Curriculum

Agile Project Management

Lecture content:

Lean thinking, agile principles and philosophy, Scrum roles, artefacts and events, requirements management via user stories and effort estimates, collaboration in agile project teams.

Learning Outcomes:

Alumni can explain the development, philosophy and basic content of agile methods and assess which of these methods are suitable for use in a given pro-ject. Alumni know the roles, artefacts and events of Scrum and are able to use them in agile projects at scrum master level.

Superior module:

Personal & Social Skills 1

Module description:

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Data Analysis

Lecture content:

Statistical features, correlations, significance tests, linear models for correlation analysis (linear regression), dimensionality reduction methods (PCA, t-SNE), clustering methods (partitioning, density-based).

Learning Outcomes:

Alumni know basic methods of data analysis and have applied them to data from practical studies. Alumni can critically evaluate analytical results and predictive models. Alumni understand significance testing and can use p-values to evaluate ana-lytical results.

Superior module:

Scientific Methods 1

Module description:

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Digital Ideation

Lecture content:

Phases, tools and methods of design thinking (e.g. brainstorming and ideation, brainwriting 6-3-5, empathy maps, body mapping, personas, journey maps, etc.), storytelling and innovation development using practical examples.

Learning Outcomes:

Alumni name different tools and methods of design thinking. Alumni use these tools for creative processes and for evaluating ideas (e.g. de-sign critique) and are able to apply them correctly in a given situation. Alumni practice the tools in the course "Project 1: Concept & Pitch".

Superior module:

Digital Ideation & Prototyping

Module description:

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Diversity in Tech

Lecture content:

Diversity concept in sociology and business administration as well as human resources, opportunities and possibilities of diversity, Diversity in IT, case studies for IT, game and web.

Learning Outcomes:

Alumni know the approaches of diversity management and various IT-specific initiatives. Alumni are aware of the problem of a lack of diversity in the IT sector and in the application areas of games and the web and know theoretical models and influenc-ing factors. Alumni articulate and justify their own position on diversity in group discussions. Alumni are sensitised to the responsible promotion of digital inclusion and di-versity in user groups and in the use itself. Alumni are sensitised to the safe and responsible use of data and in the promo-tion of digital inclusion and diversity in user groups.

Superior module:

Personal & Social Skills 1

Module description:

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Facilitation and Efficient Meetings

Lecture content:

Approaches to facilitating groups, tools of facilitation, understanding roles, lead-ership and behaviour in meetings. Verbal and non-verbal techniques for control-ling group dynamics, dealing with disruptions, media design, practical exercises.

Learning Outcomes:

Alumni know how to plan and organise meetings efficiently. Alumni apply appropriate facilitation techniques to achieve set meeting objec-tives. Alumni can deal with different types of behaviour and master disruptive factors to create a constructive group dynamic.

Superior module:

Personal & Social Skills 1

Module description:

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Innovation Workshops

Lecture content:

The Innovation Workshops take place in interdisciplinary groups of students. The workshops condense and interpret individual topics from the subject areas of MMA and MMT in the form of concept papers, paper prototypes, presentations and dis-cussions.

Learning Outcomes:

Alumni reflect on and evaluate a given problem from the fields of art, culture, me-dia, society and technology. Alumni test their reflection and argumentation skills based on the topics of the impulse workshops. Alumni present and discuss the insights gained in the workshops with peers and stakeholders.

Superior module:

Digital Ideation & Prototyping

Module description:

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Lightning Talks

Lecture content:

Lightning Talks (impulse lectures) on current problems and issues from the fields of art, culture, media, society and technology in various discussion formats. The lightning talks, which are held jointly with MMA in seminar style, serve to inspire students at the beginning of their studies.

Learning Outcomes:

Alumni will broaden their knowledge of current and relevant issues in the arts, culture, media, society and technology, and will be able to identify relevant ques-tions.

Superior module:

Digital Ideation & Prototyping

Module description:

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Project 1: Concept & Pitch

Lecture content:

Storytelling, visualisation, mock-ups, verification or falsification of ideas and as-sumptions. if applicable, obtaining external reviews and assessments regarding the state of the art, feasibility analyses, coaching of project ideas.

Learning Outcomes:

Alumni apply the tools from the Digital Ideation course and prototype individual ideas for further analysis. Alumni work on the iterative refinement of ideas for their Project 1 by critically re-flecting on the feedback received from the peer group and coaches. Alumni present their overall concept in the form of a pitch at the end of the se-mester and convince a jury of the relevance of the chosen topic. Alumni develop a treatment written according to professional standards and a resource plan for the planned Project 1.

Superior module:

Digital Ideation & Prototyping

Module description:

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Rapid Prototyping

Lecture content:

Practical implementation of software prototypes based on modern programming languages and software frameworks. Coaching regarding technology selection and implementation.

