Modules in English for Exchange Students in the Summer Semester

Exchange students may select their modules for a study stay in the summer semester out of this module catalogue. One ECTS point requires 30 hours a 45min student´s workload for attending lectures, seminars, laboratory work and expected self studies time.

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Advanced Graph Theory and Network Algorithms (M.Sc.)

Learning objectives:

The course covers combinatorial aspects as well as applications of modern graph theory. The student will learn how to prove re-sults in graph theory and how to apply graph theoretic concepts in different areas of application such as computer science, statistical physics, or communication technology.

Syllabus:

  • Connectivity in undirected graphs
  • Graph isomorphism, graph invariants
  • Distances in graphs
  • Independence and domination
  • Vertex and edge coloring of graphs
  • Graph polynomials
  • Graph classes: chordal graphs, partial k-trees
  • Graph algorithms

Lecturers: Prof. Peter Tittmann

Contact hours per week: 4

ECTS Credits: 6

Course: Applied Mathematics for Network and Data Sciences, 1st semester

Faculty: Applied Computer Sciences & Biosciences

Business Expansion (M. Sc.)

Learning objectives:

This course offers an advanced applied examination of the techniques and tools of the strategic management process of business expansion. Students acquire comprehensive knowledge of modern methods, measures and tools and apply these instruments for an advanced external and internal company analysis and its environment.On the external analysis level students learn to analyze industry trends, to recognize types of industries, to develop strategic maps of industry competitors, and to utilize business information systems. Students are required to conduct an in-depth analysis of certain industries. Applying Internal capability analysis students will develop a profound understanding of techniques for analyzing a company's value chain, or business processes, and resources,among others. In a second step, students learn how this information is to be used in the strategic management process to generate strategic options and for further steps in strategic decision making. The seminar type course will be supported by case study series that provide the students application-oriented content on a highly specialized level. Special focus is given to the critical awareness of howto select relevant data from non-relevant data in the interplay between data analysis and the strategic outline of a company or an industry.After completion the course participants are able to critically review or develop strategy of business expansion and translate it into actions use management know how and economic skills.

Syllabus:

Students learn:

  1. Conducting the environment analysis using established andmodern tools (e.g. Five Forces, Industry lifecycle, Key externalsuccess factor assessment)
  2. Analyzing the company (e.g. stages theory, 3C's model ofOhmae, value chain analysis, benchmarking, core competenceassessment, business model, key internal success factor assessment)
  3. Strategic options for business expansion (e.g. Ansoff growthmatrix, SWOT)
  4. Planning strategies and implementation (e.g. organizationalstructure, KPIs, management reviews, stakeholder matrix)

Lecturers: Prof. Dr. rer. oec. Serge Velesco (course director)

Contact hours per week: 4

ECTS Credits: 5

Course: Industrial Management, 2nd semester

Business Planning (M. Sc.)

Learning objectives:

Business plans for pursuing concrete company concepts are todaypart of the "standard tool-kit" for successful idea management.Each student should be taught how to structure and pursue a projector business idea systematically and comprehensively from thedefinition of the objective to its implementation. This requires bothtechnical and economic knowledge and approaches. The final resultof the business plan is a written company concept, from which onecan on the one hand estimate the marketability (USPs, customeruses and sales changes) of a service or product which can be specifiedquantitatively and qualitatively. On the other hand, the businessidea should also be hedged in terms of organisation and financesand realised on the market/in the company. Ultimately, thefinished business plan should also be approved in terms of its implementabilityand its potential for risks and development so as tobe able to verify its suitability for real-life implementation.

Syllabus:

Each completed business plan, as well as the content, shall in principlebe organised and defined as follows:

  1. Presentation of project or business idea
  2. Market estimation
  3. Service offer/portfolio
  4. Organisation and management
  5. Financial and success planning
  6. Schedule and success controlling
  7. Chance and risk assessment8. Executive summary

    Lecturers: Prof. Dr. rer. oec. Johannes Stelling (course director), Prof. Dr. rer. pol. Andreas Hollidt, Prof. Dr. rer. oec. Volker Tolkmitt

    Contact hours per week: 4

    ECTS Credits: 5

    Course: Industrial Management, 2nd semester

     

    Computational Intelligence II and Machine Learning II (M.Sc.)

