -

Advanced HCI: Ubiquitous Computing

Code UBI
Lecturer NN
Modules Distributed and Secure Systems, Interactive Information Systems, Electives
ECTS / SWS 4.5ECTS, V2/Ü1 SWS
Teaching concept The course consists of lectures and accompanying lab classes. Team work (2 students per team) during lab classes is recommended.
Cycle Bi-yearly.
Requirement BSc in a relevant study field
Objective The aim of the course will be to explore the theoretical, applied and technical foundations of modern pervasive and ubiquitous systems. The focus in on user interfaces and usability of such systems. The accompanying lab classes allow students to implement various technology demonstrators and a final project of their own choice.
Content The course focuses on the following topics:
  • History of ubicomp systems
  • Sensing, tracking and monitoring technology
  • Wearable technology
  • Mobile projection
  • Modern user interfaces for ubicomp systems
  • Evaluation techniques
Grading Oral exam. Admission to the exam requires the successful completion of the lab classes and the completion of a one-week project.
Literature Ubiquitous Computing Fundamentals. Ed. John Krumm. ISBN: 1420093606. Chapman & Hall/CRC 2009.
Comments -

Advanced Numerical Mathematics

Lecturer Gürlebeck
Modules Modeling, Electives
ECTS / SWS 4.5 ECTS/2+1 SWS (Lecture+Lab)
Teaching concept The lecture introduces concepts, algorithms, and theoretical background for the solution of Partial Differential Equations. The accompanying exercise classes are concerned with theoretical as well as applied tasks to deepen the understanding of the field. This will be completed by practical exercises in the computer lab.
Cycle Alternating with other courses of Prof. Gürlebeck
Requirement BSc in a relevant study field
Content The students learn to derive a numerical model from an analytical model. They understand the meaning of systematic and numerical errors and learn to develop efficient algorithms.
  • Numerical linear algebra
  • iterative solution of linear and non-linear systems of algebraic equations
  • numerical solution of ordinary and partial differential equations
  • finite difference methods
  • stability.
Examination Oral exam. Admission to the exam requires the successful completion of homework on the computer.
Literature  
  • Varga. Matrix iterative analysis.
  • Hermann. Numerische Mathematik
  • Kress. Numerical Analysis
Comments -

Cognitive Systems

Lecturer Schultheis/Dylla
Modules Modeling, Electives
ECTS / SWS 4.5 ECTS/2+1 SWS (Lecture+Lab)
Teaching concept This course will provide a systematic introduction into the interdisciplinary field of natural and artificial cognitive systems. It will present the relevant computational and psychological concepts, theories, methods, and terminology. Lectures will be complemented by labs, in which participants will work in teams to address selected practical and theoretical aspects in more depth.
Cycle Every second winter semester
Requirement Bachelor’s degree in a relevant field of study
Content

Participants will learn about selected natural and artificial cognitive systems and predominant theories, models, and concepts. Diverse cognitive architectures and approaches to cognitive modeling will be studied, in part practically and through cases. Applications to human-computer interaction, intelligent user interfaces, (multi-media) information design, and other areas will be discussed.

Selected topics: 

  • Cognitive architecture
  • Parameter estimation
  • Model evaluation
  • Cognitive models for HCI
  • Cognitive abilities
  • Physiological foundations & perception
  • Information processing
  • Connectionism.
Grading Written or oral exams. Points obtained by successful completion of the labs will be counted towards the final grade. Admission to exams requires a successful completion of the labs.
Literature To be announced on the course’s website before the start of the semester.
Comments -

