Knowledge
After completing this programme, you will
- have advanced knowledge of central methods in machine learning and artificial intelligence, including methods for modelling, prediction, generation, and data-driven decision making
- understand important theoretical, algorithmic, and empirical foundations of modern machine learning and AI systems, including their assumptions, strengths, limitations, and evaluation principles
- have knowledge of how machine learning and AI methods can be used to analyse and generate different types of data, such as language, images, video, and time series
- know central concepts in responsible and trustworthy AI, including issues related to fairness, privacy, transparency, safety, and governance
Skills
After completing this programme, you will be able to
- formulate relevant problems in machine learning and artificial intelligence, choose suitable methods, and justify methodological choices in light of the task, the data, and the intended use
- implement, adapt, and evaluate machine learning and AI methods and systems for analysis, generation, reasoning, or decision support using rigorous experimental methodology, relevant baselines, and appropriate validation strategies
- analyse results critically, assess uncertainty and limitations, and communicate findings clearly and precisely
- carry out an independent research-based or development-oriented master’s thesis using relevant scientific methods and in accordance with research ethics and professional standards
General competence
After completing this programme, you will be able to
- work independently and in teams, communicate results to specialists and non-specialists, and act professionally
- critically reflect on societal impacts of machine learning and contribute to discussions on responsible use and governance of machine learning systems