Modules What you can expect during your studies

Programming for Data Science
Introduction to basic programming and language concepts needed for Data Science. Basic and essential language elements of the programming language Python. Fundamental design principles, professional development environments. Guided programming tasks and tutorials for the independent development of effectively tested solutions. (6 ECTS)

Object Oriented Programming
A profound, practice-focused introduction into the basics of object-oriented programming using the Java programming language. Through a host of practice exercises and examples, students are given the skills to develop simple, automatically tested  programs. The application of selected design principles as well as the use of professional development tools complete the course content. (6 ECTS)

Close

Linear Algebra
Introduction to linear algebra. Linear systems of equations, matrix algebra, vector spaces, linear mappings and their properties & applications. Eigenvalue and singular value decomposition. Numerical software packages for linear algebra. (3 ECTS)

Analysis
Differential and integral calculus. Integration rules: product, quotient and chain rule, integration by substitution and partial integration. Applications to graphs of functions and in optimization problems. Multidimensional differential calculus. Numerical methods for integration and differentiation. (6 ECTS)

Discrete Math
Logic and proofs. Sets, functions, numbers and matrices. Justification, induction and recursion, basics of counting, discrete probability theory, number theory and graph theory. (6 ECTS)

Statistics & Probability
Mathematical foundations of statistics and stochastic processes. Descriptive statistics, probability theory, and distributions. Estimation and testing problems. Experiment design. Time-series data analysis. (3 ECTS)

Close

Knowledge Representation
Logic programming to support symbolic and probabilistic reasoning. Formalisms for knowledge representation. First order logic. Conceptual modelling in information systems. Automated reasoning, inference engines, and ontology engineering. Probabilistic reasoning, Markov chains, Bayesian Inference and Bayesian networks for reasoning under uncertainty. (3 ECTS)

Machine Learning & Neural Networks
Fundamental methods of machine learning. Supervised and unsupervised learning. Data quality analysis. Regression and classification. Neural networks and deep learning, including Feed-Forward Networks, and Back Propagation Training. Deep learning. Convolutional Neural Networks. Recurrent neural networks. Generative Adversarial Networks. (6 ECTS)

Reinforcement Learning
Sequential decision making. Dynamic programming. Reinforcement learning algorithms such as Temporal- Difference learning, Monte Carlo, Sarsa, Q-learning, Policy Gradients and Dyna. Actor-critic and gradient-based optimisation. Multi-agent reinforcement learning. Environments with partial observability. Training agents and evaluating performance. Case Studies and applied reinforcement learning project work. (3 ECTS)

Close

Introduction to AI & Machine Learning  
This module is dedicated to the basics of human perception. It offers an introduction to the methods of (neuro)psychology and to human perception as an inspiration for artificial intelligence. It provides basic knowledge of knowledge structuring and the general philosophical and psychological foundations of language, perception, learning and cognition and highlights how these are incorporated into the development of artificial intelligence. The relationship of humans to technology is questioned and methods for technology assessment are taught. Ethical and legal aspects are addressed, particularly from the perspective of trust and acceptance of new technologies and against the background of the history of artificial intelligence. (6 ECTS)

Philosophy, Art & AI
A critical examination of current social and technological developments in AI. Limits and expansion of human possibilities through the use of AI. Ethical and legal issues, including the limits of what is technically feasible and ethically acceptable. Critical questioning of current and future developments. Examination of positions and ways of thinking from the fields of philosophy, politics, art and film. (3 ECTS)

Ethics & AI
In-depth analysis of the opportunities and risks of AI from an ethical perspective. Ethical questions in the context of decision-making processes of autonomous systems, and the tension between human dignity and machine autonomy. Significance of digital change for an ethical humanism. Application of ethical theories to students' practical questions and the working world of students and their future professional fields. (3 ECTS)

Close

Project Management
Classic, agile and hybrid project management based on theory, exercises and a case study. Verbal and visual communication.  Constructive criticism, stakeholder management,  time management, practical exercises and presentation techniques. (3 ECTS)

Enterpreneurship
General and specific skills for starting a disruptive AI business are taught. The module explains and discusses how AI innovation can occur in an economically responsible way. The knowledge will be applied in a group project to develop ideas that make today's applications more "intelligent". The disruptive ideas will be pitched in an evaluation forum. Lean Start-Up and methods of human-centred design are applied. (3 ECTS)

Game Theory
In this module we will explore the foundations of game theoretical description of conflicts and mathematical models that are used to resolve them, meaning finding the optimal strategy accounting for opponents’ responses. We will explore simultaneous, repeated and dynamic games and discuss the most important game-theoretical concepts (such as rationality, best responses, optimal strategy, Nash equilibrium, sub-game perfection, game-theoretic values) and their applications within classical toy-examples and real-world cases. (3 ECTS)

Algorithmic Business
Introduction to Algorithmic business, the use of complex mathematical algorithms pivotal to driving improved business decisions or process automation for competitive differentiation. It provides speed and scale to accelerate digital business to deliver significant business impact. As opposed to the Entrepreneurship module the focus will be on bringing AI disruption to established and large organisations. (3 ECTS)

Close

Industrial Optimization
Mathematical methods of continuous, discrete and combinatorial optimization under constraints. Linear programming non-linear optimization, constraint programming, integer optimization with heuristic methods. Practical applications to optimization in an industrial context. (6 ECTS)

Robotics 
This module will focus on the computer science aspects of robotics such as control, search and optimization. Students will focus on programming (rather than building) robots in our cognitive robotics lab, for problem solving, and human interaction. This includes the  realization and testing of suitable learning methods, as well as the design of intuitive user interfaces. There will be a lot of practical work, e.g. with drones and other autonomous systems. (6 ECTS)

Natural Language Processing
Natural language processing (NLP) is a subfield of linguistics, computer science, and AI concerned with the interactions between computers and human language, in particular how to program computers to process and analyse large amounts of natural language data. We cover text-processing, semantics analysis, sentiment analysis, document classification and chatbots, as well as the latest deep learning methods for NLP. (9 ECTS)

Computer Vision & AI
This module is concerned with how computers can gain a high-level understanding from digital images or videos. This includes image processing, colour perception, enhancement and filtering, feature detection, image segmentation, object recognition and classification. We also cover convolutional neural networks and the latest methods in deep learning for computer vision. (9 ECTS)

Close

Product Development
Learning project, working on a given or open question in a heterogeneous/interdisciplinary and creative team.  Traceability and planning are essential aspects. If necessary, missing technical basics must be acquired in self-study. A teacher coaches the teams. It is an exciting opportunity for students to conceive and implement a digital disruption in the domain of Artificial Intelligence and Machine Learning. (12 ECTS)

AI Challenge / Competition
Students participate in national and international AI Challenges and compete against other universities or organizations. Significant prize money is often at stake. Students will be supported to be ambitious and win these projects. The challenges are, for example, competitions from Kaggle or RoboCup, or relevant Hackathons. (12 ECTS)

Bachelor Thesis Project
Individual bachelor thesis in the context of AI and Machine Learning. The projects are assigned by business partners or research groups/lecturers. Practical implementation of technology and great communication of results have the highest priority. The bachelor thesis is always carried out as an individual piece of work. (12 ECTS)

Close