Modules in English for Exchange Students in the Winter Semester

Exchange students may select their modules for a study stay in the winter semester out of this module catalogue.

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03-PMIP Projektmanagement Internationales Projekt (B.Sc.)

Learning objectives:

Gaining competences in Elaborating an actual topic of international relevance or with international partners in order to get cross cultural knowledge and understanding.

 

Syllabus:

Training of core and key competencies concerning Cultural aspects of decision processes, development methods and decision criteria, intercultural project management, Multidisciplinary project collaboration Quality management.

Lecturer: Prof. Dr. rer. pol. Petra Schmidt

ECTS Credits: 5

Course: Applied Informatics

Faculty: Applied Computer Sciences & Biosciences

 

 

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

Advanced Topics in Computer Science (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:

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. P. Tittmann & Prof. K. Dohmen & Prof. Th. Villmann

      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

      Algebra and Number Theory (B. Sc.)

      Learning objectives:

      Students get to know the basic algebraic structures of abstract algebra and are enabled to see number theory problems from an algebraic point of view. The module serves to sharpen the ability to abstract.

      Syllabus:

      modular arithmetic, groups, subgroups, cyclic groups, permutation groups, cosets, Lagrange's theorem, direct products, normal subgroups, factor groups, isomorphism theorems, group actions, Burnside's counting theorem, Sylow theorems, rings, integral domains, fields, finite fields, Galois theory

          Lecturers:

          Prof. Dr. rer. nat. Klaus Dohmen (Dozent, Inhaltverantwortlicher, Prüfer) & Dr. David Nebel

          Contact hours per week: 7

          ECTS Credits: 10

          Course: Applied Mathematics, 3rd semester

          Faculty: Applied Computer Sciences & Biosciences

           

          Analysis III (B. Sc.)

          Learning objectives:

          The third course gives an introduction to complex analysis and partial differential equations.

          Syllabus:

          1. Vector analysis - Differential operators, calculation rules, vector potentials
          2. Line and surface integrals, multiple integrals - Integral theorems (Gauss, Stokes, Green's formula)
          3. Partial differential equations - Laplace Poisson equation, Cauchy problems (heat equation, wave equation), Separation of variables (Fouriers's method), wave equation, Fourier series, heat equation, Laplace Poisson equation
          4. Complex functions - complex differentiability, complex integrability, residue theory, properties of holomorphic functions

              Lecturers: 

              Prof. Dr. rer. nat. Franka Baaske

              Contact hours per week: 7

              ECTS Credits: 10

              Course: Applied Mathematics, 3rd semester

              Faculty: Applied Computer Sciences & Biosciences

               

              Biotechnology 1 (M. Sc.)

              Learning objectives:

              General: The aim of this module is to introduce students to the basics of biotechnology.With regards to this module: the focus is on teaching basic biotechnological concepts which are essential for the understanding of later teaching units (e.g. Biotechnology II, Bio-Procedural Technology, Project Biotechnology/Bioinformatics etc..Technical/methodological/learning/social competences:The students will gain the basic technical knowledge necessary for a career in biotechnology. Selection production processes will be discussed in order to illustrate the complexity of biotechnological material production.Theoretical and practical learning of basic microbial and biotechnological methods and processes. Familiarising oneself with the literature and precise academic writing.

              Syllabus:

                • Definitions, historical development of biotechnology, areas of work in biotechnology, current academic data, advantages and disadvantages of biotechnological processes.
                • Working steps for establishing a fermentation process, maintaining/improving strains, short overview of biotechnological procedure, types of fermentation, bioreactors, scale-up, processing products).
                • Typical products of red, green and white biotechnology.
                • Biotechnological production processes for insulin, erythropoietin, hormones, citric acids, glutamine, ethanol, biopolymers, antibiotics etc.
                • Important production strains.
                • Enzymes as catalysts for household and industry (enzyme classification, manufacture, use).
                • Biotechnology in protecting and caring for the environment (deinking processes, biological cleaning of exhaust gases and earth, microbial ore extraction etc.).
                • Processes to immobilise cells and enzymes, advantages/disadvantages of using immobilised biocatalysts, typical industrial areas of application.

