Artificial Intelligence

Overview

This international MSc programme trains you to understand and design the algorithms that form the backbone of modern artificial intelligence. It combines methods from computer science, mathematics and psychology so you can research and create advanced AI techniques. Core topics include Deep Learning, Support Vector Machines, Markov Random Fields, Wavelets, Bayesian Networks, optimal reasoning, knowledge representation, pattern recognition, multi-scale representation, program verification and constraint-based programming. You will learn how to conceptualize AI methods, test and validate their properties, refine them, and implement them in practice.

The programme places strong emphasis on the mathematical and algorithmic models that drive AI, so a solid grounding in theoretical computer science is expected. You will be trained to make AI behaviour predictable and explainable, to understand how systems react in unexpected situations, and to build trust in their outputs. At the same time, the curriculum encourages critical assessment of the limits and societal implications of applying AI to real-world problems.

This MSc is taught in English and offers close links to research and industry partners — for example, the Lausitz Center for Artificial Intelligence (LZKI), the Leibniz Institute for High Performance Microelectronics (IHP), the German Aerospace Center (DLR), Fraunhofer Institutes and Rolls Royce — which can provide opportunities for research projects or for completing your master’s thesis. If your main interest is the engineering and development of complex hardware- or software-based AI systems, there is a related Master’s programme (Artificial Intelligence Engineering) taught in German that may better match those goals.

Admission-related requirements (concise)

• Solid background in theoretical computer science (mathematical and algorithmic foundations)
• Proficiency in English (programme is taught in English)
• Motivation to engage with mathematical, psychological and computational aspects of AI
• Willingness to consider ethical and societal implications of AI applications

Relevant career paths

• Data science and mathematical data analysis
• Medical data analysis
• Development of AI algorithms and methods (e.g., machine learning)
• Research and scientific treatment of AI-related questions
• Autonomous driving and assistance systems
• Intelligent control systems (e.g., supply chains, energy and environmental sectors)
• Research positions at universities and institutes

Overview

This two-year (four-semester) programme is 120 credit points (CP) in total, organized as four semesters of 30 CP each. Its academic core is built around three mandatory elective blocks—Advanced Methods; Knowledge Acquisition, Representation, and Processing; and Learning and Reasoning—each designed to deepen different aspects of artificial intelligence. Together with seminars and laboratory work, a general studies course, an obligatory internship and a Master's thesis, the curriculum balances theoretical foundations, methodological depth and practical application.

Key modules and learning outcomes

• Advanced Methods: equips you with sophisticated methodological tools and technical approaches central to contemporary AI research and development.
• Knowledge Acquisition, Representation, and Processing: focuses on ways to model, represent and process structured and unstructured knowledge so systems can use and manipulate information effectively.
• Learning and Reasoning: concentrates on algorithms and systems for learning from data and for automated reasoning, enabling you to design intelligent systems that infer, decide and adapt.
• Seminars and laboratories: at least 8 CP of hands-on, project- or discussion-based modules provide practical experience, experimentation and presentation skills that complement coursework.
• Internship and Master's thesis: a mandatory internship places you in an industrial environment to apply subject-specific and methodological skills in practice and to bridge research with real-world work; the Master's thesis concludes the programme with an independent research project.

Program requirements (concise)

• Total credit load: 120 CP (4 semesters × 30 CP).
• Three mandatory elective blocks: Advanced Methods; Knowledge Acquisition, Representation, and Processing; Learning and Reasoning — at least 6 CP must be earned in each block.
• Seminars/laboratories: minimum of 8 CP.
• One general studies course is required.
• Completion of a Master's thesis.
• Mandatory internship to apply course knowledge in an industrial/practical setting.
• Remedial/bridging modules: a set of modules is available for students whose prior degrees are close to AI but lack some essential AI content; up to 18 CP from these modules can be counted toward the Master's programme.
• Modules already credited toward a Bachelor's degree may not be reused for this Master's programme.

Graduates are prepared for roles that require strong theoretical and practical AI skills, such as data scientist, algorithm and method developer, machine learning engineer, or specialist in mathematical data analysis. The programme’s emphasis on rigorous mathematical and algorithmic understanding makes alumni attractive to research departments, high-tech companies, and organisations working on autonomous systems, intelligent control, and medical or scientific data analysis.

Many students find opportunities in research institutions and industry partners associated with the programme (e.g. DLR, Fraunhofer, Rolls Royce, IHP) through internships or thesis projects. The degree also provides a solid foundation for doctoral studies and academic careers in AI and related computational sciences.