Complete Module Catalogue of the Computer Science for Digital Media Master's program (since summer semester 2017)
Courses  Modelling  Distributed and Secure Systems  Intelligent Information Systems  Graphical & Interactive Systems  Specialization  WS  SS 

Advanced Analysis  •  •  •  
Advanced HCI: Theory and Methods  •  •  
Advanced HCI: Ubiquitous Computing  • *  •  •  •  
Advanced Numerical Mathematics  •  •  •  
Advanced Type Theory for Functional Programming  •  •  •  
Cognitive Systems  •  •  
Computer Graphics II: Animation Systems  •  •  •  
Computer Graphics II: Fundamentals of Imaging  •  •  •  
Cryptographic Hash functions  •  •  •  •  
Discrete Optimization  •  •  •  
Geometry  •  •  •  
Image Analysis and Object Recognition  •  •  
Introduction to Functional Programming with Haskell  •  •  •  
Introduction to Machine Learning and Data Mining  •  •  •  
Logics and Semantic Web  •  •  
Machine Learning for Software Engineering  •  •  •  
Mobile Information Systems  • *  •  •  
Online Computation  •  •  •  
Photogrammetric Computer Vision  •  •  
Randomized Algorithms
 •  •  •  
Safety and Security Engineering  •  •  •  •  
Search Algorithms  •  •  •  
Secure Channels  •  •  •  •  
Software Product Line Engineering  •  •  •  
Spatial Computational Geometry  •  •  •  
Spatial Information Systems (GIS)  (Elective only)  •  
Usability Engineering  •  •  
Virtual Reality  •  •  
Watermarking & Steganography
 • *  •  •  •  
Web Search and Information Retrieval  •  •  • 
* (by application to the examination committee)
Course Index
Advanced Analysis
Advanced Analysis
Course Title  Advanced Analysis 
Coordinator  Prof. Dr. rer.nat. habil. Klaus Gürlebeck 
Assigned Module(s)  Modelling 
Formal requirements for participation  Analysis (course) 
Examination requirements  Written examination 
Specific target qualifications  Many realworld 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:
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
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 special problems at research level and be able to work with them in form of supervised projects. 
Contents 

Special information  Burg/Haf/Wille: Höh. Math. f. Ing., Bde. 35, Taylor: Partial Differential Equations IIII. Maple

Advanced Numerical Mathematics
Advanced Numerical Mathematics
Course Title  Advanced Numerical Mathematics 
Coordinator  Prof. Dr. rer.nat. habil. Klaus Gürlebeck 
Assigned Module(s)  Modeling 
Formal requirements for participation  Courses Analysis, Linear Algebra and Numerical Mathematics 
Examination requirements  Oral examination, 30 minutes with 30 minutes for preparation 
Specific target qualifications  The lecture course introduces concepts, algorithms, and theoretical background for the numerical solution of partial differential equations. The accompanying practical classes are concerned with theoretical as well as applied tasks in order to expand understanding of the field. This will be completed by classes in the computer lab. The computer simulations are based on Matlab programs.
The course will deal with the following topics:
Students should be able to apply the above tools and the theory to solve concrete problems. Furthermore, they should be able to create numerical computer simulations with Matlab.
Students should be able to understand
in order to solve practical problems from mathematical physics and engineering in order to create accurate simulations. They should be able to adapt standard models to the given situation if necessary.
Students should be able to understand special problems at research level and be able to work with them in the form of supervised projects. 
Contents 

Special information  Varga, Matrix iterative analysis. Hermann, Numerische Mathematik Kress, Numerical Analysis Matlab

Advanced Type Theory for Functional Programming
Advanced Type Theory for Functional Programming
Course Title  Advanced Type Theory for Functional Programming 
Coordinator  Dr. rer. nat. Dmitrii Legatiuk 
Assigned Module(s)  Modeling 
Formal requirements for participation  No specific requirements for this course 
Examination requirements  Submission of a project given during semester with weight of 50% of total grade. The project has to be presented at the final oral examination (max. 30 min) with a time for preparation (max. 30 min). 
Specific target qualifications  Type theory is a solid part of modern programming languages. Development of new concepts and understanding principles of programming languages require a careful consideration of types and their role in programming. Functional programming is a paradigm in which type theory is naturally included via formalism of typed versions of lambda calculus. Another modern formalism closely related to functional programming and type theory is category theory, which is, in simple terms, the abstract theory of functions. Haskell is an example of a modern programming language in which both concepts come naturally together. The goal of this course is to present advanced topics in functional programming related to type and category theories. Students should understand the following topics:
Students should be able to apply the above tools and theories to solve concrete problems. Furthermore, they should appreciate the limits and constraints of the above theories. Students should be able formalise and generalise their own solutions using the above topics. Students should master concepts and approaches such as
in order to tackle problems from programming and its application to digital media. They should be able to understand proposed programming problems, to make wellinformed decisions about the preferred proposal and, if necessary, to find their own solutions to given programming problems. Students should develop an understanding of the current state of research in type theory and functional programming. With appropriate supervision, students should be able to tackle research problems. 
Contents 

Special information  B. Pierce, Basic category theory for computer scientists B. Pierce, Types and programming languages Haskell Platform 
Cognitive Systems
Cognitive Systems
Course Title  Cognitive Systems 
Coordinator  Sven Bertel 
Assigned Module(s)  Intelligent Information Systems 
Formal requirements for participation  Bachelor’s degree in a relevant field of study 
Examination requirements  Active participation in labs (minimum 50% of achievable points across all lab sections). Final oral exam (max. 45 min.). 
Specific target qualifications  This course will provide a systematic introduction to the interdisciplinary field of natural and artificial cognitive systems. It will present the relevant computational and psychological concepts, theories, methods, and terminology. Students should learn about predominant theories, models, techniques, methods, and concepts of information processing in and presentation for humans as well as selected artificial systems. Students should understand the technical approaches for user simulation and modelling. Students should be able to assess selected approaches for complex problems for appropriateness and effectiveness, and should be able to justify choices of methods. Students should understand the following topics:
Students should be able clearly to understand the potential and limits of currentgeneration computational models of human cognition. Students should be able to distinguish good and bad model and be able to recommend suitable methods designed to improve a model’s quality.
Students should develop an understanding of the current state of research in cognitive systems. With appropriate supervision, students should be able to tackle research problems in the area. 
Contents 
plus selected other topics. 
Special information  The Cambridge Handbook of Computational Psychology by Ron Sun 
Cryptographic Hash Functions
Cryptographic Hash Functions
Course Title  Cryptographic Hash Functions 
Coordinator  Stefan Lucks 
Assigned Module(s)  Distributed and Secure Systems and Specialisation 
Formal requirements for participation  Basic knowledge of cryptography, as expected either from a previous Bachelor’s course or from “Modern Cryptography”. 
Examination requirements  Active participation in problem session (minimum 25% of achievable points per problem set). Final oral exam (max. 45 min.). 
Specific target qualifications  A cryptographic hash function serves as a “fingerprint” to uniquely identify data. The goal of this course is to understand the principles of designing and analysing cryptographic hash functions, and to apply them to the context of digital media. The course deals with the following topics:
Students should understand the application of hash functions for solving concrete problems and be able to distiguish secure from insecure designs. Students should be able to maser the following concepts:
Students should understand the current state of research in cryptography, specifically of the design, analysis and application of cryptographic hash functions. With appropriate supervision, students should be able to tackle research problems in cryptogaphy. 
Contents 

