This English-taught, consecutive and research-focused Master's programme gives you deep, practice-oriented knowledge of artificial intelligence and the methods used to create and evaluate AI systems. It places strong emphasis on learning how to conduct scientific research so you can both advance theoretical understanding and contribute to evidence-based development in the field.
You will engage critically with core AI principles and techniques, with particular attention to machine learning and the mathematical foundations that underpin it. The curriculum also covers how these foundational concepts can be adapted and applied to real-world AI use cases, preparing you to transfer methods across domains and design robust, practical solutions.
Graduates are equipped for a wide range of roles in AI, data science and cognitive computing, as well as positions in research and industry that involve developing AI systems. The programme also prepares you for leadership responsibilities — including overseeing development, integration and the safe operation of AI technologies.
This Master's programme examines both the practical uses and broader implications of artificial intelligence, training students to apply AI methods in a rigorous, scientific way. From the start, the course emphasizes methodical work: you will learn to assess existing solutions critically, adapt known approaches to new problems, and follow structured, methodologically sound procedures for describing and solving research questions.
The first semester builds a solid foundation in the mathematical and methodological concepts that underpin AI—especially machine learning—and includes a Scientific Seminar that prepares you for independent research. Elective modules are available in the first and third semesters so you can begin to specialise early and tailor the degree to your interests. In the second semester the focus shifts to concrete application areas such as robotics, natural language processing and computer vision, while also addressing ethical and legal considerations surrounding AI.
Practical, team-based projects in the second semester consolidate the competencies you acquire; these project credits are equivalent to two regular courses and give hands-on experience applying methods to real problems. The programme culminates in an independently written Master’s thesis in the third semester, where students apply scientific methods and the knowledge gained throughout the course to investigate a self-chosen topic.
Learning outcomes:
Admission overview
This Master's programme requires a strong, relevant undergraduate background in computer science and mathematics. You must hold a completed Bachelor's degree of 210 ECTS with an overall grade of 2.5 or better on the German grading scale (or an equivalent degree/grade from an international institution). The closer your Bachelor’s degree is to computer science, the easier it is to demonstrate suitability; if your degree is less related, you will need to clearly show your prior focus on CS and mathematics.
You must document your academic preparation using your Bachelor’s degree certificate and transcript only—individual course certificates from outside the Bachelor’s, letters of recommendation, or school reports are not accepted as proof. In addition to the formal credit and grade thresholds, we expect solid results in the subjects most relevant for this Master’s (especially mathematics and computer science). Practical programming experience and evidence of scientific work (e.g., your thesis) are strongly recommended to strengthen your application. These conditions are intended as minimum requirements to successfully study and complete the programme.
Admission requirements (bullet points)
Winter Semester (International)
The application period runs from 15 October to 15 December. We strongly recommend that applicants from non-European countries submit all application documents between 15 October and 15 November to ensure that the documents can be checked early enough so there is enough time left to fix potential visa issues!
Summer Semester (International)
15 November 2026
Summer Semester (EU/EEA)
15 December 2026
Graduates are prepared for technical roles developing and deploying AI systems in industry, research institutions and start-ups, as well as for positions in data science, cognitive computing, and related fields. The programme’s combination of rigorous theory, applied projects and a research thesis also provides a solid foundation for continuing into a PhD or other research careers.
Because the curriculum covers both practical software development and theoretical foundations, alumni can move into specialist roles (e.g., machine learning engineer, computer vision/NLP developer) as well as into managerial positions responsible for integrating and safely operating AI technologies.
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