Micro-Degree Offers in Quarter 3

In the second deep dive for coding, students are challenged to understand digital image processing and working with interactive devices, such as cameras or microphones. Discussed topics will be coordinate systems, color modes, the history of human-computer interfaces, vector vs. pixel graphics, computer vision, etc. To deepen the understanding, the students will also look into the necessary maths, e.g. matrix manipulations, etc. The basics of Object-Oriented Programming as discussed in Coding Camp 1 will be re-capitulated and deepened.

To get started quickly and be able to work on interesting projects soon, we will use tools and libraries, such as Processing and OpenFrameworks. The programming languages of these tools are a subset of the Java programming language and C++. Both will be introduced in the module, always with a hands-on focus.

In this second coding camp, students expand on the knowledge from the first coding camp: Knowledge and experiences of the first Coding Camp, such as project management and software development principles can be reiterated and students can experience further methods.

Learning Objectives

• Students have developed an interactive application using input devices different from a mouse or keyboard, e.g. camera, microphone, etc.
• Students can reflect on the advantages or disadvantages of different software development projects.

Module Instructor: Prof. Dr. Thomas Staubitz

This module is part of the “Rootcamp” which onboards students to the program, the teaching and learning methods. It teaches the principles, techniques and processes of Design Thinking, a user-centric approach to generating innovations. The Design Thinking process combines methods and tools from the fields of design, engineering, the social sciences and business administration. The approach uses these tools to determine the latent desires and needs of future customers. This user-orientation is combined with the perspective of technological feasibility and economic viability. A team-based approach, it not only relies on the creativity of the individual, but also on collaboration and cooperation.

Learning Objectives

• Acquire subject-specific theoretical and methodological knowledge
• Explore, understand and apply the methods and mindsets of Design Thinking
• Practice techniques using concrete project challenges
• Learn how to effectively and productively contribute to a collaborative team
• Practice teamwork and conflict management
• Present in front of an audience and in the face of critique

Module Instructor: Prof. Dr. Steven Ney

An increasing number of attacks are attempting to compromise individual systems or networked infrastructures.

This module first looks at the relevant charac-teristics of different systems and networks in order to identify and categorize attack vectors and potential vulnerabilities. This then makes it possible to consider various theoretical security concepts and measures and to examine their practical implementation for specific attack vectors. In addition to the security concepts used and corresponding vulnerabilities of "classic" computer systems and networks, this module also deals with the security functions and potential vulnerabilities of systems - such as smartphones, IoT devices and cloud infrastructures - as well as emerging technologies for modern telecommunication - such as 6G.

Learning Objectives

• Students know relevant characteristics/security concepts of common systems and networks, as well as potential vulnerabilities.
• Students will be able to independently analyze systems and networks using appropriate methods on a theoretical level and identify potential attack vectors.
• Students can evaluate described security measures in the context of various threats/attacks.
• Students acquire experience in using systems and tools to analyze security measures and identify potential attack vectors.
• Students have gained an insight into current state and open challenges of practice and research on the topic of systems and network security.

Module Instructor: Dr. Pejman Najafi

Decision-making is empowered by (big) data through the use of machine learning and data analytics principles. The course looks at subsymbolic systems: regression models, linear and nonlinear discriminators, decision trees, small neural networks, and support vector machines, among others.

This module conveys fundamental technologies behind big data applications. The students are tasked with understanding and experiencing core principles of e.g. data harmonization and data pipelines fueling machine-learning algorithms.

In the course, students will compare different theoretical approaches to AI and be able to choose the best problem-solving strategy for a given application.

Learning Objectives

• Students understand the fundamental goals of machine learning and data analytics.
• Provide students with an advanced understanding of the mathematics and technology behind AI models.
• They become proficient in the application of methods of data science, machine learning, and data analytics.
• Students can identify problems in data science and are prepared to select the best approach from a toolbox of algorithms.
• Students are able to formulate a scientific problem-oriented report structuring and explaining their methods of problem-solving and the respective results.