    Learning objectives:

    The course provides advanced principles and algorithms in CI and dis-cusses their realization. Additionally, students will start to study recent articles in the field, give short talks about recent developments and learn to communicate own ideas and problem solutions.

    Syllabus:

    • Convergence and stability of algorithms,
    • information theoretic learning,
    • statistical learning theory and kernel methods,
    • metric adaptation and feature selection,
    • life-long learning, deterministic and simulated annealing,
    • evolutionary algorithms, modern heuristics.

      Lecturers: Prof. Dr. Thomas Villmann

      Contact hours per week: 4

      ECTS Credits: 6

      Course: Applied Mathematics for Network and Data Sciences, 3rd semester

      Faculty: Applied Computer Sciences & Biosciences

      Corporate Social Responsibility (CSR) (B.A. / B.Sc. / B.Eng. / M.Sc. / M.A.))

      Learning objectives:

      To instill the basic knowledge of how companies can integrate social and ecological aspects into business models. Students will study corporate social responsibility activities in developing countries, their impact and the outcome on society and business. Students will raise their awareness of CSR through active participation through analysis, evaluation and development of case studies that explain in detail particular issues related to the development-oriented corporate social responsibility discussion and options to improve the sustainability of companies and develop recommendations for their implementation in practice.

      Syllabus:

      Students learn:

      1. Corporate Social Responsibility Perspective
      2. Current Trends, Challenges and Opportunities in Sustainable Development
      3. Establishing, Communicating and Implementing Strategy  Mechanisms

        Lecturers: Jill Deschner-Warner MS, MA, MA

        Contact hours per week: 4

        ECTS Credits:

         

        Cryptanalysis (M.Sc.)

        Learning objectives:

        Conveying up-to-date knowledge and advanced methods on cryptanal-ysis; ability for independent acquisition of new knowledge; mastery of the international jargon

        Syllabus:

        • Attack scenarios
        • Models and statements on the security of cryptographic methods Statistical Methods in cryptanalysis
        • Linear and differential cryptanalysis Dictionary attacks
        • Side channel attacks
        • Password recovery (GPU-based, CUDA)
        • Algebraic and number-theoretic methods
        • Applications and real-world examples

          Lecturers: Prof. Dr. K. Dohmen

          Contact hours per week: 4

          ECTS Credits: 6

          Course: Applied Mathematics for Network and Data Sciences, 2nd semester

          Faculty: Applied Computer Sciences & Biosciences

          Film & TV Production 2 (B. A.)

          Learning objectives:

          The teaching module Film & TV Production II covers the basics concepts of creating professional television studio productions. It builds upon the skills and knowledge of electronic news gatherings attained in the previous modules. The aim is to further expand and promote skills in the organizational production, management, design, workflow, conception and creation of studio productions. This professional expertise includes basics skills for television production workflows, TV journalism and production management. The module is based on a synthesis of academic theory and well-founded production practice, which enables students to internalize and apply the particular characteristics of the industry.

          Syllabus:

              The essentials of a television studio production will be conveyed in the lecture Film & TV Production II. The focus will be on the structure, procedures, directing and design of a television studio production. Traditional television genres as well as new forms and formats will be presented and discussed, with a focus on their respective characteristics and production requirements. Students will learn the principals of disposition, production management, budget planning, design, dramaturgy, conception, and line- and executive production regarding television studio productions. In the practical part of the course, students will transfer this know-how to real productions, and apply and reflect upon their knowledge.

                Lecturers: Amrhein, Christof, Prof. Dipl.-Ing. (FH)

                Contact hours per week: 4

                ECTS Credits: 5

                Course: Media Management

                Faculty: Media

                Finite Element Analysis / 03-STENG Selected Topics in Engineering Science (Diplom, M. Sc., M. Eng.)

                Learning objectives:

                The student is able to independently solve complex tasks by using the method of Finite Elements and apply this knowledge in new fields.

                Syllabus:

                    This course introduces finite element methods for the analysis of solid, structural, fluid, field, and heat transfer problems. Steady-state, transient, and dynamic conditions are considered. Finite element methods and solution procedures for linear and nonlinear analyses are presented using largely physical arguments. Applications include finite element analyses, modeling of problems, and interpretation of numerical results.