                  Lecturers: Prof. Dr. rer. nat. Petra Radehaus (course director)

                  Contact hours per week: 4

                  ECTS Credits: 5

                  Course: Industrial Management, 1st semester

                   

                  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

                     

                    Communication Skills (B.A., B.Sc., M.Sc.)

                    Learning objectives:

                    This module includes two parts:

                    I. Academic Standards, Writing and Presentations (in English)

                    Upon successfully completing this module, students will be able to identify and describe in English key conventions and standards of academic/scholarly work and university correspondence/campus communication at a German university in general and within their course of studies in particular. They will be capable of constructively applying the acquired skills in their everyday life as a student.

                    II. Basic German (in German)

                    Successful completion of this module will further provide students with the necessary skills to communicate effectively in German on an A1 level of the Common European Framework of Reference for Languages (CEFR) as well as to recognize and position intercultural differences. By means of these skills and insights, students are then better able to successfully navigate everyday situations and communication in their German surroundings. 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.

                     

                    Syllabus:

                    I. ACADEMIC STANDARDS, WRITING AND PRESENTATIONS

                    In this (English-language) part of the module the following topics are intro-duced, discussed and reflected upon using practical examples:

                    • structures within German universities
                    • oral and written communication/correspondence at German uni-versities (standards and conventions)
                    • academic integrity and plagiarism
                    • academic writing: register and style as well as structuring, format-ting and citations/referencing
                    • preparing and delivering presentations

                    Students apply their newly acquired skills within their individual context.

                    Contact hours per week: 2

                    I. BASIC GERMAN

                    Basic German vocabulary, key expressions and essential grammatical structures are taught inductively and used in a variety of exercises in read-ing, speaking and writing. Using the target language, students learn how to

                    • get to know and introduce each other
                    • discuss times and quantities
                    • spell words
                    • talk about languages/ family/ jobs/ travel
                    • get around on foot, by car or public transport
                    • read and compose short messages

                    Students talk about and analyse experiences with their German surroundings and develop an understanding and classification there of, finding similarities to their own sociocultural backgrounds. Among others, the follow-ing topics will be discussed:

                    • German holidays
                    • food and eating in Germany
                    • travelling Germany (Mittweida - Saxony - Germany - Europe)
                    • interpersonal communication/ networking in Germany

                    Contact hours per week: 4

                     

                    Total ECTS Credits: 6

                    Lecturers: M.A. Sarah Reader

                    Course: General Studies

                    Faculty: IKKS (Institute of Competence, Communication & Languages)

                    Computational Intelligence I (M.Sc.)

                    Learning objectives:

                    The course provides the basic principles and algorithms in CI. Particu-larly, neural networks for clustering and classification as well as Hebb learning are in the main focus. Completing the course, students are able to program basic models and to study their behavior.

                    Syllabus:

                    • Biological neurons, perceptrons, multi-layer perceptrons
                    • Hebbian learning, vector quantization
                    • Machine Learning in MATLAB: programming of machine learning models in MATLAB
                    • analysis of convergence behavior, exemplary applications.

                    Lecturers: Prof. Dr. Thomas Villmann

                    Contact hours per week: 4

                    ECTS Credits: 6

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

                    Faculty: Applied Computer Sciences & Biosciences

                    Computational Statistics (B. Sc.)

                        Lecturers:

                        M.Sc. David Nebel (Dozent, Prüfer) & Prof. Dr. rer. nat. habil. Thomas Villmann (Dozent, Inhaltverantwortlicher, Prüfer)

                        Contact hours per week: 7

                        ECTS Credits: 10

                        Course: Applied Mathematics, 5th semester

                        Faculty: Applied Computer Sciences & Biosciences

                         

                        Digital Communications (M.Sc.)

                        Learning objectives:

                        Goal of this course is to make the students familiar with the principles of modern digital data transmission systems. Starting from the information theoretic basics of digital communication, the components of digital communication systems are studied with focus on digital transmission and multiple access schemes such as OFDM.
                        The students are enabled to assess, analyze, design and specify as well as simulate digital communication systems.