Special information  The course is based on recent publications; which will be provided during the semester. 
Digital Watermarking & Steganography
Digital Watermarking & Steganography
Discrete Optimisation
Discrete Optimisation
Course Title  Digital Watermarking & Steganography 
Coordinator  Andreas Jakoby 
Assigned Module(s)  Specialist Module: Distributed and Secure Systems On special application to the examination committee: Modeling 
Formal requirements for participation  (no specific requirements for this course) 
Examination requirements  Final oral exam (max. 45 min.). 
Specific target qualifications  A digital watermarking and steganography deals with hiding additional information in digital data such as audio data or pictures. The main goal of digital watermarking is to embed information about the content of data within the content, for instance copyrights. Steganography, on the other hand, deals with the aspect of hiding the existence an embedded message. The goal of this course is to understand the principles of designing and analysing schemes for digital watermarking and steganography, and to apply them to the context of digital media. The course deals with the following topics:
Students should understand the application of digital watermarking and steganography for solving concrete problems. They should be able to distinguish secure from insecure designs. Students shall maser the following concepts:
Students should understand the current state of research in digital watermarking and steganography, specifically of the design, analysis and application of schemes for digital watermarking and steganography. With appropriate supervision, students should be able to tackle research problems in digital watermarking and steganography. 
Contents 

Special information  I. J. Cox, M. L. Miller, J. A. Bloom, J. Fridrich, T. Kalker, Digital Watermarking and Steganography (Second Edition), Korgan Kaufmann, 2008. Octave or Matlab will be used in the Lab 
Course Title  Discrete Optimisation 
Coordinator  Andreas Jakoby 
Assigned Module(s)  Modeling, Specialist Module 
Formal requirements for participation  (no specific requirements for this course) 
Examination requirements  Final oral exam (max. 45 min.). 
Specific target qualifications  Discrete optimisation is about finding optimal solutions for discrete problems. Finding efficient algorithms for discrete optimisation problems is one of the main topics in algorithm design. The goal of this course is to understand the principles of analysing discrete optimisation problems and designing efficient algorithms for such problems. Students should understand the following topics and methods:
Students should be able to apply the above concepts to solve concrete problems. Furthermore, they should appreciate the limits and constraints of the above schemes. Students should be able formalise and generalise their own solutions using the above tools. Students should master concepts and approaches such as
in order to tackle optimisation problems. They should understand the current state of research in discrete optimisation, specifically of the design, analysis and application of schemes for optimisation problems. With appropriate supervision, students should be able to tackle research problems in discrete optimisation.

Contents 

Special information  T. Cormen, C. Leiserson, R. Rivest, Introduction to Algorithms, MIT Press, 1990 J. Kleinberg, E. Tardos, Algorithm Design, Addison Wesley, 2005 W. Kocay, D. Kreher, Graphs, Algorithms and Optimisation, CRC 2005 D. Kreher, D. Stinson, Combinatorial Algorithms, CRC Press, 1999 C. H. Papadimitriou, K. Steiglitz, Combinatorial Optimisation: Algorithms and Complexity, Dover Books on Computer Science, 2000 
Geometry
Geometry
Course Title  Geometry 
Coordinator  Reinhard Illge 
Assigned Module(s)  Modeling 
Formal requirements for participation  (No specific requirements for this course) 
Examination requirements  Active participation in problem session: solving at least two problems identified in the session and presenting the solutions on the blackboard. Final oral exam (max. 45 min.). 
Specific target qualifications  One of the defining features of mathematics, already formulated in ancient Greece, is to achieve truth by proof, to find laws by logical conclusions. This approach was explained in Euclid`s Elements and completed by David Hilbert in his book “Grundlagen der Geometrie”. The goal of this course is to present synthetic geometry as a prime example for the systematic development of a theory, based on a few axioms.
Students should understand the following topics:
Students should be able to apply the above tools and topics for solving concrete problems. Furthermore, they should appreciate the limits and constraints of the above, e.g that the change of the parallel axiom leads to another, NonEuclidean geometry. Students should be able to formalise and generalise their own solutions using the above tools. Students should master concepts and approaches such as
in order to tackle problems from geometry and its application to digital media. They should be able to understand proposed geometrical problems, to make wellinformed decisions about the preferred proposal and, if necessary, to find their own solutions to given geometrical problems. Students should develop an understanding of the current state of research in Geometry. With appropriate supervision, students should be able to tackle research problems. 
Contents 

Special information  Walter Meyer: Geometry and Its Applications, Elsevier 2011 
Spatial Information Systems (GIS)
Spatial Information Systems (GIS)
Course Title  Spatial information systems (GIS) 
Coordinator  Volker Rodehorst 
Assigned Module(s)  Electives 
Formal requirements for participation  (no specific requirement for this course) 
Examination requirements  Successful completion of the lab classes, final written exam. 
Specific target qualifications  The course covers advanced basics of spatial information systems (GIS), such as acquisition, organization, analysis and presentation of data with spatial reference. The practical classes and the individual project lead to a deeper understanding of GIS workflows, tools and extensions and should turn knowledge into practice. Students should understand the following topics:
Students should be able to apply the above topics to solve problems with spatial reference. Furthermore, they should appreciate the limits and constraints of the above topics. Students should be able formalise and generalise their own solutions using the above concepts of acquisition, organization, analysis and presentation of geospatial data. Students should master concepts and approaches such as
in order to tackle problems of spatial information systems and its application to digital media. They should be able to understand the proposed concepts, to compare different proposals for GIS systems, to make wellinformed decisions about the preferred proposal and, if necessary, to find their own solutions to given problems with spatial reference. Students should develop an understanding of the current state of research in spatial information systems. With appropriate supervision, students should be able to tackle research problems. 
Contents 

Special information  Course material:
Literature:

Image Analysis and Object Recognition
Image Analysis and Object Recognition
Course Title  Image Analysis and Object Recognition 
Coordinator  Volker Rodehorst 
Assigned Module(s)  Intelligent Information Systems 
Formal requirements for participation  (no specific requirement for this course) 
Examination requirements  Successful completion of the lab classes, final written exam 
Specific target qualifications  The course gives an introduction to advanced concepts of image processing, image analysis and object recognition. The goal is to understand the principles, methods and applications of computer vision from image processing to image understanding. Students should learn the following topics:
Students should be able to apply the above topics to solve computer vision problems. Furthermore, they should appreciate the limits and constraints of the above topics. Students should be able formalise and generalise their own solutions using the above concepts of image processing, image analysis and object recognition. Students should master concepts and approaches such as
in order to tackle computervision problems and their application to digital media. They should be able to understand proposed image analysis methods, to compare different proposals for object recognition systems, to make wellinformed decisions about the preferred proposal and, if necessary, to find their own solutions to given computer vision problems. Students should develop an understanding of the current state of research in image analysis and object recognition. With appropriate supervision, students should be able to tackle research problems. 
Contents 