Computer Graphics II: Fundamentals of Imaging

Lecturer Wuethrich
Modules Intelligent IS,Interactive IS, Elective 
ECTS / SWS 4.5 ECTS/2+1 SWS(Lecture + Lab)
Teaching concept The lecture introduces advanced topics and methods for analyzing the quality of pictures and of animated sequences. Besides traditional lectures, students are given programming and close to practice exercise tasks to complete in order to get hands on experience on the lecture themes.
Cycle Yearly.
Requirement B.Sc.
Content In Computer Graphics, and also in Image processing and in Design, professionals are used to speak about "better" or "worse" quality for pictures. Contrary to popular belief, however, there is no general method for analyzing the quality of picture. The course will start with a wide introduction to light transport and reflection theory, continue with a trip through digital and analogue image capture and reproduction and a survey of image compression methods. In its last part the course will focus on methods for evaluating the quality of pictures and of animated sequences, revealing advantages and disadvantages of different display and printing techniques and of the different compression methods.
Examination Examination on the lecture content will be either oral or written. Admission to the exam requires the successful completion of the lab classes.
Literature
  • Zhou, W and Bovik, A.C., “Image Quality Assessment”
  • Hsien-Che Lee, “Fundamentals of Color Imaging”
Comments -

Computer Graphics II: Computer Animation

Lecturer Wuethrich
Modules Interactive IS, Elective 
ECTS / SWS 4.5 ECTS/2+1 SWS (Lecture + Lab)
Teaching concept The lecture introduces advanced topics and methods for Computer Animation. Besides traditional lectures, students are given programming and close to practice exercise tasks to complete in order to get hands on experience on the lecture themes.
Cycle Yearly.
Requirement B.Sc.
Content The course will teach the secrets behind movement in 2D and 3D rendering. Among the course themes there are: double buffering, 3D coordinate systems, quaternions, interpolation techniques, kinematics, inverse kinematics, dynamics, hierarchical skeleton techniques. Physics based simulations, movement control, real time issues in animation for gaming and real time environments. Part of the course requirements is the development of an animation by the participants.
Examination Examination on the lecture content will be either oral or written. Admission to the exam requires the successful completion of the lab classes.
Literature
  • Rick Parent. Computer Animation - Algorithms and Techniques, Morgan Kaufmann, 2002, ISBN 1558605797
Comments -

Cryptographic Hash Functions

Lecturer Lucks
Modules Distributed & Secure Information Systems, Electives
ECTS / SWS 4.5 ECTS/3 SWS (Lecture)
Teaching concept Either Course with Lab or Reading Class with Tutorial
Cycle At least every 2nd year
Requirement Bachelor's degree, fundamental knowledge about cryptography and discrete mathematics
Content The first part of this course introduces cryptographic hash functions and their application in practice. The second part is about cryptanalysis of cryptographic hash functions. Finally, the last chapter is about the ongoing SHA-3 competition.
Grading Oral exam
Literature -
Comments -

Digital Watermarking & Steganography

Lecturer Jakoby
Modules Distributed and Secure IS, Modeling, Electives
ECTS / SWS 4.5 ECTS / 2 SWS lectures + 1 SWS labs
Teaching concept The course consists of lectures and accompanying lab classes.
Cycle Winter term
Requirement BSc in a relevant study field
Objective In this lecture we will introduce some basic concepts, methods and applications of digital watermarking and steganography.
Content Digital watermarking is the practice of hiding a message about an image, audio clip, video clip, or other work of media within that work itself. One goal of the used methods is to ensure that the message cannot be removed after it is embedded in the media. Thus, systems can use such a message to provide additional information of the content of the media itself, e.g. copyrights. Digital watermarks have to be readable or detectable, but they should be hard to remove from the content.
In steganography we investigate systems where the embedded information is completely hidden for unauthorized parties. Even the fact that a media file contains a hidden message should be hidden. Thus, by using such a systems two parties can communicated in such a way that a third party cannot detect the communication.
Grading Oral examination
Literature Ingemar Cox, Matthew Miller, Jeffrey Bloom, Jessica Fridrich, Ton Kalker
Digital Watermarking and Steganography (The Morgan Kaufmann Series in Multimedia Information and Systems)
Comments -