Learning Outcomes:

Alumni implement ideas from ¿Project 1: Concept & Pitch¿ in the form of software prototypes and evaluate the results for applicability and further development. Alumni explore relevant programming languages and modern software frame-works from the areas of Game & Simulation Engineering or Web Engineering for the implementation of individual prototypes. Alumni use methods such as pair programming for the collaborative develop-ment of software prototypes.

Superior module:

Digital Ideation & Prototyping

Module description:

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Symposion on Ethics and Sustainability

Lecture content:

The need for professional-ethical orientation has never been as great as it has be-come in the past decade. At this stage, we are being confronted with the topic of ethics from all directions: bioethics, medical ethics, animal ethics, ethics and poli-tics, ethics and economy, ethics as a school subject instead of religion¿ from personalized ethics to environmental ethics, from day-to-day to systems ethics¿. our very existence seems to be sailing in a sea of ethical and morally charged is-sues ¿ particularly because the two terms ¿ ethics and sustainability ¿ are being used more and more am-biguously and prolifically. This lecture will therefore at-tempt to shed some light on the question of terminology and to sensitize partici-pants to the questions behind professional ethics and sustainability.

Learning Outcomes:

Alumni analyse and reflect on ethical-moral dilemmas. Alumni evaluate opinions from a lecture in their own context of action. Alumni argue social issues with a view to their own professional environment. Alumni articulate and justify their own opinion in the group discussion.

Superior module:

Personal & Social Skills 1

Module description:

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Advanced Gameplay Programming

Lecture content:

Communication between and coordination of game artificial intelligence (AI) agents, tactical and strategic AI, communication between world and AI (perception of the world), advanced dynamic or strategic pathfinding and optimisation, simula-tion of large groups (crowd simulati-on), genre-specific AI (e.g. rubber-band AI in racing gamaes, minimaxing in board games), basic learning mechanisms for AI (e.g. neural networks, evolutionary algorithms), AI implementations and tools of commercial game engines (e.g., Unity, Unreal).

Learning Outcomes:

Alumni explain and discuss methods, concepts and algorithms for Game AI. Alumni evaluate and implement methods, concepts and algorithms for Game AI for game types. Alumni utilize AI tools of current commercial engines (e.g. Unity, Unreal). Alumni utilize basic machine learning tools in existing engines (e.g. Unity) for Game AI.

Superior module:

Wahlpflicht 1 / Major - Game & Simulation Engineering 1

Module description:

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Applied Games

Lecture content:

Serious Games, Educational Technologies (EdTech), Exercise Games (Exer-games), Persuasive Games, Gamification, approaches to playfulness und playful design for various application contexts (e.g., education, health); Overview of psychological concepts, prototyping of applied games with various input and output modalities (e.g., Augmented Reality, Virtual Reality, Motion Capturing, Hand Tracking).

Learning Outcomes:

Alumni use various input and output modalities in applied game settings. Alumni apply game concepts and technologies to specific, also socially relevant, problems. Alumni apply game concepts and approaches to playfulness to various application contexts. Alumni apply a methodological approach to realize context-specific game de-signs and discuss benefits and effects of applied games.

Superior module:

Wahlpflicht 1 / Major - Game & Simulation Engineering 1

Module description:

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Multiplayer & Online Gaming

Lecture content:

Types of multiplayer games (real-time, turn-based, massive multiplayer), user man-agement, cloud saves, persistent world, matchmaking and game balance, syn-chronisation between players, predictive models for latency compensation, anti-cheat mechanisms, security aspects of online gaming, backend-as-a-service (BaaS) models, cloud gaming (streaming, virtualisation), cloud computing in games (e.g. physics computing using servers).

Learning Outcomes:

Alumni explain the necessary steps to realise different types of online games. Alumni design and implement software components of multiplayer/online games. Alumni explain basic techniques in the field of cloud gaming/computing. Alumni have an overview of the management of online users and their data (user management, cloud saves), the balancing of groups of players (match-making, game balance) and security-relevant aspects in order to both protect user data and enable fair joint gaming (anti-cheat mechanisms).

Superior module:

Wahlpflicht 1 / Major - Game & Simulation Engineering 1

Module description:

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Applied Programming Paradigms

Lecture content:

Programming Paradigms (e.g. declarative, functional, imperative, object oriented). Selected Programming Languages including WebAssembly, and at least one functional language (e.g. Elm, Elixir). The role of Web Assembly in web development. Basics of compiler architecture.

Learning Outcomes:

Alumni describe the difference between machine language and higher program-ming languages. Alumni explain the role and use of WebAssembly in the modern web. Alumni understand basic programming paradigms and their practical use in web develop-ment. Alumni identify and use declarative, functional, object-oriented, and procedural aspects of a programming language. Alumni understand the role of syntax in compilers and interpreters.