Special information  Course material:
Literature:

Introduction to Functional Programming with Haskell
Introduction to Functional Programming with Haskell
Course Title  Introduction to Functional Programming with Haskell 
Coordinator  Dr. rer. nat. Dmitrii Legatiuk 
Assigned Module(s)  Modeling 
Formal requirements for participation  No specific requirements for this course 
Examination requirements  Submission of a project given during semester with weight of 50% of total grade. The project should be presented at the final oral examination (max. 30 min) with time for preparation (max. 30 min). 
Specific target qualifications  Functional programming is a modern programming paradigm based on lambda calculus and recursive functions as models of computation. A program in functional programming is a function in a strong mathematical sense, and the output of a program is application of the function to its arguments. Haskell is a brilliant example of a welldesigned programming language illustrating all the advantages of functional programming. The goal of this course is to present basic concepts of the functional paradigm and their realisation in Haskell. Students should understand the following topics:
Students should be able to apply the above tools and theories to solvie concrete problems. Furthermore, they should appreciate the limits and constraints of the above theories, e.g. limitations of pure lambda calculus and a need for types. Students should be able formalise and generalise their own solutions with reference to the above topics. Students should master concepts and approaches such as
in order to tackle problems from functional programming and their application to digital media. They should be able to understand proposed programming problems, to make wellinformed decisions about the preferred proposal and, if necessary, to find their own solutions to given programming problems. Students should develop an understanding of the current state of research in functional programming. With appropriate supervision, students should be able to tackle research problems. 
Contents 

Special information  R. Bird, Thinking Functionally with Haskell Haskell Platform 
Logics and Semantic Web
Logics and Semantic Web
Course Title  Logics and Semantic Web 
Coordinator  Benno Stein 
Assigned Module(s)  Modeling, Specialist Module 
Formal requirements for participation  (no specific requirement for this course) 
Examination requirements  Active participation in lab classes. Final written exam. 
Specific target qualifications  The first part of this lecture course (twothirds) introduces the notions and methods of formal logic, covering propositional logic, predicate logic and the foundations of automated deduction. Based on this, the second part of the lecture explains the inference concepts behind the semantic web. Students should understand the following concepts from logics:
Students should be able to employ logics as a modeling tool. They should understand and be able to explain the concept of entailment and how to automate theorem proofing. Based on these insights, they should be able to explain the working principles of the semantic web, to model knowledgebased relations and to encode them using an OWL variant. Students should master
in order to model semantic relations in webbased applications. Students should develop an understanding of the current developments of the semantic web, as well as its possibilities and its limits and constraints. With appropriate supervision, they should be able to tackle research problems. 
Contents 

Special information  Course material: www.uniweimar.de/en/media/chairs/webis/teaching/lecturenotes/ Literature:

Intelligent Software Systems, Specialist Module
Intelligent Software Systems, Specialist Module
Course Title  Machine Learning for Software Engineering 
Coordinator  Norbert Siegmund 
Assigned Module(s)  Intelligent Software Systems, Specialist Module 
Formal requirements for participation  (no specific requirements for this course) 
Examination requirements  Active participation in programming tasks (minimum 50% of achievable points across all tasks). Final oral exam (max. 45 min.). 
Specific target qualifications  NatureInspired Machine Learning (NiML) is about learning and optimising complex tasks that are computationally intractable for exact methods. The goal of this course is to understand the principles of metaheuristics in optimisation, as well as key concepts of learning based on neural nets. Students should understand the following techniques and theories:
Students should be able to apply the above theories to solve concrete learning and optimisation problems. Furthermore, they should appreciate the limits and constraints of the individual methods above. Students should be able formalise and generalise their own solutions using the above concepts and implement them in a specified language (preferable in Python). Students should master concepts and approaches such as:
in order to tackle the difficulty of learning and optimising huge problems inherent to digital media. They should also be able to implement the algorithms and techniques in Python and be able to understand a proposed problem, to compare different approaches and techniques regarding applicability and accuracy, to make wellinformed decisions about the preferred solution and, if necessary, to find their own solutions. Students should develop an understanding of the current state of research in optimisation and learning. With appropriate supervision, students should be able to tackle new research problems, especially in the area of searchbased software engineering. 
Contents 

Special information  Python recommended Sebastian Raschka: Python Machine Learning, Packt Publishing 2015, ISBN13: 9781783555130. Jeff Heaton: Artificial Intelligence for Humans, Volume 2: NatureInspired Algorithms, CreateSpace Independent Publishing Platform 2015, ISBN13: 9781499720570 Jeff Heaton: Artificial Intelligence for Humans, Volume 3: Deep Learning and Neural Networks, CreateSpace Independent Publishing Platform 2015, ISBN13: 9781505714340. 
Introduction to Machine Learning and Data Mining
Introduction to Machine Learning and Data Mining
Course Title  Introduction to Machine Learning and Data Mining 
Coordinator  Benno Stein 
Assigned Module(s)  Intelligent Information Systems, Specialist Module 
Formal requirements for participation  (no specific requirement for this course) 
Examination requirements  Active participation in lab classes. Final written exam. 
Specific target qualifications  Given a task and a performance measure, a computer program (and hence a machine) is said to learn from experience if its performance at the task improves with experience. In this course, students will learn to understand machine learning as a guided search in a space of possible hypotheses. The mathematical means of formulating a particular hypothesis class determines the learning paradigm, the discriminative power of a hypothesis and the complexity of the learning process. As well as the basis of supervised learning, an introduction to unsupervised learning is also provided. Students should understand the following concepts and theories:
Students should be able formalise realworld decision tasks as machine learning problems. They should be able to apply the above concepts and theories to solve concrete learning problems. In particular, they should be able to choose the appropriate learning paradigm within a concrete setting. Students should master concepts and approaches such as
in order to tackle learning and mining problems and their application to digital media. They should be able to analyse machine learning problems, to compare different learning algorithms, and to make wellinformed decisions about the prefered learning paradigm. Students should develop an understanding of current developments in machine learning. With appropriate supervision, they should be able to tackle research problems. 
Contents 

Special information  Course material: www.uniweimar.de/en/media/chairs/webis/teaching/lecturenotes/ Tools: Weka, scikitearn, R, SciPy, GNU Octave Literature:

Online Computation
Online Computation
Course Title  Online Computation 
Coordinator  Andreas Jakoby 
Assigned Module(s)  Modeling, Specialist Module 
Formal requirements for participation  (no specific requirements for this course) 
Examination requirements  Final oral exam (max. 45 min.). 
Specific target qualifications  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. The goal of this course is understand the principles of designing and analysing schemes for online computations and for competing online problems. The course deals with the following topics:
Students should understand the application of competitive analysis for solving concrete problems. They should be able to distinguish efficient from inefficient solutions. Students should master concepts and approaches such as
in order to tackle problems from online computation and competitive analysis. They should be able to understand proposed competitive analysis problems, to compare different proposals for online computations, to make wellinformed decisions about the preferred proposal and, if necessary, to find their own solutions to given online problems. Students should develop an understanding of the current state of research in online computation and competitive analysis. With appropriate supervision, students should be able to tackle research problems within this field. 
Contents 

Special information  Allan Borodin, Ran ElYaniv, Online Computation and Competitive Analysis, CAMBRIDGE UNIVERSITY PRESS, 2005 
Photogrammetric Computer Vision
Photogrammetric Computer Vision
Course Title  Photogrammetric Computer Vision 
Coordinator  Volker Rodehorst 
Assigned Module(s)  Electives 
Formal requirements for participation  (no specific requirement for this course) 
Examination requirements  Successful completion of the lab classes. Final written exam. 
Specific target qualifications  The course 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 imagebased measurement. Students should learn about the following topics:
Students should be able to apply knowledge of the above topics to solve photogrammetric problems. Furthermore, they should appreciate the limits and constraints of the above topics. Students should be able formalise and generalise their own solutions using the above concepts of sensor orientation and 3D reconstruction. Students should master concepts and approaches such as
in order to tackle problems in photogrammetry and its application to digital media. They should be able to understand proposed sensor orientation problems, to compare different proposals for imagebased 3D reconstruction systems, to make wellinformed decisions about the preferred proposal and, if necessary, to find their own solutions to given problems in photogrammetry. Students should develop an understanding of the current state of research in photogrammetric computer vision. With appropriate supervision, students should be able to tackle research problems. 
Contents 

Special information  Course material:
Literature:

Randomised Algorithms
Randomised Algorithms
Course Title  Randomised Algorithms 
Coordinator  Andreas Jakoby 
Assigned Module(s)  Modeling, Specialist Module 
Formal requirements for participation  (no specific requirements for this course) 
Examination requirements  Final oral exam (max. 45 min.). 
Specific target qualifications  For many problems, randomised algorithms are the only known efficient solution method. For some other problems we can find randomised algorithms that are much simpler and more understandable than any known deterministic method. The goal of this course is to understand the principles of designing and analysing randomised algorithms. The course deals with the following topics:
Students should be able to apply the above tools, algorithm, and concepts to solve concrete problems. Furthermore, they should appreciate the limits and constraints of the above topics. Students should be able formalise and generalise their own solutions using randomization. Students should master concepts and approaches such as
Students should understand the current state of research in randomised algorithms, specifically of the design, analysis and application of randomised algorithms. With appropriate supervision, students should be able to tackle research problems in randomised algorithms. 
Contents 

Special information  Michael Mitzenmacher, Eli Upfal, Probability and Computing  Randomised Algorithms and Probabilistic Analysis, CAMBRIDGE UNIVERSITY PRESS, 2005 
Search Algorithms
Search Algorithms
Course Title  Search Algorithms 
Coordinator  Benno Stein 
Assigned Module(s)  Intelligent Information Systems, Specialist Module 
Formal requirements for participation  (no specific requirement for this course) 
Examination requirements  Active participation in lab classes. Final written exam. 
Specific target qualifications  The course will introduce search algorithms as a means of solving combinatorial problems such as constraint satisfaction and optimisation problems. Tackling such problems by machine often follows a twostep approach: (1) definition of a space of solution candidates followed by (2) intelligent exploration of this space. We will cover the modeling of search problems, basic (uninformed) search algorithms, informed search algorithms, as well as hybrid combinations. Special focus will be placed on heuristic search approaches. Students should understand the following concepts and theories:
Students should be able to model a search space by selecting the appropriate representation principle and by devising encoding for partial solution bases. They should understand and describe how different search algorithms will explore the search space differently. With regard to informed search algorithms, they should understand the principle of admissible search and be able to prove basic properties of the search algorithms (completeness, soundness, admissibility). The students will learn to analyse the nature of search problems, this way being able to
Students should eventually be able to tackle nontrivial search and constraint satisfaction problems and their application to digital media. In this regard, they should be able to make wellinformed decisions and explain their approach to finding solutions, considering the theoretical background. With appropriate supervision, students should be able to tackle research problems. Students should develop an understanding of the current developments of the semantic web. With appropriate supervision, they should be able to tackle research problems. 
Contents 

Special information  Course material: www.uniweimar.de/en/media/chairs/webis/teaching/lecturenotes/ Literature:

Safety and Security Engineering
Safety and Security Engineering
Course Title  Safety and Security Engineering 
Coordinator  Stefan Lucks 
Assigned Module(s)  Distributed and Secure Systems, Specialist Module On special application to the examination committee: Modeling 
Formal requirements for participation  (no specific requirements for this course) 
Examination requirements  Active participation in problem session: solving at least two problems identified in the session and presenting at least one solution. Final oral exam (max. 45 min.). 
Specific target qualifications  Safety is about systems running reliably under normal and exceptional circumstances. Security is about systems defending themselves against malicious manipulation and attacks. The goal of this course is to provide an introduction to the specific skills and the mindset which the designers of such systems need.
Students should understand the following tools and theories:
Students should be able to apply the above theories and tools to solve concrete problems. Furthermore, they should appreciate the limits and constraints of the above theories and tools.
Students should be able formalise and generalise their own solutions using the above tools and theories.
Students should master concepts and approaches such as
in order to tackle problems from safe and secure system development and its application to digital media. They should be able to understand proposed solutions to safety and security problems, to compare different proposals for safe and secure systems, to make wellinformed decisions about the preferred proposal and, if necessary, to find their own solutions to given safety and security problems.
Students should develop an understanding of the current state of research in safety and security engineering. With appropriate supervision, students should be able to tackle research problems. 
Contents 