                        Syllabus:

                          • Principles of digital transmission, channel models and multiple access schemes (TDMA, CDMA, OFDM)
                          • Major digital modulation schemes and their performance for var-ious channel models
                          • Transmit and receive techniques used to increase transmission diversity and minimize interference
                          • Overview and comparison of major digital transmission systems (data rates, spectral and power efficiency)
                          • Forthcoming developments, especially techniques used in 4th generation mobile communication standard and planned for 5th generation standard

                            Lecturers: Prof. Dr.-Ing. A. Lampe

                            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

                            Digital Video Analysis (M.Sc.)

                            Learning objectives:

                            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 these advanced techniques which are applied especially in video forensics and autonomous systems are introduced.
                            The students are enabled to assess, analyze, design and specify as well as simulate image and video processing systems.

                            Syllabus:

                              • 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
                              • Image and video analysis and feature detection, classification and representation using e.g. Fourier- and Wavelettransformation, integral images and self-learning classification techniques
                              • Applications of video analysis, especially forensic video analysis, face detection and recognition in videos and autonomous driving

                                Lecturers: Prof. Dr.-Ing. A. Lampe

                                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

                                Film & TV Production 1 (B. A.)

                                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:

                                    Students will receive a broad insight into professional field- and postproduction with a focus on Electronic News Gathering, and taking into account current requirements and technologies for audiovisual media. (1)Lecture "Film & TV Production"(2)Practical part: "Media Practice Film & TV-Production II"The lectures will cover theory-based content regarding basic concepts, issues and the classification of moving image productions and transmedia projects. This will include the professional handling of recording and editing equipment under consideration of technological, health and safety, artistic and legal guidelines, as well as the production processes for different output media. The seminars will mainly concentrate on applying this newly-acquired expertise, at first in exercises and, later, in specifically chosen audio-visual team projects.

                                      Lecturershttps://www.intranet.hs-mittweida.de/nsoft/his/dozenten/dozent.info.asp?id=2675

                                      Amrhein, Christof, Prof. Dipl.-Ing. (FH) (Dozent, Prüfer); Fleck, Rika, M.Sc. (Dozent, Prüfer); Jackstien, Dennis, Dipl.-Ing. (FH) Dipl.-Ing. (FH) (Dozent); Koch, Maximilian, B.Eng. (Dozent); Rohrscheidt, Christoph, Dipl.-Kameramann (Dozent); Stoev, Bony (Dozent, Prüfer)https://www.intranet.hs-mittweida.de/nsoft/his/dozenten/dozent.info.asp?id=4745

                                      Contact hours per week: 4

                                      ECTS Credits: 5

                                      Course: Media Management, 3rd semester

                                      Faculty: Media

                                      Film & TV Production 3 (B. A.)

                                      Learning objectives:

                                      The teaching module Production AV3 deepens the knowledge of professional television studio productions, and builds upon the basic skills attained in Film & TV Production 2. It transfers theoretical knowledge into production practice under scientific consideration, while reflecting upon media production as a whole in the field of television and film. As part of a reflective transfer process, the respective media-scientific, creative, legal, economic and production-related knowledge will be categorized and applied within the industry sector. The module conveys the necessary skills for producing, managing, designing and distributing complex studio productions. This expertise includes knowledge of television production workflow, television 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:

                                          During this module, students will deepen their knowledge in the field of professional television studio productions and make practical use of their know-how. In the process, they will develop their own concepts and studio formats, and produce complex moving image content for faculty events and -channels. They will independently develop methods and workflows in determined task areas and regularly reflect and present their results. They will develop, design, produce and create their own formats and solutions and summarize them in a final project presentation.

                                            Lecturershttps://www.intranet.hs-mittweida.de/nsoft/his/dozenten/dozent.info.asp?id=2675

                                            https://www.intranet.hs-mittweida.de/nsoft/his/dozenten/dozent.info.asp?id=2675Amrhein, Christof, Prof. Dipl.-Ing. (FH) (Dozent, Prüfer), Fleck, Rika, M.Sc. (Dozent, Prüfer), Wiebach, Manuela (Dozent)https://www.intranet.hs-mittweida.de/nsoft/his/dozenten/dozent.info.asp?id=3905

                                             

                                            Contact hours per week: 4

                                            ECTS Credits: 5

                                            Course: Media Management, 5th semester

                                            Faculty: Media

                                            Financial Management (M. Sc.)