                      Lecturers: Prof. Dr.- Ing. Frank Weidermann

                      Contact hours per week: 4

                      ECTS Credits: 5

                      Faculties: Industrial Engineering, Applied Computer and Bio Sciences

                      Foundations of Modern Cryptography (M.Sc.)

                      Learning objectives:

                      Conveying a very deep understanding of the operation and safety of asymmetric cryptographic methods; imparting current research-related knowledge and methods; key skills; sharpening of programming skills

                      Syllabus:

                      • Computational number theory
                      • Public-key cryptosystems based on factoring and logarithms
                      • Cryptosystems based on NP-hard problems
                      • Digital signature schemes, DSS
                      • Elliptic curve cryptography

                      In the seminar, the students present solutions to weekly exercices. An interactive learning environment is used to experience the concepts introduced in the lecture. Furthermore, methods presented in the lecture will be implemented using the Python programming language and the computer algebra system Sage.

                            Lecturers: Prof. K. Dohmen, Prof. P. Tittmann

                            Contact hours per week: 4

                            ECTS Credits: 6

                            Course: Applied Mathematics for Network and Data Sciences, 2nd semester

                            Faculty: Applied Computer Sciences & Biosciences

                            Functional Mathematical Models for Machine Learning (M.Sc.)

                            This module is an elective subject. It depends on the number of partcipants, if it will be offered or not. Please ask for the recent situation.

                            Lecturers: Prof. Dr. Thomas Villmann, Prof. Dr. Franka Baaske

                             

                            descriptions follows

                            Image Processing and Machine Vision (02-IPMV-20)

                            Learning Objectivs:

                            Goal of this course is to make the students familiar with the foundations of digital image and video processing and their application in video analysis. Starting from the physical basics and key components of digital image and video recording and compression systems, standard image and video processing tasks and the used algorithms are studied first. Based on this advanced techniques which can among others be applied in face detection, video forensics and autonomous systems are introduced. Finally, an introduction into machine learning methods for image processing is given.

                            The students are enabled to analyze, specify and design, implement as well as simulate, evaluate and optimize image and video processing algorithms and systems.

                            Syllabus:

                            Topics of this course are among others:

                            • Physical basics of image representation and recording
                            • Key components of digital image and video processing and compression systems
                            • Standard image manipulations applying e.g. point and morphological operations, affine transformations, contrast adjustment
                            • Linear and non-linear filters
                            • Transformations used in image processing
                            • Interest point detection techniques
                            • Image compression and representation
                            • Implementation aspects
                            • Applications of classicial image processing algorithms for face detection, video forensics and autonomous systems
                            • Introduction into machine learning for image processing

                            Lecturers: Prof. Dr.-Ing. Alexander Lampe, Markus Süß, M.Schttps://www.intranet.hs-mittweida.de/nsoft/his/dozenten/dozent.info.asp?id=4955

                            Contact hours per week: 4

                            ECTS Credits: 5

                            Courses: Electrical Engineering & Automation, 2nd semester

                            Faculty: Engineering Sciences

                            Innovation Management (M. Sc.)

                            Learning objectives:

                            Participants should be able to use management know how and economic skills to understand, to develop and to support the full innovation process in enterprises. They can adopt management instruments and tools in research and development, the generation and protection of IP and the realization of products. They should be able to plan, to carry out and to control the management and financing of innovation processes.

                            Syllabus:

                              Students learn:

                              1. Understanding of the innovation process as one key for the success of companies.
                              2. Technological and scientific skills to create and manage an invention
                              3. Generation and protection of IP (patent recherche and writing)
                              4. Launching of new products / pilot production for market entrance
                              5. Implementation of industrial production and sales structures,ramp-up processes, cost-of-ownership calculations
                              6. R&D controlling, quality management and risk analysis during product development cycles

                                Lecturers: Prof. Dr. rer. nat. Thoralf Gebel (course director) & Prof. Dr. rer. oec. Volker Tolkmitt

                                Contact hours per week: 4

                                ECTS Credits: 5

                                Course: Industrial Management, 2nd semester

                                 

                                International Management (M. Sc.)