Software Development for Safe and Secure Systems

Code DSIS, MA-III
Lecturer Prof. Lucks
Modules Distributed & Secure Information Systems, Modeling, Electives
ECTS / SWS 4.5 ECTS, 3 SWS (2 SWS lecture, 1 SWS lab)
Teaching concept Course with Lab
Cycle At least every 2nd year
Requirement Bachelor's degree, sound knowledge about programming
Objective - getting an overview over security issues - learning concepts for developing medium and high assurance systems
Content - introduction into a programming language for safe and secure systems- black-box and white-box testing methods- the theory of program verification- practical program verification
Grading Oral exam
Literature will be given in advance of the semester at the website
Comments none

Geographic Information Systems

Code GIS
Lecturer Rodehorst
Modules Electives
ECTS / SWS 6 ECTS / 2+2 SWS ( Lecture+Exercises)
Teaching concept The course consists of lectures and accompanying exercises. Team work (3 students per team) for the exercises is recommended.
Cycle
Requirement
Objective The lecture covers basics of spatial information systems, such as acquisition, organization, analysis and presentation of spatial data. The exercises and an individual project gain a deeper understanding of GIS workflows, tools and extensions and should turn the knowledge into practice.
Content The course topics include
  • Acquisition of spatial data and public resources
  • Reference systems and map projections
  • (Geo-)databases and efficient data structures
  • Geometrical and topological data analysis
  • Cartographic generalization and visualization
Grading Written exam. Admission to the exam requires the successful completion of the exercises.
Literature
  • R. Bill: Grundlagen der Geo-Informationssysteme, 5. Ed., Wichmann, 2010.
  • N. Bartelme: Geoinformatik – Modelle, Strukturen, Funktionen, 4. Ed., Springer, 2005.
  • N. de Lange: Geoinformatik in Theorie und Praxis, 2. Ed., Springer, 2006.
Comments -

Discrete Optimization

Code DisOpt
Lecturer Schmiedel
Modules Modeling
ECTS / SWS 4ECTS, V2/Ü2 SWS
Teaching concept The lecture introduces concepts, algorithms, and theoretical backgrounds.
Cycle
Requirement
Objective Discrete or combinatorial optimization is an area at the interface of mathematics and computer science. Applications for such optimization problems can be found in many different fields. The course provides a survey about different approaches to the discrete optimization.
Content We consider both discrete optimization problems, for which efficient algorithms exist (minimal spanning tree, shortest paths problems, flow problems ...), as well as NP-complete problems. For the latter, both exact methods (greedy algorithms on matroids, branch-and-bound algorithm ...) as well as heuristics and metaheuristics to find approximate solutions are treated.
Grading Written exam.
Literature
  • Korte, Vygen: Combinatorial Optimization
  • Schrijver: Theory of linear and integer programming
  • Dück: Diskrete Optimierung
Comments -

Dynamical Systems

Code Dynamic
Lecturer Gürlebeck
Modules Modeling
ECTS / SWS 4ECTS, V2/Ü2 SWS
Teaching concept The lecture introduces the theory, concepts, and strategies The accompanying exercise classes are concerned with theoretical as well as applied tasks to deepen the understanding of the field.
Cycle Alternating with other courses of Prof. Gürlebeck
Requirement BSc in a relevant study field
Objective The first part of this lecture introduces the notions and methods of the theory of dynamical systems. In the second part certain problems of continuous and discrete dynamical systems are discussed. The knowledge from the first two parts will be deepened by a practical third part where the students work on an individual project.
Content
  • Transfer of basic knowledge on modelling. The models have to beinterpreted and classified. This is the basis for a well-adapted choice of the numerical or analytical model.
  • Ordinary differential equations and continuous dynamical systems.
  • Equivalence, flow, orbits, invariant sets, stability, fixed points.
  • Applications in mechanics, traffic systems, electrical engineering and ecology.
  • Discrete dynamical systems and finite difference equations; stability, fixed points, periodical solutions.
  • Applications in biology, population dynamics, image and pattern recognition.
  • Modeling, simulations and visualization of the results (course project).
Examination Presentation/Oral exam.
Literature
  • L. Perko, Differential Equations and Dynamical Systems
  • M. W. Hirsch and S. Smale, Differential Equations, Dynamical Systems, andLinear Algebra
  • G. Teschl. Ordinary differential equations and Dynamical Systems
Comments -