                                            Learning objectives:

                                            This teaching area is intended to highlight key interdependencies within the context of financial analysis, planning, management and controlling at companies. Due to the interdependencies with in-vestment product markets, financial markets and the state sector (subsidies, taxes) as well as the significance placed on financial decisions for the ability of companies to survive, financial management plays a key role in all operative and strategic company decisions. Therefore, students should learn not just analytical and planning skills and knowledge, but also methodological and instrumental skills.

                                            Syllabus:

                                              Overview of basic financial concepts, objectives and methods:

                                              • Liquidity, profitability, financial risk, financial equilibrium,
                                              • Organisation of company economy.

                                              Financial analysis:

                                              • Financial success according to analysis of annual accounts,
                                              • Cash flow statement,
                                              • Value added statement.
                                              • Financial planning and financial management:
                                              • Liquidity and capital requirements planning,
                                              • Cash and cash flow management

                                              Risk management.

                                              Forms of financing:

                                              • Types of financing, regulations on financing, market access (rating),
                                              • Capital resources, self-financing,
                                              • Capital resources, external financing,
                                              • Special forms: Leasing, factoring, swaps, mezzanine capital,
                                              • Public finances.

                                              Approaches to optimisation of financing

                                                Lecturers: Prof. Dr. rer. pol. Andreas Schmalfuß (course director)

                                                Contact hours per week: 4

                                                ECTS Credits: 5

                                                Course: Industrial Management, 1st semester

                                                 

                                                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

                                                      Graphs and Networks (B. Sc.)

                                                      Learning objectives:

                                                      This module provides a first introduction to graph theory. The student will learn to model problems in the language of graph theory, to prove statements about finite undirected graphs, and to analyse properties of graphs and networks.

                                                      Syllabus:

                                                       

                                                      Topics:

                                                      • undirected graphs, subgraphs, walks, paths, cycles, special graphs, dergree sequences
                                                      • connectedness, components,
                                                      • representations of graphs, graphs and matrices, graph isomorphism,
                                                      • spanning trees and their enumeration,
                                                      • distances in graphs,
                                                      • independence and domination,
                                                      • graph colorings networks and algorithms

                                                          Lecturers:

                                                          Prof. Dr. rer. nat. Peter Tittmann

                                                          Contact hours per week: 4

                                                          ECTS Credits: 5

                                                          Course: Applied Mathematics, 3rd semester

                                                          Faculty: Applied Computer Sciences & Biosciences

                                                           

                                                          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 (B. Sc.)

                                                              Learning objectives:

                                                              After completion of all courses of this module, students should be able to understand, evaluate and develop strategies and tactics of MNEs/SMEs in developing international markets. The module will enable students to understand socio-economic conditions of a rapidly changing global business environment. Students will be able to analyze, differentiate and prioritize international markets (countries, regions) according to their market potential, political situation, risks and other relevant factors under consideration of unique selling propositions, business models, competition situation, capacities and capabilities, interests and influences of stakeholders of the company. Based on analysis they should be able to draw conclusions (apply and reflect skills) on how these markets can be developed using appropriate strategies and entry forms. Thereby they apply acquired expertise from economics, media and politics as well as intercultural competences.

                                                              Syllabus:

                                                                Students learn the major areas of international management: concepts, tools and methods to analyze of critical drivers and dynamic changes in global environment (globalization, megatrends, etc.), evaluation and selection of markets and countries, strategic management related to internationalization as well as market entry and expanding strategies, international marketing and business development, international organizational and managerial structures and human resources management.

                                                                  Lecturers: Prof. Dr. rer. oec. (BY) Serge Velesco

                                                                  Contact hours per week: 4

                                                                  ECTS Credits: 5

                                                                  Course: Global Communication in Business and Culture, 5th semester

                                                                  Faculty: Institute for Knowledge Transfer and Digital Transformation

                                                                  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 materials engineering (M. Sc.)

                                                                    Learning objectives:

                                                                    Acquisition of basic knowledge and skills in the area of materials engineering, and training in practical capabilities in the area of material assessment. The focus here is on the relationship between material structure and material property. This is associated with a basic ability to assess mechanical and chemical load capacity of available materials and material groups such as steel, non-iron metals and plastics which form the basis for constructive utilisation in machine engineering. Aspects of environmental protection also play a role here.