                                Learning objectives:

                                After completion of all courses of this module, students should beable to understand, evaluate and develop strategies and tactics ofMNEs/SMEs in international markets. The module will enable studentsto understand socio-economic conditions of a rapidly changingglobal business environment. Students will be able to analyze,differentiate and prioritize international markets (countries, regions)according to their market potential, political situation, risks and otherrelevant factors. Based on analysis they should be able to drawconclusions on how these markets can be developed using appropriatestrategies and entry forms. Students raise their awareness offoreign cultures and their practices (customs, values, in particular inthe business of life) what helps them to enter into successful internationalcooperation and global relations. Students also create ability(get competence) to consult SME in international business activities:develop strategies, build-up global organization, conduct peoplemanagement across countries, and adopt marketing and operationfor foreign regions and countries. As case studies are integrativepart of this module negotiating skills and teamwork are alsotrained.

                                Syllabus:

                                Students learn:

                                1. Evaluate regions and countries
                                2. Develop global enterprise strategy
                                3. Understand specialty about international management for organizational structures, people management, marketing, operations

                                  Lecturers: Prof. Dr. rer. oec. Serge Velesco (course director)

                                  Contact hours per week: 3

                                  ECTS Credits: 5

                                  Course: Industrial Management, 2nd semester

                                   

                                  Introduction to Programming (B. Sc.)

                                  Learning objectives:

                                  The students learn how to use a first programming language. The focus is on mathematical problems that must be solved algorithmically on the computer.

                                  Syllabus:

                                  Python interactive shell, functional programming, process management, classes and objects, persistence and databases, network programming, web programming, threads and concurrency, mathematical libraries

                                      Lecturers:

                                      Prof. Dr. rer. nat. Klaus Dohmen (Inhaltverantwortlicher, Prüfer), Dipl.-Informatiker (FH) Daniel Stockmann (Dozent, Prüfer)

                                      Contact hours per week: 4

                                      ECTS Credits: 5

                                      Course: Applied Mathematics, 2nd semester

                                      Faculty: Applied Computer Sciences & Biosciences

                                       

                                      Logistics (M. Sc.)

                                      Learning objectives:

                                      The module aims at understanding the systematic description of thebehavior of Manufacturing Systems and further Supply Chains. Itenables students to analyze existing systems, understand theirnatural tendencies, identify opportunities for improving such systemsand design new systems. Manufacturing is the production ofphysical goods (and related services) and includes, for example,process development, plant design, capacity management, workforceorganization and supply chain management. Students will beable to manage the flow of material through a plant which refers tothe application of resources (materials, workstations, staff, technology,capital). This module also provides an introduction to the useof computer simulation in studying Manufacturing Systems. Studentswill learn the principles of Manufacturing Systems in a playfulmanner. Case studies and independent projects are integrative partof this module.

                                      Syllabus:

                                      Students learn:

                                      1. Analyze and design Manufacturing Systems
                                      2. Understand modern manufacturing processes
                                      3. Gain the knowledge on how to evaluate and manage supplychains to achieve overall efficiency and effectiveness
                                      4. Use of computer simulation in manufacturing and logistics systems

                                        Lecturers: Prof. Dr. rer. pol. Gunnar Köbernik (course director)

                                        Contact hours per week: 4

                                        ECTS Credits: 5

                                        Course: Industrial Management, 2nd semester

                                         

                                        Marketing Research (M. Sc.)

                                        Learning objectives:

                                        In this course students develop an understanding of marketing research and its relevance to management decision-making. They acquire comprehensive knowledge about the marketing research process including problem definition, research design and methodology,sampling procedure, data collection, data analysis, and reporting the findings. They develop the ability to apply key research techniques. Students are required to design and implement a marketing research plan, develop a research instrument, collect and analyse data, prepare an oral presentation and write a marketing research report.After completing the course participants are able to systematically appraise the different stages of a marketing research project. They should have the ability to critically assess marketing research in the context of understanding and evaluating the market.