Image Analysis and Object Recognition

Lecturer Rodehorst
Modules Intelligent Information Systems, Electives
ECTS / SWS 4.5ECTS / 2+1 SWS (Lecture+Lab)
Teaching concept The course consists of lectures and accompanying lab classes. Team work (2-3 students per team) during lab classes is recommended.
Cycle Alternating with other courses of computer vision.
Requirement BSc in a relevant study field
Objective The lecture gives an introduction to the basic concepts of image processing, image analysis and object recognition.
Content Outline of the course topics:
  • Image enhancement 
  • Local filters and morphological operators
  • Feature extraction
  • Image segmentation
  • Operations in frequency domain
  • Generalized Hough transform
  • Object categorization and recognition
Grading Written exam. Admission to the exam requires the successful completion of the lab classes.
Literature  
  • lecture notes for download
  • R.C. Gonzalez and R.E. Woods: Digital image processing, Prentice Hall, 2008.
  • R. Szeliski: Computer vision: algorithms and applications, Springer, 2010.
  • R.O. Duda, P.E. Hart and D.G. Stork: Pattern classification, Wiley, 2000.
Comments -

Introduction to Machine Learning

Code ML I
Lecturer Stein
Modules Intelligent Information Systems, Electives
ECTS / SWS 4ECTS, V2/Ü1 SWS
Teaching concept The lecture introduces concepts, algorithms, and theoretical backgrounds. The accompanying lab treats both theoretical and applied tasks to deepen the understanding of the field. Team work (2-3 students) is appreciated.
Cycle Alternating with other IIS courses of Prof. Stein.
Requirement BSc in a relevant study field
Objective Students will learn to understand machine learning as a guided search in a space of possible hypotheses. The mathematical means to formulate a particular hypothesis class determines the learning paradigm, the discriminative power of a hypothesis, and the complexity of the learning process. Aside from foundations of supervised learning also an introduction to unsupervised learning is given.
Content
  • Introduction
  • Concept learning
  • Regression
  • Performance measures
  • Decision trees
  • Neural networks
  • Bayesian learning
  • Kernel methods
  • Cluster analysis
Grading Written or oral examination. Participation requires the successful completion of the course labs.
Literature
  • Mitchell. Machine Learning
  • Duda/Hart/Storck. Pattern Classification
  • Hastie/Tibshirani/Friedman. The Elements of Statistical Learning
  • Cristianini/Taylor. An Introduction to Support Vector Machines
Comments -

Mobile Information Systems

Lecturer Echtler
Modules Interactive Information Systems, Distributed and Secure Systems
ECTS / SWS 4.5 ECTS / 2+1 SWS (Lecture+Lab)
Teaching concept Weekly lecture in combination with group projects
Cycle Bi-annual (Summer Term)
Requirement  
  • Good knowledge in databases
  • Good knowledge in a programming language used for smartphones
 
Objective The lecture "Mobile Information Systems" focuses on the topics and issues surrounding modern mobile devices, their software and hardware and the structure of the associated networks.
Content Preliminary list of topics:
Overview: history & current state of mobile devices
  • Hardware & related issues (power consumption)
  • Software & major OSs: Android & iOS Architecture of Mobile Networks
  • 3G (UMTS) Network
  • SS7 Backend Network
  • Location Discovery & Queries Service Discovery & ad-hoc networking
  • „Big brother“ issues
  • Decentralization/P2P Dealing with Limited Bandwidth & Connectivity
  • Distributed Filesystems (Case Study: Dropbox)
  • „rsync“ rolling checksum algorithm
  • Background: distributed databases (CAP theorem)
Exercises: Development of Android apps with advanced features (P2P networking, location features, NFC, ...)
Examination 50 % project, 50 % exam
Literature ---
Comments  