                                                                    Syllabus:

                                                                      Based on knowledge of chemistry and physics at a school-leaving level, the area of material engineering is explained in a fundamental manner, beginning with atomic structure, chemical double bonds, and the resulting composition of solid bodies with characteristics properties. Ideal and real atomic structures, as well as the foundations of alloying techniques for metallic materials, will be dealt with using state of matter diagrams. The area of material properties is focused on the mechanical and chemical behaviour that is of great significance for constructive utilisation. The material groups of steel, selected non-iron metals and plastics, with a focus on thermoplastics, are dealt with in terms of manufacture (environmental protection), processing and application. Aspects of property change caused by the chemical structure and mechanical processes are taken into consideration, as is standardisation in material labelling. Knowledge in the area of material inspection is necessary for assessing the behaviour of the material. To do this, inspection processes in a mechanical-thermal material test will be dealt with.

                                                                        Lecturers: Prof. Dr.-Ing. Frank Hahn (course director)

                                                                        Contact hours per week: 4

                                                                        ECTS Credits: 5

                                                                        Course: Industrial Management, 1st semester

                                                                         

                                                                        IT-Management (M. Sc.)

                                                                        Learning objectives:

                                                                        The students can establish the importance of operational information processing within strategic company planning and organisation; the students will know of the possibilities of using information processing as an instrument for achieving company aims; the students can ascertain and evaluate the defining parameters for decisions of optimum IT organisation in a company

                                                                        Syllabus:

                                                                          Business-management orientation of IT management, core competences, targeted competitive advantages, core processes, management information on core processes, authority;Planning communication infrastructure; decision processes, devel-opment model, decision criteria and processes, IT controlling;Database decisions, selection of development tools, network planning, process management; alternatives for classification of IT structure units, advantages and disadvantages; structural organisation of IT department; comparison of various scenarios of IT organisation such as internal organisational unit, outsourcing, cloud computing; ascertaining service levels; legal foundations of data protection, handling personal data, data security measures, IT governance; quality management models and standards, such as PMBOK, CMMI, ISO 27000.

                                                                            Lecturers: Prof. Dr. rer. pol. Petra Schmidt (course director)

                                                                            Contact hours per week: 4

                                                                            ECTS Credits: 5

                                                                            Course: Industrial Management, 1st semester

                                                                             

                                                                            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

                                                                               

                                                                              Machine Learning / Pattern Recognition (B. Sc.)

                                                                                  Lecturers:

                                                                                  Dr. Tina Geweniger (Dozent, Prüfer) M.Sc. David Nebel (Dozent, Prüfer) Prof. Dr. rer. nat. habil. Thomas Villmann (Dozent, Inhaltverantwortlicher, Prüfer) Mandy Lange (Dozent, Prüfer)

                                                                                  Contact hours per week: 7

                                                                                  ECTS Credits: 10

                                                                                  Course: Applied Mathematics, 5th semester

                                                                                  Faculty: Applied Computer Sciences & Biosciences

                                                                                   

                                                                                  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 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.

                                                                                    Media IT-Systems (M. Sc.)

                                                                                    Learning objectives:

                                                                                    The module teaches theoretical relationships between functionalities, parameters and applications of media IT systems, and their data formats, interfaces, security aspects and EMC requirements.Students will be given the skills to select, evaluate, implement in practice, and configure media IT systems based on solid technical knowledge, and even to develop selected applications themselves.

                                                                                    Syllabus:

                                                                                      Classification and development of media IT systems, such as data processing, communication, management, sound, video, backup and security systems; sensors recording systems, periphery components; functionality, set-up, characteristics, performance features, EMC requirements, interfaces, media data formats, certifications, configurations, special features of media IT systems.

                                                                                        Lecturers: Prof. Dr.-Ing. Wilfried Schmalwasser (course director)

                                                                                        Contact hours per week: 4

                                                                                        ECTS Credits: 5

                                                                                        Course: Industrial Management, 1st semester

                                                                                         

                                                                                        Research in Application (M.Sc.)

                                                                                        Lecturers: all professors of mathematics

                                                                                        You submit your Master thesis research project, work on it in our faculty and get supervised by our lecturers.