                                        Syllabus:

                                        Students learn:

                                        1. Introduction to Marketing Research: The Marketing ResearchProcess and problem definition
                                        2. Key research techniques
                                        3. Secondary research and conducting a literature review
                                        4. Qualitative research and Quantitative research methods
                                        5. Measurement and scaling6. Questionnaire design
                                        6. Sampling
                                        7. Data Collection
                                        8. Qualitative Data Analysis and quantitative data analysis
                                        9. Producing marketing research reports
                                        10. Ethical issues in marketing research Students will apply the key concepts and principles of marketing research to a real world project

                                          Lecturers: Dr. Julia Köhler (course director) & Prof. André Schneider

                                          Contact hours per week: 4

                                          ECTS Credits: 5

                                          Course: Industrial Management, 3rd semester

                                           

                                          Mathematical Logic (M.Sc.)

                                           

                                          This module is an elective subject. It depends on the number of partcipants, if it will be offered or not. Please ask for the recent situation.

                                          Learning objectives:

                                          After completing the module, students will be able to formalize complex problems using propositional and predicate logic, and to solve these problems algorithmically. Through the study of English literature as well as exercises and contributed talks, students will be able to communicate their own ideas and solutions using the mathematical jargon. They will be able to formulate, prove or disprove mathematical hypotheses.

                                          Syllabus:

                                          • Propositional logic,
                                          • Predicate Logic,
                                          • Automated Theorem Proving,
                                          • Logic Programming,
                                          • Undecidability.

                                          In the seminar, weekly exercises are posed, the solution of which are presented by the students. In addition, each student gives a contrib-uted talk at the seminar which lasts 30 minutes.

                                            Lecturers: Prof. Dr. K. Dohmen

                                            Contact hours per week: 4

                                            ECTS Credits: 6

                                            Course: Applied Mathematics for Network and Data Sciences, 1st or 3rd semester

                                            Faculty: Applied Computer Sciences & Biosciences

                                            Mathematical Project (B.Sc.)

                                            Learning objectives: The students learn to work on a narrowly limited topic from applied mathematics with practical relevance.

                                            Syllabus: The course content is subject-related.

                                            Literature: will be announced on an individual basis

                                            Lecturers: depending on the topic:
                                            Prof. Dr. rer. nat. Klaus Dohmen
                                            Prof. Dr. rer. nat. habil. Thomas Villmann
                                            Prof. Dr. rer. nat. Cordula Bernert
                                            Prof. Dr. rer. nat. Peter Tittmann
                                            Prof. Dr. rer. nat. Franka Baaske

                                            Contact hours per week: Practical, independent work over 180 hours per semester, consisting of self studies time and consultation time.

                                            ECTS Credits: 5

                                            Course: Applied Mathematics (B.Sc.): You need to attend the third semester of your study programme or higher.

                                            Mathematical Seminar (B. Sc.)

                                                Learning objectives:

                                                Students learn to familiarize themselves with a topic using a book chapter or an original paper and to give a lecture on it.

                                                Syllabus:

                                                The contents depend on the specific topic, e.g., topics from discrete mathematics and computational intelligence.

                                                Lecturers:

                                                Prof. Dr. rer. nat. Peter Tittmann (Dozent, Prüfer), Prof. Dr. rer. nat. Franka Baaske (Dozent, Prüfer), Prof. Dr. rer. nat. habil. Thomas Villmann (Dozent, Prüfer) & Prof. Dr. rer. nat. Klaus Dohmen (Dozent, Inhaltverantwortlicher, Prüfer)

                                                Contact hours per week: 4

                                                ECTS Credits: 5

                                                Course: Applied Mathematics, 4th semester

                                                Faculty: Applied Computer Sciences & Biosciences

                                                 

                                                Numerical Mathematics (B. Sc.)

                                                    Learning objectives:

                                                    The course introduces students to the algorithms and methods that are commonly needed in scientific computing

                                                    Syllabus:

                                                    Error Analysis, Numerical Methods for Solving Nonlinear Equations and Systems of Linear Equations, Reconstruction of Functions, Numerical Integration, Numerical Solutions of Ordinary Differential Equations

                                                    Lecturers:

                                                    Prof. Dr. rer. nat. Franka Baaske 

                                                    Contact hours per week: 7

                                                    ECTS Credits: 10

                                                    Course: Applied Mathematics, 4th semester

                                                    Faculty: Applied Computer Sciences & Biosciences

                                                    Organisationspsychologie (H.65532)

                                                    Learning objective:

                                                    The aim of this module is to teach students the basics of rganisational psychology. Here, students should gain an insight into various areas and central questions of organisational psychology. They  should be familiar with research methods in this field, and should understand organisational psychology as applied psychology, withall its opportunities for intervention but also its limits.As well as teaching specific technical knowledge, this module will serve in particular to establish and expand methodological and social  competences. Roleplays and other interactive forms of teaching offer the opportunity for students to test themselves out in different roles in team and group exercises.