Number Theory

Code Number
Lecturer Gürlebeck
Modules Modeling
ECTS / SWS 4ECTS, V2/Ü2 SWS
Teaching concept The lecture introduces the basics of the algebraic number theory. The accompanying exercise classes are concerned with theoretical as well as applied tasks to deepen the understanding of the field.
Cycle Alternating with other courses of Prof. Gürlebeck
Requirement BSc in a relevant study field
Objective
Content
  • Introduction to Algebra
  • Number systems
  • Divisibility
  • Diophantine equations and congruences
  • Reciprocity
  • Outlook to cryptography
Examination Written or Oral exam.
Literature
  • H. Weyl, Algebraic Theory of Numbers
  • Remmert, R. & P. Ullrich: Elementare ZahlentheorieJones,G. and Jones, J. Elementary Number Theory Jones,G. and Jones, J. Elementary Number Theory Formularende
  • Jones,G. and Jones, J. Elementary Number Theory
Comments -

Online Computation

Lecturer Jakoby
Modules Modeling, Specialist Module
ECTS / SWS 4.5 ECTS / 2+1 SWS (Lecture+Lab)
Teaching concept
The course consists of lectures and accompanying lab classes.
Cycle summer term
Requirement
Objective In the lecture Online Computation, we will present and analyze online problems and algorithms as well as some basic methods from some of these areas.
Content Online computation is a model for algorithms and problems which require decision under uncertainty. In an online problem the algorithm does not know the entire input from the beginning: the input is revealed in a sequence of steps. An online algorithm should make its computation based only on the observed past and without any secure knowledge about the forthcoming sequence in the future. The effects of a decision taken cannot be undone.
We find online problems and online algorithms within many areas, such as
  • data structures,
  • optimization problems,
  • geometric algorithms,
  • parallel and distributed systems,
  • scheduling problems.

Grading oral or written examination (depending on the number of students)
Literature Allan Borodin, Ran El-Yaniv
Online Computation and Competitive Analysis CAMBRIDGE UNIVERSITY PRESS, 2005
Comments -

Photogrammetric Computer Vision

Code PCV
Lecturer Rodehorst
Modules Electives
ECTS / SWS 4.5 ECTS / 2+1 SWS (Lecture+Lab)
Teaching Concept

The course consists of lectures and accompanying lab classes.

Cycle Winter term
Requirement -
Objective The lecture gives an introduction to the basic concepts of sensor orientation and 3D reconstruction. The goal is an understanding of the principles, methods and applications of image-based measurement. It covers topics such as the algebraic projective geometry, imaging geometry, calibration, orientation methods, stereo image matching and other surface reconstruction methods.
Content Structure:
  • Fundamentals
  • Algebraic projective geometry
  • Imaging geometry of a camera
  • Sensor calibration
  • Orientation methods
  • Dense stereo image matching
  • Methods for surface reconstruction
Grading       Written examination. Participation requires the successful completion of the course labs.
Literature
  • Course notes for download
  • R. Hartley and A. Zisserman: Multiple View Geometry in Computer Vision, 2. Ed., Cambridge University Press, 2003.
  • O. Faugeras and Q.-T. Luong: The Geometry from Multiple Images, MIT Press, 2004.
  • R. Szeliski: Computer Vision: Algorithms and Applications, Springer, 2010.
Comments -

Perceptual Computer Graphics

Code CGA2011-x2309756.1
Lecturer Wuethrich
Modules Interactive Information Systems, Elective courses
ECTS / SWS 4.5ECTS, Lecture2/Lab1 SWS
Teaching concept The lecture introduces advanced topics and methods for Computer Graphics. Besides traditional lectures, students are given programming and close to practice exercise tasks to complete in order to get hands on experience on the lecture themes.
Cycle Biannual.
Requirement B.Sc.
Objective To acquire solid knowledge in the interaction between Computer Graphics and the Human Perceptual system. To learn evaluation methods for the quality of images and videos and their consequences in compression algorithms.
Content Compression algorithms, Modeling Methods for modeling the Human perceptual System, Compression algorithm quality analysis, display technology. .
Examination Examination on the lecture content will be either oral or written. Admission to the exam requires the successful completion of the lab classes.
Literature Wang + Bovik, “Modern Image Quality Assessment”.Course Notes
Comments -