                                                                                        Research Seminar (M.Sc.)

                                                                                        Lecturers: all professors of mathematics

                                                                                        description follows

                                                                                        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 1 (B. A.)

                                                                                          Learning objectives:

                                                                                          This teaching module covers the basics of the dramaturgy and aesthetics of audiovisual media products. The aim is to familiarize students with the function and applicability of dramaturgy. During the teaching units, students will become acquainted with dramaturgical concepts and their functions, and will use this knowledge to develop their own material. They will discover the complexity of narratives and will learn to distinguish and analyze elements of a narrative, and to purposefully use these elements in their own material.

                                                                                          Syllabus:

                                                                                          The lecture Dramaturgy and visual narration is an introduction to the theories and techniques of cinematic narration. Students will learn the basics of dramatic narration and become acquainted with different genres and their respective visualization. The module covers the basic history of theater and drama, as well as the basics of using aesthetic elements as parts of a narrative (film sequences, images, sounds, characters). Students will learn about the functions of narrative elements (characters, characterization in visual imagery, content-related dramatic compression). The lecture enables students to identify and evaluate the functions of the elements of narratives. The seminar "Story development" covers basic aspects of script writing, with a focus on the formal structure (scene, sequence, act) and, content-wise, on the main thread, including plot and characters. This teaching unit gives an overview of the possibilities for creatively using dramaturgical principles, and highlights them using film excerpts. Students will learn to analyze and critically evaluate material under the aspects of storytelling. The seminar expands and deepens the skills acquired during the teaching unit "Dramaturgy and visual narration" to include practical questions such as "What characterizes an idea for a film? What role do the characters and plot play? How can storylines be developed? How can narratives be presented and visualized in a clear way?" Students will acquire the skills to develop material and to purposefully use the respective practical tools when doing so.

                                                                                          LecturersAmrhein, Christof, Prof. Dipl.-Ing. (FH); Emig, Carina

                                                                                          ECTS Credits: 5

                                                                                          Course: Media Management, 3rd semester

                                                                                          Faculty: Media

                                                                                           

                                                                                          Screenwriting 3 (B. A.)

                                                                                          Learning objectives:

                                                                                          The teaching module "Screenwriting 3" is project-oriented and ties into the module "Film and TV Production 3". Students are familiar with the basics of formulating goals in storytelling and are given an opportunity put the material conceived during the teaching module "Screenwriting 2" into practice. They are taught to create visual-dramaturgical drafts and to realize content anchored in the storyboard. The module covers content-related and technical aspects of film production by means of practical exercises.

                                                                                          Syllabus:

                                                                                          The aim of the seminar is to put into practice the scripted scenes developed during teaching module 2. Students will learn the basic process of film production and will become acquainted with the associated content-related and technical aspects. They will learn how to organize and execute smaller film shoots by themselves. In this context, they will also learn about important planning factors that need to be taken into account, such as permits and the legal framework. Furthermore, they will become acquainted with the technical possibilities of camera systems, file formats and post-production processes, while learning to assess the implementation potential of their developed material.

                                                                                          Lecturers

                                                                                          Amrhein, Christof, Prof. Dipl.-Ing. (FH) (Dozent, Prüfer); Bahn, Claudia; Praxenthaler, Matthias, Dipl.-Kaufmann

                                                                                          Contact hours per week: 4

                                                                                          ECTS Credits: 5

                                                                                          Course: Media Management, 5th semester

                                                                                          Faculty: Media

                                                                                           

                                                                                          Selected Topics in Computational 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:

                                                                                          This course provides advanced principles and knowledge about mathematical approaches and discusses their numerical 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:

                                                                                          Dimensionality estimation in data sets, sparse models, robust estimators of parameters, non-Euclidean dissimilarities, independent component analysis & blind source separation, recent topics.

                                                                                            Lecturers: Prof. Dr. Franka Baaske

                                                                                            Contact hours per week: 4

                                                                                            ECTS Credits: 5

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

                                                                                            Faculty: Applied Computer Sciences & Biosciences

                                                                                            Signals and Systems (M.Sc.)