                                                    Syllabus:

                                                    The organisational psychology lecture (2 contact hours) offers an overview of the basic elements of organisational psychology. Here,organisational structures and the types of people found in these will be explained. Moreover, focus topics such as changes in organisations,and psychological aspects of the group and the individual inselection and development of personnel will also be looked at. Concepts on typical methods of personnel development in organisationsand methods of employee surveying will also be described and explained. In addition, students will learn the forms of group work and the areas of applications of these in practice.The Teams and Groups in Organisations seminar (2 contact hours)should illustrate to students the processes taking place in teams and groups, and the effect of such on organisations. The topics of processes of social influence, social identity in groups, conflicts in and between groups, and communication and decision-making in groups will play a key role here. Students should be confronted with approaches to team development and should be taught how to assess the climate within a team.

                                                    Lecturers: Jill Deschner-Warner MS, MA, MA

                                                    Contact hours per week: 4

                                                    ECTS Credits: 5

                                                    Course: Industrial Management, 2nd semester

                                                    Probability and Statistics (B. Sc.)

                                                        Lecturers: Prof. Dr. rer. nat. habil. Thomas Villmann, Dr. David Nebel

                                                        Contact hours per week: 7

                                                        ECTS Credits: 10

                                                        Course: Applied Mathematics, 4th semester

                                                        Faculty: Applied Computer Sciences & Biosciences

                                                         

                                                        Programming Project (M.Sc.)

                                                        Learning objectives:

                                                        Consolidation of programming skills; ability to use documentation tools; independent scientific work; ability to work in interdisciplinary teams; ability to communicate mathematical content in speech and writing; ability to think conceptually, logically and algorithmically; ability to communicate using the international jargon on a high scale of expertise.

                                                        Syllabus:

                                                          • Problem Analysis,
                                                          • Preparing team work,
                                                          • Creating a project plan,
                                                          • Definition of programming interfaces,
                                                          • Algorithmization,
                                                          • Programming,
                                                          • Testing and evaluating,
                                                          • Use of documentation tools,
                                                          • Presentation of intermediate results,
                                                          • Final Presentation.

                                                          In the accompanying seminar, students regularly report on the pro-gress of the project.

                                                            Lecturers: Villmann Prof.,Tittmann Prof.,Nebel D.,

                                                            Contact hours per week: 4

                                                            ECTS Credits: 6

                                                            Course: Applied Mathematics for Network and Data Sciences, 1st or 3rd semester

                                                            Faculty: Applied Computer Sciences & Biosciences

                                                            Research and Development Project (M. Sc.)

                                                            Learning objectives:

                                                            The research/development project serves to strengthen and expand on theoretical knowledge through independent academic work.The focus is on shaping skills and abilities to work across subjects in application-level research and/or development.

                                                            Syllabus:

                                                              Application of management techniques acquired in planning, processing, documenting and defending and academic task closely related to the selected subject specialisation.

                                                              Teaching methods:

                                                              The students will select a task from an annually updated subject catalogue containing the latest academic projects, or will suggest a topic of his own choosing from an area outside of the college. In implementing the project, he will be monitored by an academic supervisor/tutor. The tutor will offer initial orientation (current literature, research methods, framework conditions to be observed) and con-firm the technical approach of the project. He shall be available for operative decisions relating to the successful technical implementation of the project for a short while.

                                                                Lecturers: Subject supervisor according to selected specialisation

                                                                ECTS Credits: 20

                                                                Course: Industrial Management, 3rd semester

                                                                 

                                                                Risk Management and Venture Capital Enterprise (M. Sc.)

                                                                Learning objectives:

                                                                Participants should be able to apply management know how andeconomic skills for financial risk analysis and risk evaluation. Studentsacquire comprehensive knowledge about the risk managementprocess. They develop the ability to apply management instrumentsand tools in risk management. Finally, graduates shouldbe able to use methods and instruments of financial risk identification,-measurement and evaluation. They should be able to managefinancial risks as well as risk capital in enterprises.