Module Instructor: Prof. Dr. Felix Weitkämper

Digital Business Models play a fundamental role in an organization for realizing new ways of value creation for customers and remaining competitive in an increasingly digital world.

The module focuses on advanced concepts for developing digital business models and create new ventures. The focus will be on tools from the ideation of business ideas to building a minimal-viable-product or prototype of the respective digital business model. The module therefore follows a structured approach cov-ering all relevant steps applicable to building stand-alone ventures (start-ups) as well corporate ventures (new businesses within existing organizations) alike.

Learning Objectives

• Provide students with an understanding of advanced concepts for digital busi-ness model development.
• have in-depth knowledge of the venture building process
• know how to create and realize new business ideas outside and inside of existing organizations.

Module Instructor: Prof. Dr. Georg Loscher

This module equips students with the theoretical foundations and technical skills to design and construct immersive virtual worlds, integrating the core systems of geometry, light, physics, rendering, and interaction. Emphasising a synthesis of physical realism and interactive responsiveness, the course prepares students to build visually compelling, perceptually convincing, and functionally robust virtual worlds. Students will engage with topics including spatial modelling, coordinate transformations, lighting models, physical simulations, and real-time rendering pipelines, with practical implementation in professional XR development tools such as Unity. The course also addresses the optimisation of performance, balancing aesthetic fidelity with technical constraints across XR platforms. Project-based work encourages students to iteratively build, test, and refine virtual environments where users can move, see, and interact fluidly.

Learning Objectives

• Apply core concepts of geometry, 3D transformations, and spatial layout to model and structure immersive environments.
• Design and implement lighting and visual rendering systems informed by both perceptual psychology and real-time graphics principles.
• Simulate physics-based interactions, including collisions, forces, particle systems, and environment-based motion, using tools such as Unity and C#.
• Integrate interactive components such as locomotion, object manipulation, UI controls, and dynamic environmental responses.
• Understand and optimize rendering pipelines and performance constraints for deployment on VR, AR, and MR platforms.
• Evaluate the aesthetic, experiential, and computational quality of virtual worlds, ensuring alignment with user-centred design goals and immersive realism.

Module Instructor: Prof. Dr. Daniele Di Mitri

This module provides advanced knowledge about the structure and analysis of large amounts of data and how this is to be used in decision-making processes. Students learn the difference between descriptive, diagnostic, predictive and prescriptive analytics and their application in business contexts. In the module, theoretical concepts and approaches of objective analysis and decision-making are covered.

Learning Objectives

• Acquire subject-specific theoretical and methodological knowledge.
• Understand the possibilities and limitations of data analytics.
• Are able to assess, compare and apply different methods of analysis.
• Practice objective analysis of large amounts of data and decision-making.
• Learn how to critically discuss completed tasks.
• Acquire the ability to select and implement appropriate solution concepts and strategies to a given problem.

Module Instructor: Prof. Dr. Ekaterina Artemova

Within fast changing environments, a strategic perspective is fundamentally important for setting the right directions for future developments of companies. Therefore, the central aim of this module is to provide students with the economic understanding and skills necessary to become good strategists — whether in a large corporation, a mid-sized company, or an entrepreneurial startup.

The module makes participants familiar with the economic foundations of strategic management, the role of entrepreneurs within firms and the economy, and various disciplines of strategic management, such as technology strategy, pricing strategy, or investment strategy. It covers strategic management on the business as well as on the corporate level and the entrepreneurial transformation following a good strategy process by analyzing case studies from various industries and regions.

Learning Objectives

• Enable students to recognize and describe the characteristics of entrepreneurial management in established companies.
• Assess and evaluate the relevance of entrepreneurial activity in established companies for innovation.
• Evaluate the economics of strategies chosen by companies concerning their potential for future.
• Apply tools and frameworks for corporate entrepreneurial activities within companies across industries or public institutions.

Module Instructor: Prof. Dr. Marco Bade