Special information  This course was previously offered under the title “Software Developement for Safe and Secure Systems”
Participants will need compilers for the Ada and SPARK programming languages (gnat, gnatprove), for the generation of automatic tests (testgen or AUnit) and for test covereage evaluation (gcov, lcov). All these tools are available at no cost under GPL. 
Secure Channels
Secure Channels
Course Title  Secure Channels 
Coordinator  Stefan Lucks 
Assigned Module(s)  Distributed and Secure Systems, Specialist Module 
Formal requirements for participation  Basic knowledge of cryptography, as expected either from a previous Bachelor’s course or from “Modern Cryptography”. 
Examination requirements  Active participation in problem session (minimum 25% of achievable points per problem set). Final oral exam (max. 45 min.). 
Specific target qualifications  A secure channel between two or more participants provides the privacy and integrity of the transmitted data. The goal of this course is to understand the principles of designing and analysing secure channels. Students should understand the following topics:
Students should master the design of secure channels from secure components, such as block ciphers, stream ciphers, MACs or universal hash functions. Students should understand the limits and constraints of the approaches and formalisms presented in the course. They should know how to distinguish secure from insecure designs for secure channels.
Students should recognise the following concepts:
Students should develop an understanding of the current state of research in cryptography, specifically in cryptography as applied to enhance confidentiality and authenticity. With appropriate supervision, students should be able to tackle research problems in the area. 
Contents 
plus other requirements and constraints 
Special information  Introduction to Modern Cryptography by Mihir Bellare and Phillip Rogaway and recent publications 
Spatial Computational Geometry
Spatial Computational Geometry
Course Title  Spatial Computational Geometry 
Coordinator  Bernd Fröhlich 
Assigned Module(s)  Graphic and Interactive Systems, Specialist Module By special application to the examination committee: Modeling 
Formal requirements for participation  (no specific requirements for this course) 
Examination requirements  Active participation in the lab class; a score of 50% of the assignments and the final project needs to be achieved for admission to the final exam. Final oral exam (max. 45 min.). 
Specific target qualifications  The goal of this course is to provide students with the theoretical and applied foundations for the design and analysis of efficient algorithms for problems involving geometric input and output. The course focuses on realtime problems in 2D and 3Dgraphics and visualization applications.
Students should understand the following constructs, techniques, algorithms and efficient data structures:
Students should be able to implement the above algorithms and data structures to solve concrete problems. Furthermore, they should be able to analyse the complexity of the algorithms and data structures.

Contents 

Special information  This course is partially based on the book Computational Geometry, Algorithms and Applications by 
Software Product Line Engineering
Software Product Line Engineering
Course Title  Software Product Line Engineering 
Coordinator  Norbert Siegmund 
Assigned Module(s)  Intelligent Information Systems, Specialist Module 
Formal requirements for participation  (no specific requirements for this course) 
Examination requirements  Active participation in programming tasks (minimum 50% of achievable points from all tasks). Final oral exam (max. 45 min.). 
Specific target qualifications  Software Product Line Engineering (SPLE) is about designing, managing and implementing configurable software systems and software product lines. The goal of this course is to understand the principles of variability management in noncode and coderelated software artefacts, as well as key implementation techniques.
Students should understand the following concepts and techniques:
Students should be able to apply the above techniques to implement concrete configurable software systems using the tool FeatureIDE. Furthermore, they should be able to compare the strengths and weaknesses of the aforementioned approaches, tools, and techniques and select the appropriate methods for the problem at hand.
Students should master concepts and approaches such as
in order to realize SPLE and its application to digital media.
Students should develop an understanding of the current state of research in SPLE. With appropriate supervision, students should be able to tackle research problems in overcoming the inherent difficulties resulting from the variability. 
Contents 

Special information  Sven Apel, Don S. Batory, Christian Kästner, Gunter Saake: FeatureOriented Software Product Lines  Concepts and Implementation. Springer 2013, ISBN 9783642375200. Krzysztof Czarnecki, Ulrich Eisenecker: Generative Programming: Methods, Tools, and Applications: Methods, Techniques and Applications. Pearson Education (Us) 2010, ISBN13: 9780201309775. 
Computer Graphics: Animation Systems
Computer Graphics: Animation Systems
Course Title  Computer Graphics: Animation Systems 
Coordinator  Charles Wuethrich 
Assigned Module(s)  Graphic and Interactive Systems 
Formal requirements for participation  (no specific requirements for this course) 
Examination requirements  Conception and submission of a computer animation. Algorithm study. Final exam. 
Specific target qualifications  Computer animations and animation systems have achieved quite widespread use. This course has a double aim; to allow students to understand the algorithm and modelling techniques used in common high level animation systems, and at the same time be able to appreciate the hard work involved in the production process of a computer animation.
Successful students in this course should understand and be able to programme the underlying algorithms and physics used in a 3Danimation program and to cooperate with artists and designers on a common 3Danimation project, which might involve the programming of plugins for the animation system. At the end of the course, they should have mastered the conception, design and implementation of 3Danimation software. 
Contents  Contents:

Special information 

Advanced HCI: Theory and Methods
Advanced HCI: Theory and Methods
Course Title  Advanced HCI: Theory and Methods 
Coordinator  Hornecker 
Assigned Module(s)  Graphic and Interactive Systems 
Formal requirements for participation  (no specific requirements for this course) 
Examination requirements  Submission of practical project and problembased coursework in combination with presentations and technical discussions. Final exam. 
Specific target qualifications  Students should have an understanding of the difference between quantitative and qualitative methods. They should master core HCI research methods and theories for understanding and analysing human interaction with technology. They should be aware of how the role of theory in HCI has expanded from the early days of human factors and mathematical modeling of behaviour to include explanatory and generative theories, which reflect influences from fields such as design, sociology and ethnography.
Students should know how to apply core HCI methods to novel (and realworld) problems and tasks. Students should be able to run studies using appropriate data gathering or evaluation techniques and methods, in particular qualitative methods (interviews, observation), to adapt and adjust these in light of the given research question and use context, and to justify research method and study design. They should understand and be able to discuss complex HCI issues from the research literature for emerging areas of humancomputer interaction and be able to engage with the literature and acquire other methods independently. With appropriate supervision, students should be able to tackle research problems.
In addition, social and general transferable skills are trained via group work in the classes, based on concrete problems and tasks.

Contents  Sample contents are:

Special information  Introductory Literature:

Computer Graphics: Fundamentals of Imaging
Computer Graphics: Fundamentals of Imaging
Course Title  Computer Graphics: Fundamentals of Imaging  
Coordinator  Charles Wuethrich  
Assigned Module(s)  Graphic and Interactive Systems  
Formal requirements for participation  (no specific requirements for this course)  
Examination requirements  Presentation, discussion, implementation and submission of imaging algorithms. Final exam.  
Specific target qualifications  Modern Digital Imaging Devices are ubiquitous nowadays. The goal of this course is to understand the principles of imaging and to be able to conceive, design and implement systems relevant for imaging.
Students should understand the following topics:
At the end of the course, they should have mastered the conception, design and implementation of imaging software for both generic digital light sensors and digital photography.  
Contents  Contents:
 
Special information 

Advanced HCI: UbiComp
Advanced HCI: UbiComp
Course Title  Advanced HCI: UbiComp 
Coordinator  Hornecker 
Assigned Module(s)  Graphic and Interactive Systems On special application to the examination committee: Distributed and Secure Systems 
Formal requirements for participation  (no specific requirements for this course) 
Examination requirements  Submission of practical project and problembased coursework in combination with presentations and technical discussions. Final exam. 
Specific target qualifications 
Students should have an understanding of theoretical, applied and technical foundations of modern ubiquitous computing systems. They should understand how such Ubicomp systems work on a technical level and also understand their societal relevance. They should know about the technical and socialdesignrelated challenges in developing such systems. They should be able critically to assess societal implications and discuss design tradeoffs. They should be able to develop concpets for novel UbiComp applications, to determine their technical feasibility, and to reflect critically on their feasibility in an application context. Moreover, they should be able to apply a usercentered approach in the design process of UbiComp applications.
Students should understand and be able to discuss complex issues from the HCI and UbiComp research literature for emerging areas of UbiComp and be able to engage with the literature. With appropriate supervision, students should be able to tackle research problems.
In addition, social and general transferable skills are trained via group work in the classes based on concrete problems and tasks.