                                                                Syllabus:

                                                                Students learn:1. importance of risk management as management process.2. phases, methods and instruments of risk managementprocessesin general and in venture capital enterprises.3. identification of financial risks and application of venture capitalinstruments.4. Managing risk capital and financial risks in venture capital enterprises.

                                                                  Lecturers: Prof. Dr. rer. oec. Serge Velesco (course director)

                                                                  Contact hours per week: 4

                                                                  ECTS Credits: 5

                                                                  Course: Industrial Management, 2nd semester

                                                                   

                                                                  Screenwriting 2 (B.A.)

                                                                  Learning objectives:

                                                                  The goal of this course is to equip students with mathematical techniques and practical skills that are useful for the design and analysis of algorithms, for the investigation of large data sets, and for the analysis and optimization of computer and information systems. This class also provides the student with the theoretical foundation necessary to effectively perform research in computer science.

                                                                  Syllabus:

                                                                  • Modelling structures: finding appropriate data structures and representations.
                                                                  • Basic methods of analysis of algorithms, time and space requirements, limits of computation.
                                                                  • In addition a selection of topics from complexity theory, finite automata, language theory, algebraic methods in computer science, mathematical logic, and cryptography is presented.
                                                                  • Applications in science and industries.

                                                                  Lecturers: Prof. Dr. P. Tittmann & Prof. Dr. K. Dohmen

                                                                  Contact hours per week: 4

                                                                  ECTS Credits: 6

                                                                  Course: Applied Mathematics for Network and Data Sciences, 1st or 3rd semester

                                                                  Faculty: Applied Computer Sciences & Biosciences

                                                                   

                                                                  Selected Topics in Discrete Mathematics (M.Sc.)

                                                                  This module is an elective subject. It depends on the number of partcipants, if it will be offered or not. Please ask for the recent situation.

                                                                  Learning objectives:

                                                                  Deep investigation of a branch of discrete mathematics, e.g., enumerative combinatorics, extremal combinatorics or randomized algorithms. On a weekly basis, complex exercises are posed, the solutions of which are presented by the students in the seminar. The tasks for each student include a short presentation of 45 minutes duration on current topics.

                                                                  Syllabus:

                                                                  • Propositional logic,
                                                                  • Predicate Logic,
                                                                  • Automated Theorem Proving,
                                                                  • Logic Programming,
                                                                  • Undecidability.

                                                                  In the seminar, weekly exercises are posed, the solution of which are presented by the students. In addition, each student gives a contrib-uted talk at the seminar which lasts 30 minutes.

                                                                    Lecturers: Prof. Dr. K. Dohmen & Prof. Dr. P. Tittmann

                                                                    Contact hours per week: 4

                                                                    ECTS Credits: 6

                                                                    Course: Applied Mathematics for Network and Data Sciences, 1st or 3rd semester

                                                                    Faculty: Applied Computer Sciences & Biosciences

                                                                    Selected Topics in in Computational Statistics (M.Sc.)

                                                                    Learning objectives:

                                                                     

                                                                    Students expand their knowledges in statistical methods, in theory and practice. In particular, the students acquire the ability to inde-pendently plan studies and carry out the statistical analysis on the computer.

                                                                    Syllabus:

                                                                    The most essential statistical procedures are discussed in theory and application (Data-handling, descriptive statistics, one- and two-sam-ple t-tests, ANOVA, and respective non-parametric counterparts, goodness-of-fit tests, linear models, power-calculations, confidence intervals, multiple testing, etc.). Furthermore, a selection of more so-phisticated statistical methods will be discussed (e.g. Bootstrap, Ap-proximate Bayesian Computation, Likelihood-methods).

                                                                      Lecturers: Prof. Dr. Kristan Schneider

                                                                      Contact hours per week: 4

                                                                      ECTS Credits: 6

                                                                      Course: Applied Mathematics for Network and Data Sciences, 2nd semester

                                                                      Faculty: Applied Computer Sciences & Biosciences

                                                                      Simulation and Visualization (M.Sc.)