                                                                                            Learning objectives:

                                                                                             

                                                                                            The aim of the course is to provide knowledge for the analysis, description, classification and transformation of random variables and to chance processes, to estimate its characteristic parameters and its effect on and influence by linear/nonlinear and/or systems timebariant / invariant systems.

                                                                                            Syllabus: The course starts with an introduction and deepening of the foundations of probability theory and description of random processes. Based on this, one- and multi-dimensional transformations of random variables and the generation of random variables and processes considered to chance with desired properties. This includes an introduction to the properties and application of Markov chains.The second part of the lecture contains mathods to estimate the cur-rent implementation of a random variable and the properties of the underlying random process. This includes, among other things, the motivation and derivation of the MAP- and ML-estimation rules and the CramerRao bound for estimating the predictive accuracy.Content of the third part of the lecture is the processing of random processes by means of filters with the objective of interference, interpolation and estimate of random variables. It is the derivation and application of the matched filter and Wiener filter, associated adaptive filter algorithms (LMS, RLS) and the Kalmanfilter.

                                                                                             

                                                                                              Lecturers: Prof. Dr. Alexander Lampe

                                                                                              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

                                                                                              Statistics and Probability Theory (M.Sc.)

                                                                                              Learning objectives:

                                                                                              The main objective is the acquirement of solid knowledge of probabil-ity theory and statistics. Students learn to handle various classes of stochastic processes and statistical models. Practical applications will be discussed in detail and implemented and solved using computer-ized methods. Based on that, students will gain a deep understanding of probability theory and statistics. Additionally, students acquire the abilities to comprehend practical problems conceptually, to structure, to classify and to solve them self-contained.

                                                                                              Syllabus:

                                                                                              In this course measure-theoretical foundations of probability theory, probability- and moment-generating as well as characteristic functions, types of convergence, and limit theorems are introduced. The main focus lies on their applications in statistics (construction of sta-tistical tests, asymptotic distributions). Theory will be made tangible by using stochastic simulations in the practical part of the course.

                                                                                                Lecturers: Prof. Dr. Kristan Schneider

                                                                                                Contact hours per week: 4

                                                                                                ECTS Credits: 6

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

                                                                                                Faculty: Applied Computer Sciences & Biosciences

                                                                                                Success Controlling (M. Sc.)

                                                                                                Learning objectives:

                                                                                                Strengthening knowledge on estimating costs and success. Cost estimation systems and a basic understanding of existing management-oriented controlling terms will be discussed in the first few lectures. In the following seminars, classical and newer approaches of controlling to particular issues of establishing technical competences will be presented. The seminars will provide all students with the theoretical foundations needed for understanding, such that the targeted handling of special questions will be easier. Furthermore, the case studies should give as conclusive an overview as possible with regards to the classic and modern instruments of controlling, such as budgeting and key data systems, in order to properly internalise methodological skills.

                                                                                                Syllabus:

                                                                                                  1. Controlling as a management function
                                                                                                  2. Definition of success and cost management
                                                                                                  3. Direct costing and applications
                                                                                                  4. Calculating planned costs
                                                                                                  5. Flexible cost controlling
                                                                                                  6. Fixed cost management
                                                                                                  7. Project controlling
                                                                                                  8. Calculating process costs
                                                                                                  9. Calculating target costs
                                                                                                  10. Budgeting
                                                                                                  11. Key data systems

                                                                                                    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, 1st semester

                                                                                                     

                                                                                                    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

                                                                                                        Topics in Modern Analysis (M.Sc.)

                                                                                                        Learning objectives:

                                                                                                        Studies of basics of functional analysis, application to special prob-lems of signal processing and data compression, ability to own sci-entific work based on in-depth mathematical skills Training of the following skills:

                                                                                                        • systematisation and classification of mathematical problems in the mathematical field and in the field between mathematics and other disciplines,
                                                                                                        • generalisation of basic mathematical correlationsproving (at a higher level of abstraction),
                                                                                                        • application of methods which are based on the functional analysis to problems of science and technology and to current problems of computational mathematics,
                                                                                                        • analysis and solution of typical application problems from image and data compression

                                                                                                        Lecturers: Prof. Dr. Franka Baaske

                                                                                                        Contact hours per week: 5

                                                                                                        ECTS Credits: 6

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

                                                                                                        Faculty: Applied Computer Sciences & Biosciences