Superior module:

Wahlpflicht 1 / Major - Web Engineering 1

Module description:

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Distributed Software Architectures

Lecture content:

Distributed architecture patterns (e.g. microservices, event-driven architecture, data-centric architectures). Challenges of designing, deploying, and managing large-scale, highly available, and secure distributed systems (e.g. fault tolerance, consistency, interoperability, cost). Overview of cloud computing and its various deployment models (e.g. public, private, and hybrid clouds). Understanding of the key benefits and limitations of using cloud services for distributed applications (e.g. scalability, availability, security, and cost). Case studies and implementation examples. Introduction to Cloud Computing.

Learning Outcomes:

Alumni identify and describe the functional and non-functional requirements of a distributed application. Alumni effectively use (distributed) software architectures to meet the given requirements. Alumni evaluate the advantages and limitations of using cloud services to implement distributed applications. Alumni plan and implement a distributed application.

Superior module:

Wahlpflicht 1 / Major - Web Engineering 1

Module description:

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Web Performance Optimisation

Lecture content:

Web performance metrics (e.g. page load time, time to first byte, first meaningful paint, and time to interactive); performance vs. load vs. scalability; performance and energy consumption. Performance measurement and instrumentation tools (WebPageTest, Google Lighthouse, Google PageSpeed Insights, the Performance API in the browser). Synthetic load generation methods and synthetic testing (e.g. Apache Jmeter, gatling, atillery). Statistical methods and visualisation of web per-formance. Performance optimisation at all levels of the web stack.

Learning Outcomes:

Alumni discuss the role of performance in green computing and sustainability. Alumni use appropriate tools to diagnose performance problems in web applica-tions at different levels: server, network, and browser. Alumni are familiar with optimisation strategies for all three areas and apply them in all three areas. Alumni take measurements and evaluate them using statistics and visualisations. They create test programs to reproduce performance issues.

Superior module:

Wahlpflicht 1 / Major - Web Engineering 1

Module description:

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Ethics in Informatics

Lecture content:

Professional ethics, ethical guidelines of different professional associations, dis-cussion of case studies. Ethical, social and environmental impacts of information technology. Basic approaches/perspectives of ethics and responsibility. Respon-sibility of individuals and organizations. Codes of conduct in information technol-ogy. Ethical issues related to new (digital) technologies (e.g. autonomous driving, new materials, AI, etc.). Ways to apply and establish ethical guidelines within a project.

Learning Outcomes:

Alumni know professional ethics, ethical guidelines of different relevant profes-sional associations. Alumni know case studies regarding ethical and social issues and can discuss them. Alumni know the social effects of digital technologies, can analyse and critically reflect on them. Alumni know common procedures of ethics applications in the context of higher education.

Superior module:

Personal & Social Skills 1

Module description:

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IT Law and Data Protection

Lecture content:

Application of European IP/IT law to contractual situations. Basics of European and national data protection law (DSGVO, DSG, ePrivacy). Lawfulness of processing, operational data protection, types of IT contracts, case law guidelines. Case studies.

Learning Outcomes:

Alumni name central elements of data protection and can explain them. Alumni name central sample contracts in the IT sector. Alumni analyse data protection and IT-legal processes considering technical, legal and business requirements. Alumni can apply the acquired knowledge to selected case studies and argue legal implications.

Superior module:

Personal & Social Skills 1

Module description:

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Innovation Coaching (Impact)

Lecture content:

Identification of an opportunity, trend and environment analysis, analysis of the potential and attractiveness of markets, needs analysis.

Learning Outcomes:

Alumni expand their knowledge and skills to systematically assess the market opportunities of their Project 1. Alumni recognise what an opportunity is and can evaluate an idea as objectively as possible. This enables them to minimise the risk of innovation at an early stage and increase the probability of success of an innovation.

Superior module:

Project 1

Module description:

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Project 1: Implementation

Lecture content:

Coaching in the implementation of multimedia master projects, agile project planning, resource planning, creation of a minimum viable product or vertical slice.

Learning Outcomes:

Alumni have specific knowledge and skills in the planning and implementation of ambitious and innovative multimedia projects using state-of-the-art technology. Alumni argue the tools and frameworks chosen for the technical implementation considering economic aspects. Alumni apply methods of agile project management to achieve the development of a minimum viable product (MVP) in the field of web engineering or a vertical slice in the field of game & simulation engineering in a planned manner. Alumni present the project (interim) result professionally and, if necessary, design a further development plan for the project in the third semester.

Superior module:

Project 1

Module description:

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Project Reflection 1

Lecture content:

Planning, conception and cooperation in the master project, team building, team dynamics, structuring and implementing crisis and conflict situations in terms of solution orientation.

Learning Outcomes:

Alumni can name and analyse their own role in the team, in communication, in conflict, draw conclusions and adapt their behaviour. Alumni know about the group-dynamic aspects in teamwork and can analyse and consciously shape these based on their project work. Alumni reflect on their teamwork based on concrete project experience, recognise problems/conflicts in the team and are familiar with the corresponding tools and methods to deal with them and solve them constructively. Alumni are aware of the importance of dealing with resource bottlenecks, the communicative integration of the relevant stakeholders, the building of trust and a culture of responsibility in dealing with each other.