                                                                      Learning objectives:

                                                                      Students acquire in-depth knowledge in simulating processes with applications in life-sciences, engineering and economy. The mentioned contents qualify participants to address and solve applied problems using simulations techniques. Students will gain skills to categorize, analyze and implement complex problems. The abilities to assess simulation procedures and to pursue interdisciplinary approaches will be fostered.Preparing a presentation about a self-chosen topic will deepen specific topics beyond the contents of the course and promote the students' ability to independently advance in related topics.In the tutorials, students will have the opportunity apply their acquired programming and software skills to independently work on simulation projects.

                                                                        Lecturers: Prof. Dr. Kristan Schneider

                                                                        Contact hours per week: 4

                                                                        ECTS Credits: 6

                                                                        Course: Applied Mathematics for Network and Data Sciences, 2nd semester

                                                                        Faculty: Applied Computer Sciences & Biosciences

                                                                        Stochastic processes with applications in signal processing (M.Sc.)

                                                                        This module is an elective subject. It depends on the number of partcipants, if it will be offered or not. Please ask for the recent situation.

                                                                        Learning objectives:

                                                                        Goal of this course is to make the students familiar with the foundations of stochastic processes and their application in signal processing. Starting from the basics of probability theory and random variables, stochastic processes are introduced and their key features are studied with focus on signal processing applications like Markov processes which are widely used in autonomous systems. The students are enabled to assess, analyze, design and specify as well as simulate signal processing systems dealing with stochastic processes.

                                                                        Syllabus:

                                                                          • Basics of probability theory and random variables
                                                                          • Continuous-time and discrete-time stochastic processes and their key parameters
                                                                          • Wide-sense-, strict-sense- and cyclo-stationary as well as ergodic stochastic processes
                                                                          • Gaussian and Markov processes
                                                                          • Generation and modelling of stochastic processes
                                                                          • Estimation and filtering of stochastic processes
                                                                          • Selected applications in signal processing

                                                                          Lecturers: Prof. Dr.-Ing. Alexander Lampe

                                                                          Contact hours per week: 4

                                                                          ECTS Credits: 5

                                                                          Courses: Electrical engineering & Autmation, Applied Mathematics for Network and Data Sciences, 3rd semester

                                                                          Faculty: Egineering Sciences, Applied Computer Sciences & Biosciences

                                                                          The Self and the Other: Cultural and Social Theories of Diversity and Othering (B. Sc., B. A.)

                                                                          Learning objectives:

                                                                           

                                                                          In this course, we will analyze different concepts like class, gender, ethnicity, religion, sexual orientation, dis/ability and the role these categories play in constructing social worlds and cultures. Moreover, we will examine how these concepts have interacted with regimes of power and have produced contested histories of oppression and discrimination, but also instances of solidarity and empathy. Furthermore, we will study how individuals construct personal and cultural identities in a complex and globalized world. Apart from discussing the work of scholars like Michael Foucault, Edward Said and others, we will look at exemplary video and audio materials from everyday and popular culture, like speeches of Donald Trump, HipHop videos, and clips from movies and TV series.

                                                                           

                                                                          Syllabus:

                                                                            By the end of the semester, students will have an understanding how the categories mentioned above influence our personal lives and have shaped cultures and histories. Furthermore, students will have an active knowledge of the most prominent cultural theories, the scholars associated with them and can use these theories in analyses of everyday 'texts' (f.i. Hollywood movies, TV series, rock and pop songs) and practice.

                                                                              Lecturers: Dr. phil. habil. Gunter Süß

                                                                              Contact hours per week: 2

                                                                              ECTS Credits: 2,5

                                                                              Theoretical Computer Science (B. Sc.)

                                                                                  Learning objectives:

                                                                                  The students acquire skills in one or more sub-areas of theoretical computer science.

                                                                                  Syllabus:

                                                                                  The course content depends on the specific topic chosen by the lecturer, e.g. Formal languages and automata theory.

                                                                                  Lecturers: Prof. Dr. Klaus Dohmen

                                                                                  Contact hours per week: 4

                                                                                  ECTS Credits: 5

                                                                                  Course: Applied Mathematics, 4th semester

                                                                                  Faculty: Applied Computer Sciences & Biosciences