Real-Time Rendering

Code RR
Lecturer Fröhlich
Modules Interactive Information Systems, Electives
ECTS / SWS 4.5ECTS, V2/Ü1 SWS
Teaching concept The course consists of lectures and accompanying lab classes. Team work (2 students per team) during lab classes is recommended.
Cycle Alternating with Virtual Reality
Requirement BSc in a relevant study field
Objective The course teaches the theoretical, applied and technical foundations of real-time rendering. The accompanying lab classes allow students to implement and test a set of real-time rendering algorithms and a project of their own choice.
Content Outline of the course topics:
  • Real-time rendering hardware
  • Spatial acceleration schemes
  • Real-time ray tracing
  • Terrain rendering
  • Polygonal rendering
  • Volume rendering
  • Point-based rendering
  • Image-based rendering
  • Out-of-core techniques
Examination Oral exam. Admission to the exam requires the successful completion of the lab classes and the completion of a one-week project.
Literature
  • Akenine-Möller, Haines, Hoffman: Real-Time Rendering
  • www.realtimerendering.com
Comments -

Randomized Algorithms

Lecturer Jakoby
Modules Modeling, Specialist Module
ECTS / SWS 4.5 ECTS / 3 SWS
Teaching concept -
Frequency -
Requirement
Content

For many problems randomized algorithms are the only known efficient solution method. For some other problem we can find randomized algorithms that are much simpler and more understandable than any known deterministic method. It is therefore not surprising that we find randomized algorithms in many areas, such as in

  • data structures,
  • geometric algorithms,
  • graph algorithms,
  • parallel and distributed systems,
  • on-line algorithms and
  • number theory.

In the lecture Randomized Algorithms, we will present and analyze randomized algorithms and basic methods from some of these areas. Furthermore, basic probabilistic methods for the analysis of algorithms are presented.

Examination -
Literature  
  • Michael Mitzenmacher, Eli Upfal: Probability and Computing Randomized Algorithms and Probabilistic Analysis, CAMBRIDGE UNIVERSITY PRESS, 2005 
Comments -

Search Algorithms

Code Search
Lecturer Stein
Modules Intelligent Information Systems, Modeling, Electives
ECTS / SWS 4ECTS, V2/Ü1 SWS
Teaching concept The lecture introduces concepts, algorithms, and theoretical backgrounds. The accompanying lab treats both theoretical and applied tasks to deepen the understanding of the field. Team work (2-3 students) is appreciated.
Cycle Alternating with other IIS courses of Prof. Stein.
Requirement BSc in a relevant study field
Objective Tackling combinatorial problems by a machine usually follows a two-step approach: (1) definition of a space of solution candidates plus (2) intelligent exploration of this space. The students will learn to analyze the nature of search problems, this way being able to devise adequate search space representations. Moreover, techniques, formal means, and heuristics to construct search strategies will be taught and discussed: this aspect is inevitable since even with modern hardware only a small fraction of a search space can be analyzed.
Content
  • Introduction
  • Search space representations
  • Basic search
  • Informed search
  • Search theory
  • Relaxed models
  • Basic game playing
Grading Written or oral examination. Participation requires the successful completion of the course labs.
Literature
  • Pearl. Heuristic Search.
  • Russel/Norvig. Artificial Intelligence: A Modern Approach
  • Nilsson. Artificial Intelligence
Comments -

Secure Channels

Lecturer Lucks
Modules Distributed & Secure Information Systems, Electives
ECTS / SWS 4.5 ECTS/2+1 SWS (Lecture+Lab)
Teaching concept Course with Lab
Cycle Annual
Requirement Bachelor's degree, fundamental knowledge about cryptography and discrete mathematics
Content A secure channel between two or more participants provides privacy and integrity of the transmitted data. The goal of this course is to understand the principles of designing and analyzing secure channels. The students will learn to distinguish between a secure and an insecure design, by conceiving the basic ideas of secure channels:
  • Formalizing the security requirements
  • Analyzing existing protocol and channel designs
  • How to prove the security of a given design
  • Sound implementation of secure channels
Grading Oral exam
Literature will be given in advance of the semester at the website
Comments none