Superior module:

Project 1

Module description:

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Research Methods & Study Design

Lecture content:

Overview of qualitative research methods, overview of quantitative research methods, defining hypotheses and research questions (descriptive / compara-tive / relational research questions), types of research contributions (empirical, system-based, methodological, theoretical, design-based), translating research questions into concrete research designs and procedures, different metrics.

Learning Outcomes:

Alumni deal in depth with appropriate research, referencing and evidence methods regarding different types of research questions. Alumni can formulate suitable research questions and hypotheses for a con-crete problem based on concrete questions. Alumni know different types of research questions and their ways of answering them. Based on an overview of different qualitative and quantitative research methods, alumni know different types of research questions (descriptive vs. comparative vs. relational research questions) and the corresponding research contributions (empirical, system-based, methodological, theoretical, design-based). Alumni can answer these questions in a concrete methodological process us-ing self-developed study designs.

Superior module:

Scientific Methods 1

Module description:

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Efficient Game Programming

Lecture content:

Advanced efficient software architectures for game engines (e.g. Entity-Component System); practice-oriented techniques for optimal utilisation of memory and com-puting power at architecture level (e.g. object pools, multithreading, memory alignment); telemetry generation to assess optimisation potential; optimisation tools in existing engines (e.g. Unity, Unreal); consolidation through exercises (e.g. from initially processing 10 game objects to 1 million game objects).

Learning Outcomes:

Alumni discuss optimisation possibilities in games or game engines in relation to low-level featuers (e.g. ECS, memory management) Alumni use optimisation options in a targeted way to optimise the performance of a game Alumni define, collect and analyse telemetry data to target optimisation measures. Alumni use different profiling tools from commercial engines to analyse perfor-mance.

Superior module:

Wahlpflicht 1 / Major - Game & Simulation Engineering 2

Module description:

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Games User Research

Lecture content:

Introduction to games user research, understanding, measuring, analysing and designing the UX (player experience) in games, selection and application of (HCI) research methods with a focus on games, getting to know different methods from industry and research for different steps in development, getting to know games user research metrics and methods, survey, interviews, observations, focus groups, diary study, A/B testing, playtesting, players and game telemetry, introduc-tion to game analytics, preparing, conducting and analysing games user research studies.

Learning Outcomes:

Alumni discuss the basic principles of games user research. Alumni plan and conduct playtests in a methodologically sound manner. Alumni explain and use various methods of games user research. Alumni explain the difference between expert evaluations and player evaluations.

Superior module:

Wahlpflicht 1 / Major - Game & Simulation Engineering 2

Module description:

-

Physics-Based Simulation

Lecture content:

In-depth topics of rigid body simulation (rotation, friction, stability of the simula-tion, etc.), collision detection and resolution, optimisation methods for physical simulations (e.g., spatial data structures), simulation of physical connections (e.g. joints) and constrained movements, ragdoll physics, environment-dependent character animation, mass-spring systems, deformable objects, clothing simula-tion, simulation of liquids, weather simulation, simulation of vegetation, principles of physically-based rendering. consolidation through exercises (e.g., from simulat-ing 10 rigid bodies to simulating 1 million physical bodies).

Learning Outcomes:

Alumni have an overview of concepts of physics-based simulation including mathematical formulations of problems. Alumni explain and discuss advanced concepts and techniques of physical real-time simulations and choose the appropriate methods to implement physical effects. Alumni design and implement components of a physical real-time simulation. Alumni explain concepts of physically-based rendering.

Superior module:

Wahlpflicht 1 / Major - Game & Simulation Engineering 2

Module description:

-

Software Quality Assurance

Lecture content:

Basic terms "quality", "quality assurance" and "quality management", quality control and defect reduction with the help of code reviews and inspections, test methods (module, integration, acceptance tests, black/white box tests, unit tests, end-to-end testing, soak/stress testing), test practice (test driven development, coverage tests, mock and stub object), "bad practices" and refactoring patterns, test automation, practical examples (also in commercial engines).

Learning Outcomes:

Alumni explain and discuss quality assurance methods for games. Alumni identify and apply appropriate quality assurance methods for various use cases. Alumni ensure good code quality through appropriately selected quality assur-ance measures. Alumni have an overview of and utilize quality assurance methods and tools of commercial game engines.

Superior module:

Wahlpflicht 1 / Major - Game & Simulation Engineering 2

Module description:

-

Continous Delivery

Lecture content:

Aims and methods of Continuous integration (e.g. automatic builds, testing, deployment). Deployment strategies (e.g. cloud, on-premise, container/virtualisation). Infrastructure as code (e.g. Puppet, Terraform). Test automation. Dependency management. Configuration management.