Contents  Contents:

Special information  Introductory Literature:

Usability Engineering and Usability Testing
Usability Engineering and Usability Testing
Course Title  Usability Engineering and Usability Testing 
Coordinator  Sven Bertel 
Assigned Module(s)  Graphic and Interactive Systems 
Formal requirements for participation  (no specific requirements for this course) 
Examination requirements  Active participation in labs (minimum 50% of achievable points across all lab sections). Final oral exam (max. 45 min.). 
Specific target qualifications  Participants should 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 successfully to accompany processes of implementing such recommendations. Students should understand the following topics:
Students should master the design of behavioural experiments with users to test hypotheses and be able to use appropriate methods for data analysis. They should be able clearly to understand the limits of statistical inferences that can be drawn from an experiment. Students should be able to distinguish good and bad usability and to recommend suitable methods for increasing a system’s usability.
Students should develop an understanding of the current state of research in usability. With appropriate supervision, students should be able to tackle research problems in the area. 
Contents 
plus further selected topics. 
Special information  Lazar et al. (2009): Research Methods in HumanComputer Interaction, Wiley. Rosson & Carroll (2002): Usability Engineering. Morgan Kaufmann. Rubin & Chisnell (2008): Handbook of Usability Testing, 2nd edition. Wiley. Field (2013): Discovering Statistics Using IBM SPSS Statistics. Sage. 
Virtual Reality
Virtual Reality
Course Title  Virtual Reality 
Coordinator  Bernd Fröhlich 
Assigned Module(s)  Graphic and Interactive Systems 
Formal requirements for participation  (no specific requirements for this course) 
Examination requirements  Active participation in the lab class; a score of 50% of the assignments and the final project needs to be achieved for admittance to the final exam. Final oral exam (max. 45 min.) 
Specific target qualifications  The goal of this course is to provide students with the theoretical, technical and applied foundations of modern virtual reality systems, 3Dcinema, stereoscopic gaming and 3Duser interfaces.
Students should understand the following concepts, techniques and technical systems:
Students should be able to apply the above concepts, techniques and their knowledge of technical solutions to solve concrete problems. Furthermore, they should be able identify and discuss the main usability factors of 3Dinteraction techniques, 3Dinterfaces and 3Ddisplay technology.
Students should master concepts and approaches such as
in order to tackle problems from the virtual reality domain. Students should develop an understanding of the basics and the current state of research in virtual reality and make wellinformed decision in this context. They should be able to discuss research problems, implement current approaches and understand the limitations of the solutions.

Contents 

Special information  This course is mostly based on recent research publications. References will be provided throughout the course. 
Web Search and Information Retrieval
Web Search and Information Retrieval
Course Title  Web Search and Information Retrieval 
Coordinator  Matthias Hagen 
Assigned Module(s)  Intelligent Information Systems, Specialist Module 
Formal requirements for participation  (no specific requirements) 
Examination requirements  Oral exam of 3040 minutes 
Specific target qualifications  Web search engines and information retrieval today have the world’s information at the users’ fingertips. Research from the last few decades now helps users to find what they want for a variety of information needs in a split second. The goal of this course is to understand how search engines and IR systems work, to acquire the necessary theoretical background that enables comprehension of practical considerations, and to develop an understanding of comparison and evaluations issues. Limits and constraints and latest trends are part of the curriculum. The students should understand the following topics:
Students should be able to apply the above theories and topics to solve concrete problems in the field of retrieval systems. Furthermore, they should appreciate the limits and constraints of the respective tools and methods that make them suitable approaches in specific scenarios. Students should be able to formalise and generalise their own solutions for retrieval problems using the above theories and methods. Atudents should master concepts and approaches such as
to tackle search and retrieval problems and their application to digital media. They should be able to understand typical problems faced when developing retrieval systems, to compare different approaches suited to the different components of such systems, to make wellinformed decisions about the preferred approach and, if necessary, to find their own solutions to given retrieval and search problems. Students should develop an understanding of the current state of research in web search and information retrieval. With appropriate supervision, students should also be able to tackle research problems. 
Contents 

Special information  Tools: Lucene, Elasticsearch, Indri, Stanford NLP toolkit Literature:

Modul Catalog
Distributed and Secure Systems
Distributed and Secure Systems
Module Title  Distributed and Secure Systems  Module number 
 



 
Semester (optional)  Frequency  Regularity and duration  ECTS credit points  Workload [hours]  Language  Module coordinator  
 Every semester  During the semester, on a weekly basis  9  67.5 inclass, 160.00 selfstudy, 42.5 exam preparation (incl. exam). Total: 270.  English  Stefan Lucks  
 
Type and application of module  Formal requirements for participation  Examination requirements  
M.Sc. Computer Science for Digital Media  Admission to M.Sc. programme �Computer Science for Digital Media�.
See course descriptions for further requirements, if any.  The overall grade for the module is calculated as the weighted mean of the grades obtained in the component courses.
See course descriptions for the examination requirements specific to the component courses.  
 
Target qualifications  
A distributed system is a model in which components located on a network need to communicate and coordinate their actions. Security means defending a system against malicious adversaries. The goal of the module is to develop an understanding of specific challenges and approaches in orderto learn about concurrency and malice in systems. Students should
More specifically, students should acquire indepth knowledge of specific fields taught in the wider field of distributed and secure systems. The specific fields are taught in component courses (see below). It is not permissible for students already to have studied these fields in depth in a previous Bachelor�s programme. After completing the component courses, students should be able to undertake original research, or at least independent academic work, at Master�s thesis level in these specific fields. For each component course, there is a more detailed list of target qualifications.  
Contents  
See course descriptions.  
Didactic concept  
Unless otherwise specified in the description of a component course: lectures and practical sessions combined with individual and groupbased studies related to theoretical and practical aspects of the course contents. Practical sessions can include projectoriented and laboratory work based on concrete problems. Theoretical aspects can include reading, understanding and presenting recent publications. Classes consist of one 90minute lecture and one 45minute practical session per week during the semester. Postdoctoral researchers, doctoral students and teaching assistants supervise students and are available for intensive discussion and feedback.
 