Usability Engineering

Code IIS, RIS
Lecturer Bertel
Modules Interactive Information Systems, Electives
ECTS / SWS 4.5 ECTS / 2+1 SWS (Lecture+Lab)
Teaching concept This course will introduce the most relevant concepts, theories, methods, and techniques within the field of usability engineering. Lectures will be complemented by labs, in which participants will work in teams to address selected practical and theoretical aspects in more depth.
Cycle Alternating with other courses in Intelligent Information Systems by Prof. Bertel
Requirement Bachelor’s degree in a relevant field of study
Objective Participants will learn about the various factors that determine a system’s usability, as well as how to test for them, how to formulate recommendations towards improving a system’s usability, and how to successfully accompany processes of implementing such recommendations.
Content  
  • Factors that determine a system’s usability
  • Usability engineering lifecycles
  • Testing for usability: goals, theories, methods, techniques
  • Formulating requirements
  • Usability heuristics
  • Running an experiment
  • Usability engineering for specific systems and specific user groups
  • Issues of standardization
  • Designing for usabilityand selected other topics.
Grading Written or oral exams. Points obtained by successful completion of the labs will be counted towards the final grade. Admission to exams requires a successful completion of the labs.
Literature To be announced on the course’s website before the start of the semester.
Comments -

Virtual Reality

Code VR
Lecturer Fröhlich
Modules Interactive Information Systems, Electives
ECTS / SWS 4.5ECTS, V2/Ü1 SWS
Teaching concept The course consists of lectures and accompanying lab classes. Team work (2 students per team) during lab classes is recommended.
Cycle Alternating with Real-time Rendering.
Requirement BSc in a relevant study field
Objective The course teaches the theoretical, applied and technical foundations of modern virtual reality systems, 3D TV, 3D Cinema and 3D user interfaces. The accompanying lab classes allow students to implement a set of 3D interaction techniques in stereoscopic environments and a project of their own choice.
Content The course focuses on the following topics:
  • Scene graph technology
  • Stereoscopic single- and multi-viewer display technology
  • Basics of 3D perception
  • Rendering stereoscopic images
  • Modern 3D user interfaces
Grading Oral exam. Admission to the exam requires the successful completion of the lab classes and the completion of a one-week project.
Literature
  • Bowman et al. 3D User Interfaces
  • IEEE Virtual Reality Conference Proceedings
  • 3D User Interface Symposium Proceedings
Comments -

Web Search and Information Retrieval

Code WebIR
Lecturer Hagen
Modules Distributed & Secure Information Systems, Intelligent Information Systems, Electives
ECTS / SWS 4.5 ECTS / 2+1 SWs (Lecture+Tutorial)
Teaching concept The lecture introduces concepts, algorithms, and theoretical background. The accompanying tutorial treats both theoretical and applied tasks to deepen the understanding of the field. Team work (2-3 students) is appreciated.
Requirement BSc in a relevant study field.
Objective Students will learn to understand search engines and information retrieval systems. The mathematical background needed to understand how relevant information can be found in very large collections as well as practical considerations in implementing search engines will be introduced.
Content
  • Architecture of a Search Engine
  • Crawling, Parsing, Information Extraction
  • Inverted Indexes and Index Compression
  • Query Processing
  • Retrieval Models
  • Experimental Evaluation
  • Distributed Search
Grading Written or oral examination. Participation requires the successful completion of the tutorials.
Literature
  • Baeza-Yates, Ribeiro-Neto. Modern Information Retrieval
  • Buettcher, Clarke, Cormack. Information Retrieval: Implementing and Evaluating Search Engines
  • Croft, Metzler, Strohman. Search Engines: Information Retrieval in Practice
  • Grossman, Frieder. Information Retrieval: Algorithms and Heuristics
  • Manning, Raghavan, Schütze. Introduction to Information Retrieval
  • Van Rijsbergen: Information Retrieval
  • Witten, Moffat, Bell. Managing Gigabytes: Compressing and Indexing Documents and Images
Comments -