Learning Outcomes:

Alumni describe the aims of continuous integration. Alumni set up and maintain a CI/CD pipeline. Alumni set up test automation as part of the CI/CD pipeline. Alumni chose different deployment strategies, including cloud, on-premise, and container/virtualisation, based on their particular use case. Alumni use infrastructure as code (IAC) to manage and provision infrastructure automatically, rather than through manual configuration. Alumni use current methods to manage and track the dependencies of a software project and to manage configuration files and settings for a software project.

Superior module:

Wahlpflicht 1 / Major - Web Engineering 2

Module description:

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Frontend Engineering

Lecture content:

Current frontend frameworks (e.g. React, Kwik, ¿), Micro-Frontends, Web Components (e.g. with Stencil.js). Advanced CSS (e.g. layout with container queries, cascade layers, ¿), Methods for implementing a Design System (e.g. Storybook). Server-side rendering for frontend frameworks and hydration Strategies (e.g. React vs Kwik). Current topics.

Learning Outcomes:

Alumni are familiar with current architectures for complex frontend applications, including Micro-Frontends and server-side rendering of the frontend. Alumni use web standards and web components independent of a particular framework. Alumni use modern CSS and current tools to implement a design system into re-usable components.

Superior module:

Wahlpflicht 1 / Major - Web Engineering 2

Module description:

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Software Quality Assurance

Lecture content:

"quality", "quality assurance" and "quality management", quality control and defect reduction with the aid of reviews and inspections. Different test methods (Unit-Testing, End-to-End testing, UI-Unit-Testing; TDD, Parametrized Tests, Assertions / Expectations). Testing the test suite with mutation testing. Test Doubles (e.g. Mocks, Stubs, Spies), Test-Pyramid, Test-Strategies, the role of different test

Learning Outcomes:

Alumni explain and discuss quality assurance methods for web. Alumni identify and apply appropriate quality assurance methods for various use cases. Alumni ensure good code quality through appropriately selected quality assurance measures. Alumni develop a testing strategy for a web application and implement it.

Superior module:

Wahlpflicht 1 / Major - Web Engineering 2

Module description:

-

Web User Research

Lecture content:

Introduction to web user research: understanding, measuring, analysing, and designing the UX (user experience) in web and mobile application. Selection and application of (HCI) research methods (e.g. survey, interviews, observations, focus groups, diary study). Using A/B testing to compare the user engagement, conversion rate, and overall effectiveness of different versions of a website or web application. Analyzing User Behavior with Real User Monitoring (RUM) and clickstream analysis.

Learning Outcomes:

Alumni apply methods for collecting and analysing data about the online behavior of web users to understand how web users interact with a particular website, what they are looking for, and to identify areas for improvement.

Superior module:

Wahlpflicht 1 / Major - Web Engineering 2

Module description:

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Generative AI

Lecture content:

Processing of multimedia data; Machine Learning models for analysing multimedia data (VAE, GAN, GPT-3, DALL-E); Generation of multimedia data; Current trends and tools in the field of Generative AI. Critical examination of the use of fakes in media.

Learning Outcomes:

Alumni have theoretical knowledge in the field of processing, analysis and generation of multimedia data. Alumni have an overview of trends in the field of Generative AI and practical knowledge in generating multimedia data. Alumni learn how to deal with technologies that have a high future social or economic impact, and how to use tools to prevent the misuse of these technologies.

Superior module:

Wahlpflicht 2 / Electives Summersemester

Module description:

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Information Visualisation & Visual Analytics

Lecture content:

Introduction to Information Visualisation: Overview including history, purpose, and key concepts. Data Representation and Encoding: Methods for representing and encoding data in visual form, (e.g. charts, graphs, maps, and other types of visualisations). Visual Analytics: An exploration of the role of visual analytics in analysing and interpreting large data sets, including an overview of key techniques and tools. Interactive Visualisation: An introduction to interactive techniques and tools, (e.g. the use of click-and-drag, pan-and-zoom, and other interaction methods). Selected types of visualisations.

Learning Outcomes:

Alumni describe the field of information visualisation, its history, purpose, and key concepts. Alumni select and implement methods for representing and encoding data in visual form, including the charts, graphs, maps, and other types of visualisations. Alumni create visual analytics to enable the analysis and interpretation large data sets.

Superior module:

Wahlpflicht 2 / Electives Summersemester

Module description:

-

Mixed Reality Technologies

Lecture content:

Applications of AR and VR technology, head-mounted display (HMD) technology in AR/VR and special challenges, current AR/VR SDKs, projector-based AR, calibration methods, interaction techniques (e.g. 3D user interfaces, tangible interfaces, motion capturing), immersion-enhancing technology (e.g. haptic feedback, virtual embodiment, redirected walking). haptic feedback, virtual embodiment, redirected walking), technical basics for position determination (tracking and localisation), methods for analysing and evaluating AR/VR applications (e.g. presence questionnaires, simulator sickness, interaction performance, etc.).