Special information  
See course descriptions  
Lectures / courses included in the module (optional)  SWS / ECTS credit points (optional)  
The module consists of two of the following courses to be chosen by the students:

 
Electives
Electives
Module Title  Electives  Module number 
 



 
Semester (optional)  Frequency  Regularity and duration  ECTS credit points  Workload [hours]  Language  Module coordinator  
 Every semester  Weekly over the course of 2 semesters  24  720h  English  Stefan Lucks  
 
Type and application of module  Formal requirements for participation  Examination requirements  
M.Sc. Computer Science for Digital Media  Admission to M.Sc. programme ?Computer Science for Digital Media?.
 Varies, depending on choice of specific courses taken. Language: English (German courses may also be selected) The resulting grade of the module is calculated as the mean of the grades obtained in the component courses, weighted by the courses? ECTS credits.  
 
Target qualifications  
Students acquire indepth knowledge of specialized topics or broaden their academic knowledge. Depending on the chosen classes, students can, by taking courses from other disciplines, gain exposure to different disciplinary cultures and styles of working, methodologies and approaches, as well as firsthand experience of working in interdisciplinary teams.  
Contents  
Electives can be freely chosen from all courses offered as part of Computer Science and Media, humanities related to media, media management, media studies, architecture and urban studies, art and design, as well as advanced English courses. Students may also do a project offered by other study programs as part of their electives.
The choice of courses from other faculties with mainly Computer Science and programming topics, and of those from a Bachelor?s programme related to Computer Science, may be restricted by the examination committee.
 
Didactic concept  
Teaching and learning forms depend on the individual choice of courses, and may range from lectures and practical sessions to project work and seminars.
 
Special information  
 
Lectures / courses included in the module (optional)  SWS / ECTS credit points (optional)  

 
Intelligent Information Systems
Intelligent Information Systems
Module Title  Intelligent Information Systems  Module number 
 



 
Semester (optional)  Frequency  Regularity and duration  ECTS credit points  Workload [hours]  Language  Module coordinator  
 Every semester  During the semester, on a weekly basis  9  67.5 inclass, 160.00 selfstudy, 42.5 exam preparation (incl. exam). Total: 270.  English  Benno Stein  
 
Type and application of module  Formal requirements for participation  Examination requirements  
M.Sc. Computer Science for Digital Media  Admission to M.Sc. programme Computer Science for Digital Media.
See course descriptions for further requirements, if any.  The overall grade for the module is calculated as the weighted mean of the grades obtained in the component courses.
See course descriptions for the examination requirements specific to the component courses.  
 
Target qualifications  
The integration of software engineering, machine learning and cognition create nextgeneration information systems with intelligent behavior. These intelligent information systems are also concerned with searching, accessing, retrieving, storing and treating large collections of digital media and knowledge from multiple heterogeneous sources. The goal of the module is to acquire the relevant theoretical knowledge and practical, handson skills for successfully using, evaluating, and developing various types of intelligent information systems. Students should
More specifically, students should acquire indepth knowledge of specific fields taught in the wider field of intelligent information systems. The specific fields are taught in component courses (see below). It is not permissible for students already to have studied these fields in depth in a previous Bachelor's programme. After completing the component courses, students should be able to undertake original research, or at least independent academic work at the Master's thesis level in these specific fields. For each component course, there is a more detailed list of target qualifications.  
Contents  
See course descriptions.  
Didactic concept  
Unless otherwise specified in the description of a component course: lectures and practical sessions combined with individual and groupbased studies related to theoretical and practical aspects of the course contents. Practical sessions can include projectoriented and laboratory work based on concrete problems. Theoretical aspects can include reading, understanding and presenting recent publications. Classes consist of one 90minute lecture and one 45minute practical session per week during the semester. Postdoctoral researchers, doctoral students and teaching assistants supervise students and are available for intensive discussion and feedback.  
Special information  
See course descriptions  
Lectures / courses included in the module (optional)  SWS / ECTS credit points (optional)  
The module consists of two of the following courses to be chosen by the students:

 
Graphic and Interactive Systems
Graphic and Interactive Systems
Module Title  Graphic and Interactive Systems  Module number 
 



 
Semester (optional)  Frequency  Regularity and duration  ECTS credit points  Workload [hours]  Language  Module coordinator  
 Every semester  During the semester, on a weekly basis  9  67.5 inclass, 160.00 selfstudy, 42.5 exam preparation (incl. exam). Total: 270.  English  Charles Wuethrich  
 
Type and application of module  Formal requirements for participation  Examination requirements  
M.Sc. Computer Science for Digital Media  Admission t M.Sc. programme �Computer Science for Digital Media�.
See course descriptions for further requirements, if any.  The overall grade for the module is calculated as the weighted mean of the grades obtained in the component courses.
See course descriptions for the examination requirements specific to the component courses.  
 
Target qualifications  
Interactive Systems have become ubiquitous nowadays: they require deep knowledge of computer graphics, visualization and imaging methods, as well as a deep knowledge of interaction techniques and principles. The goal of the module is to develop an understanding of specific challenges and approaches in graphical and interactive systems, from both the graphical and interaction pointofview. Students should
More specifically, students should acquire indepth knowledge of specific fields taught in the wider fields of graphical and interactive systems. The specific fields are taught in component courses (see below). It is not permissible for students already to have studied these fields in depth in a previous Bachelor�s programme. After completing the component courses, students should be able to undertake original research, or at least independent academic work at the Master�s thesis level in these specific fields. For each component course, there is a more detailed list of target qualifications.  
Contents  
See course descriptions.  
Didactic concept  
Unless otherwise specified in the description of a component course: lectures and practical sessions combined with individual and groupbased studies related to theoretical and practical aspects of the course contents. Practical sessions can include projectoriented and laboratory work based on concrete problems. Theoretical aspects can include reading, understanding and presenting recent publications. Classes consist of one 90minute lecture and one 45minute practical session per week during the semester. Postdoctoral researchers, doctoral students and teaching assistants supervise students and are available for intensive discussion and feedback.
 
Special information  
See course descriptions  
Lectures / courses included in the module (optional)  SWS / ECTS credit points (optional)  
The module consists of two of the following courses to be chosen by the students:

 
Modelling
Modelling
Module Title  Modelling  Module number 
 



 
Semester (optional)  Frequency  Regularity and duration  ECTS credit points  Workload [hours]  Language  Module coordinator  
 Every semester  During the semester, on a weekly basis  9  67.5 inclass, 160.00 selfstudy, 42.5 exam preparation (incl. exam). Total: 270.  English  Andreas Jakoby  
 
Type and application of module  Formal requirements for participation  Examination requirements  
M.Sc. Computer Science for Digital Media  Admission to M.Sc. programme �Computer Science for Digital Media�.
See course descriptions for further requirements, if any.  The overall grade for the module is calculated as the weighted mean of the grades obtained in the component courses.
See course descriptions for the examination requirements specific to the component courses.  
 
Target qualifications  
A model is concept which is used to understand a subject of a system. It is formed after a process of conceptualization and generalization. The goal of the module is to develop an understanding of specific principles in algorithm design and mathematical models. Students should
More specifically, students should acquire indepth knowledge of specific fields taught in the wider field of modelling. The specific fields are taught in component courses (see below). It is not permissible for students already to have studied these fields in depth in a previous Bachelor�s programme. After completing the component courses, students should be able to undertake original research, or at least independent academic work at the Master�s thesis level in these specific fields. For each component course, there is a more detailed list of target qualifications.  
Contents  
See course descriptions.  
Didactic concept  
Unless otherwise specified in the description of a component course: lectures and practical sessions combined with individual and groupbased studies related to theoretical and practical aspects of the course contents. Practical sessions can include projectoriented and laboratory work based on concrete problems. Theoretical aspects can include reading, understanding and presenting recent publications. Classes consist of one 90minute lecture and one 45minute practical session per week during the semester. Postdoctoral researchers, doctoral students and teaching assistants supervise students and are available for intensive discussion and feedback.
 
Special information  
See course descriptions  
Lectures / courses included in the module (optional)  SWS / ECTS credit points (optional)  
The module consists of two of the following courses to be chosen by the students:

 
Research Project I and II
Research Project I and II
Module Title  Research Project I and II  Module number 
 



 
Semester (optional)  Frequency  Regularity and duration  ECTS credit points  Workload [hours]  Language  Module coordinator  
 Every semester  Over course of one semester  15  45h in organized meetings/ classes and 405h selfstudy. Total: 450h  English  Respective Professorship  
 
Type and application of module  Formal requirements for participation  Examination requirements  
M.Sc. Computer Science for Digital Media Can be open to M.Sc HCI  Admission to M.Sc programme �Computer Science for Digital Media�.
See course descriptions for further requirements, if any.  Completion of a body of work and its documentation, usually in the form of a scientific report. Specific criteria for evaluation will be announced in the course catalogue and at the beginning of the individual project. Quality of the presentation, results achieved, autonomy in work and creativity are important factors.  
 
Target qualifications  
Depending on the type of project, students should have gained practical experience with the design, implementation and evaluation of advanced software systems and their user interfaces. Students may also have gained practical experience in designing, planning and running user studies related to specific user interface technologies.
Participants refine their presentation skills via independent literature research based on current publications and presentations on the various aspects and milestones of the project. An evaluation and documentation of the results in the form of a scientific report completes the project. As a result of various types of activities involving presentations, participants have experience in presenting and explaining their work in oral and written form. They understand the importance of project management and organisation for complex projects and are accustomed to acquiring new skills and knowledge in selfstudy.
Projects require considerable autonomy from students and develop social and general transferable skills via group work and independent research (team work, selforganisation, project management).
 
Contents  
Depends on individual topic
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 software systems and their user interfaces with a particular emphasis on teamwork. Projects may also focus on designing, planning and running user studies related to specific user interface technologies.
Projects will often produce a body of practical work or a working system, and a scientific report, or may predominantly result in a more indepth scientific report.
 
Didactic concept  
Projects confront students with complex problems of scientific relevance and require, as well as develop autonomy and creativity, problemsolving skills and team work. They are at the core of the Bauhaus tradition of teaching. Typically, project teams meet once a week with the supervising professors and their research assistants. The majority of effort consists of autonomous selfstudy.
 
Special information  
Projects on offer are announced in the teaching catalogue for each semester and presented at the project fair at the start of the semester.  
Lectures / courses included in the module (optional)  SWS / ECTS credit points (optional)  
(none) 
 
Specialization
Specialization
Module Title  Specialization  Module number 
 



 
Semester (optional)  Frequency  Regularity and duration  ECTS credit points  Workload [hours]  Language  Module coordinator  
 Every semester  During the semester, on a weekly basis  9  67.5 inclass, 160.00 selfstudy, 42.5 exam preparation (incl. exam). Total: 270.  English  Stefan Lucks  
 
Type and application of module  Formal requirements for participation  Examination requirements  
M.Sc. Computer Science for Digital Media  Admission to M.Sc. programme �Computer Science for Digital Media�.
See course descriptions for further requirements, if any.  The overall grade for the module is calculated as the weighted mean of the grades obtained in the compenent courses.
See course descriptions for the examination requirements specific to the component courses.  
 
Target qualifications  
Students shall acquire indepth knowledge of specialist topics from Computer Science and its application to Digital Media.
Most of courses offered for the modules Modelling, Graphic and Interactive Systems, Intelligent Information Systems and Distributed and Secure Systems are also open to the Specialization module. The student can pick two such courses,which have not been taken for any of the above modules.
 
Contents  
See course descriptions.  
Didactic concept  
Unless otherwise specified in the description of a component course: lectures and practical sessions combined with individual and groupbased studies related to theoretical and practical aspects of the course contents. Practical sessions can include projectoriented and laboratory work based on concrete problems. Theoretical aspects can include reading, understanding and presenting recent publications. Classes consist of one 90minute lecture and one 45minute practical session per week during the semester. Postdoctoral researchers, doctoral students and teaching assistants supervise students and are available for intensive discussion and feedback.
 
Special information  
See course descriptions  
Lectures / courses included in the module (optional)  SWS / ECTS credit points (optional)  

 
Master's Thesis Module
Master's Thesis Module
Module Title  Master's Thesis Module  Module number 
 



 
Semester (optional)  Frequency  Regularity and duration  ECTS credit points  Workload [hours]  Language  Module coordinator  
 Every semester  Any time, 4 months  33  790h in selfstudy, 20h in meetings with the supervisor, and 180h for the defence and its preparation (1h for the defence itself). Total: 990.  English  Respective professorship  
 
Type and application of module  Formal requirements for participation  Examination requirements  
M.Sc. Computer Science for Digital Media  At least 60 ECTS of the Master's programme have to be successfully completed. English proficiency at C1 level (CERT).  Written thesis in the style of an academic publication (weight 80%) and a related defence (weight 20%)  
 
Target qualifications  
In the thesis, the students prove their ability to perform independent academic work in Computer Science on an adequately challenging topic within a given time frame. They use established methods or adapt existing approaches while adhering to standards of academic work. They are given the opportunity to develop, refine and formulate their own ideas and work critically with the literature.
 
Contents  
Depends on individual topic
 
Didactic concept  
The module conists of three phases
The students work largely independently, with regular intermediate reporting and consultation with the supervisor.
 
Special information  
The final thesis is the most important part of the module. It describes the results as well as the path that led to these results. The thesis should be written in the style of an academic publication, whereby the student's own contribution to the results should be clearly evident. The evaluation of the thesis comprises a grade for the written thesis (weighted at 80%) and a combined grade for the presentation and the related defence (weighted at 20%).
 
Lectures / courses included in the module (optional)  SWS / ECTS credit points (optional)  

 