Learning Outcomes:

Alumni have an overview of current state-of-the-art approaches and technologies for AR/VR hardware and applications. Alumni explain basic technical and human-centred concepts of AR/VR and consider them in the design of AR/VR applications. Alumni design, implement and evaluate AR/VR applications for current platforms.

Superior module:

Wahlpflicht 2 / Electives Summersemester

Module description:

-

Innovation Coaching (Business)

Lecture content:

Deepen industry and market knowledge. Selecting and shaping a business concept. Value chains, analytical tools and practical examples.

Learning Outcomes:

Alumni sharpen their knowledge of the industry and market and are able to formulate and present an appropriate business case to a target audience.

Superior module:

Project 2

Module description:

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Master Thesis Seminar 1

Lecture content:

Overview of conferences and journals. Selected qualitative and quantitative research designs. Development of a scientific topic. Preparation of an exposé for the master's thesis.

Learning Outcomes:

Alumni know relevant conferences and journals relevant to the topic of the master's thesis. Alumni have a deeper understanding of qualitative and quantitative research designs. Alumni know the procedure for writing a scientific paper at the level of a master's thesis. Alumni present their research design for the master's thesis and engage in peer group scientific discourse. Alumni argue relevance, research question(s), hypothesis(es) and research design in an exposé for the master's thesis.

Superior module:

Scientific Methods 2

Module description:

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Project 2: Concept & Implementation

Lecture content:

Conception, development and testing of components of the project, accompanying project controlling, change management, completion of prototype. The continuation of a Project 1 from the first year of study is linked to a criteria-based evaluation of the previous project results as well as to a development perspective. If there is no development perspective, a second project is started in the 3rd semester and the steps from conception to prototype are accelerated accordingly based on the experiences from the first year of study. Alternatively, students are given the opportunity to work on research projects at the Creative Technologies Department.

Learning Outcomes:

Alumni are able to conceptualise projects and have specific knowledge and skills in planning, organising and delivering ambitious and innovative multimedia projects. Alumni may extend the minimum viable product or vertical slice from the 2nd semester towards a "feature-complete" and quality-checked prototype. Alumni may contribute to the implementation of research projects in the programme.

Superior module:

Project 2

Module description:

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Project Reflection 2

Lecture content:

Reflection on the project work in the team setting. Action and solution competences in the project implementation, dealing with conflicts, conflict resolution, conversation culture.

Learning Outcomes:

Alumni reflect on their current project work and their team situation and recognise problems and conflicts. Alumni are aware of their resources and know how to activate them. Alumni master tools and methods for solution-oriented work and establish an appreciative communication and feedback culture in the project team.

Superior module:

Project 2

Module description:

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Sustainable Computing

Lecture content:

Use of technology and information systems under ecological, social and economic aspects with regard to the sustainable development goals. Criteria and approaches for the use of digital technologies under the aspect of energy efficiency.

Learning Outcomes:

Alumni are familiar with aspects of sustainability in informatics and take these into account when designing and implementing IT systems so that they are ecologically and socially compatible and at the same time meet economic requirements. Alumni are sensitised to the safe and responsible handling of data and in the promotion of digital inclusion.

Superior module:

Personal & Social Skills 2

Module description:

-

Transfer Project 1

Lecture content:

Independent organisation and participation in projects in the Creative Technologies Department and/or participation in a research project.

Learning Outcomes:

Alumni make a demonstrable contribution outside their own projects (e.g. pool of all ongoing projects, including research projects) and try out the role of an "outsourcing" employee.

Superior module:

Project 2

Module description:

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Artificial Intelligence für Games

Lecture content:

Artificial Intelligence methods for the control of game agents/bots ((Deep) Reinforcement Learning), AI for procedural generation of content (Neural Networks) and realistic rendering (Neural Rendering), Reinforcement Learning: Theory and basics, extensions by neural networks, neural networks (CNN, GAN, ...), neural rendering (light fields, NERFs, plenoxels, ...), applications such asantialiasing, upscaling, shading, relighting, realism, game analytics, cheat detection, churn prediction, dynamic player experience modelling, level generation.

Learning Outcomes:

Alumni have an overview of AI methods and their use cases in games. Alumni explain and discuss basic AI methods and their applications in games. Alumni select and apply appropriate AI methods for different use cases in games.

Superior module:

Wahlpflicht 1 / Major - Game & Simulation Engineering 3

Module description:

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Cross-Plattform Development

Lecture content:

Native development using an exemplary SDK for mobile games (e.g. Android SDK) and consoles (e.g. Nintendo Switch), challenges of mobile platforms and consoles (e.g. limitation of memory and processing power, small screens), challenges of developing for heterogeneous target platforms (e.g. resolution-independent development for different display sizes), runtime and memory optimisation for the target platform, troubleshooting (remote debugging, emulation), platform-independent development.

Learning Outcomes:

Alumni discuss and explain the specific challenges of developing for mobile platforms and consoles. Alumni implement games for mobile platforms or selected consoles. Alumni optimise games for mobile platforms and selected consoles using appropriate profiling methods. Alumni consider platform-dependent challenges in the development of games for mobile devices and consoles.

Superior module:

Wahlpflicht 1 / Major - Game & Simulation Engineering 3

Module description:

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Guest Lecture Game & Simulation Engineering

Lecture content:

Selected current topics in the field of Game & Simulation Engineering.

Learning Outcomes:

To be defined depending on the topic.

Superior module:

Wahlpflicht 1 / Major - Game & Simulation Engineering 3

Module description:

-

Data Engineering

Lecture content:

Relational databases: This section of the course provides an in-depth understanding of relational databases and the concepts of transactions and indices. Students will learn about the concepts of ACID transactions and how they are used to ensure data consistency and reliability. They will also learn about indexing techniques and how they can be used to improve query performance. NoSQL databases: This section of the course focuses on NoSQL databases, including document-oriented and graph databases. Students will learn about the differences between relational databases and NoSQL databases, and the use cases for which NoSQL databases are best suited. They will also learn about the architecture of NoSQL databases and the trade-offs involved in choosing a NoSQL database over a relational database. Event streaming: This section of the course covers event streaming, including the design and implementation of event-driven systems. Students will learn about the concept of event-driven architecture, and the use cases for event streaming in web applications. They will also learn about the various technologies available for event streaming and the trade-offs involved in choosing a particular technology.

Learning Outcomes:

Alumni have a deep understanding of the concepts and technologies involved in data engineering and choose the appropriate database management system for various application scenarios. Alumni implement their chosen solution and monitor its performance over time.

Superior module:

Wahlpflicht 1 / Major - Web Engineering 3

Module description:

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DevSecOps

Lecture content:

Sources of security news (e.g. HackerOne reports, Common Vulnerabilities and Exposures (CVEs). Using threat modelling to assess risk in software development projects. How to manage security issues within the software supply chain (e.g. Snyk, dependabot, software bill of materials).

Learning Outcomes:

Alumni know how to integrate security controls and processes into the DevOps software development cycle. Alumni are aware of the necessary collaboration between security teams, development teams and operations teams in security matters. Alumni implement DevSecOps in their software development practice,

Superior module:

Wahlpflicht 1 / Major - Web Engineering 3

Module description:

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Guest Lecture Web Engineering

Lecture content:

Selected current topics in the field of Web Engineering.

Learning Outcomes:

To be defined depending on the topic.

Superior module:

Wahlpflicht 1 / Major - Web Engineering 3

Module description:

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Scalable Web Architectures

Lecture content:

Scalability: Understanding the various methods of scaling a web application (e.g. load balancing, caching, queuing, distributed systems and cloud computing). Stability/Resilience: Strategies for ensuring the stability and resilience of a web application, including fault tolerance and disaster recovery. Visibility: Best practices for monitoring the performance of a Web application (e.g. logging, profiling, and real-time monitoring).

Learning Outcomes:

Alumni are familiar with the concepts and architectures that make web applications scalable. Alumni integrate the knowledge from Distributed Software Architectures, Continuous Delivery, Data Engineering to plan and implement a scalable web application.

Superior module:

Wahlpflicht 1 / Major - Web Engineering 3

Module description:

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Creative Entrepreneur & Corporate Innovation

Lecture content:

Development of a fictitious business or innovation idea as future design & technology entrepreneur. Business Model Canvas by Osterwalder and Pigneur, financial planning, budgeting, intellectual property. Success-Stories from Entrepreneurs or Corporate Innovations. Reflection on what has been learned into one's own master project process.

Learning Outcomes:

Alumni explain and apply the Business Model Canvas. Alumni explain the structure and content of a business plan. Alumni create a business plan for a fictitious business or innovation idea or their own master project.

Superior module:

Wahlpflicht 2 / Electives Wintersemester

Module description:

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Deep Learning & Explainable AI

Lecture content:

Theory of deep learning models; Standard neural networks (Feed-forward network, RNN, CNN); Modern neural networks (LSTM, GAN, Attention, Transformer); Application of neural networks with current frameworks on big data. Interpretability of machine learning models and specifically neural networks. Critical examination of topics related to deep learning: data protection, discrimination, etc.

Learning Outcomes:

Alumni have theoretical knowledge in the field of deep learning and neural networks about traditional and current models. Alumni know the most common frameworks for using deep learning models and have practical knowledge of the implementation of the forementioned models. Alumni interpret deep learning models and evaluate the results. They know the latest developments in the area of Explainable AI.

Superior module:

Wahlpflicht 2 / Electives Wintersemester

Module description:

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Predictive Modelling

Lecture content:

Modelling approaches for predicting future events are introduced theoretically and then applied to selected data sets and related prediction tasks. The approaches include regression and classification techniques using both linear and non-linear predictors. Particular emphasis is placed on subsequent model optimisation using feature selection and feature reduction techniques. Evaluation techniques leading to well generalied models are used to optimise model parameters.

Learning Outcomes:

Alumni train models to predict future events. Alumni have evaluated the advantages and disadvantages of different predictive modelling approaches. Alumni select appropriate solutions to selected forecasting problems.

Superior module:

Wahlpflicht 2 / Electives Wintersemester

Module description:

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Selected Topics in Human-Computer Interaction

Lecture content:

Different interaction paradigms and their contextual characteristics as well as design-related challenges and opportunities (e.g., multimodal interaction, virtual reality, augmented reality, tangible interaction, smart materials), new design and fabrication methods (e.g., personal digital fabrication), design methods and approaches (e.g., participatory design), selected methodologies, classical and modern theories of HCI, overview of current research topics, streams and application fields in HCI.

Learning Outcomes:

Alumni have an in-depth knowledge of theories, methods and interaction paradigms. Alumni can apply the theoretical and practical knowledge to their own work. Alumni can classify and discuss different contemporary research topics within HCI literature.

Superior module:

Wahlpflicht 2 / Electives Wintersemester

Module description:

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Wahlpflicht 3 [Conference Attendance | Elective Course]

Lecture content:

Students take part in a national/international conference or summer school of their choice. They practise the selection, (travel) organisation and participation in the conference as well as reporting on the knowledge gained at the conference . Alternatively, the degree programme can offer an excursion or students can help to organise a conference. Contents: Types of conferences, conference overview, conference selection, organisation, participation, reporting.

Superior module:

Wahlpflicht 2 / Electives Wintersemester

Module description:

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Lecture Series: Emerging Technologies

Lecture content:

This course deals with state-of-art developments in the field of practical informatics, compulsory elective 1 and compulsory elective 2. The course is organised as a guest lecture series.

Learning Outcomes:

Alumni have an in-depth insight into the state of the art and research in different areas of computer science, business and society. Alumni actively participate in discussions with domain experts.

Superior module:

Personal & Social Skills 2

Module description:

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Master Exam

Lecture content:

Defence of the master's thesis as well as examinations from core areas of the degree programme.

Learning Outcomes:

Alumni demonstrate their knowledge of subject matter and scientific methodology in a master's thesis defence.

Superior module:

Scientific Methods 2

Module description:

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Master Thesis

Lecture content:

Preparation of a scientifically sound master's thesis in one of the core subject areas of the master's programme.

Learning Outcomes:

Alumni identify and formulate scientifically relevant questions. Alumni are versed in the correct selection of research methodology. Alumni create study designs and conduct studies independently, considering possible ethical implications. Alumni interpret the results of quantitative and/or qualitative studies using valid methods. Alumni communicate the results of their work in writing in English, clearly and in a form that is academically appropriate.

Superior module:

Master Thesis

Module description:

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Master Thesis Seminar 2

Lecture content:

Comprehensive and multi-faceted disputation of concepts, objectives, structures, core elements, instruments and problems of the underlying master's theses.

Learning Outcomes:

Alumni defend and reflect the scientific question of their respective master's thesis, as well as the scientific methods used, in a peer group discussion.

Superior module:

Scientific Methods 2

Module description:

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Project Portfolio & Presentation

Lecture content:

Coaching in the preparation of the performance/publication (e.g. submission to competitions and festivals), creation of information material on the master's projects, planning and implementation of accompanying communication measures, public presentation of the multimedia master's projects, e.g. in the course of a release festival together with the other degree programmes of the Department of Creative Technologies.

Learning Outcomes:

Alumni have knowledge and practical experience of exhibiting and/or publishing their projects with accompanying measures (e.g. production of information material, accompanying communication measures, etc.). Student showcase of their project portfolio online.

Superior module:

Personal & Social Skills 2

Module description:

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Project Reflection 3

Lecture content:

Feedback culture, final reflections, conflict management, comparing experiences in teams and lessons learned are the aims of this course to actively prepare students for networking after graduation.

Learning Outcomes:

Alumni gain experience in accompanying the final phase of team development. Alumni can consciously adopt a meta-perspective on their own and the team's actions, behaviour, communication and interaction in the project. Alumni gain the best possible knowledge from their project and apply this knowledge in a sustainable way.

Superior module:

Personal & Social Skills 2

Module description:

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Transfer Project 2

Lecture content:

Independent organisation and participation in projects in the Creative Technologies Department and/or participation in a research project.

Learning Outcomes:

Alumni make a demonstrable contribution outside their own projects (e.g. pool of all ongoing projects, including research projects) and try out the role of an "outsourcing" employee.

Superior module:

Personal & Social Skills 2

Module description:

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