Machine Learning

Artificial Intelligence (AI) has become nearly ubiquitous, with a tremendous impact on science, economy, healthcare, and society at large. At the core of AI systems are machine learning algorithms – computer programs that can be trained on large data sets and improve with experience. Machine learning is the engine that powers state-of-the-art AI applications, from image recognition systems, decision-support systems, self-driving cars and humanoid robots, to seemingly intelligent chatbots like ChatGPT.

The Machine Learning Group studies both statistical learning theory and applications of learning algorithms. The group active in three different, but partially overlapping research directions:

1. model evaluation and selection (including statistical evaluation of performance of algorithms and data resampling techniques);
2. integration and analysis of high-dimensional data from the life and health sciences, as well as from sports science (specifically football);
3. continual learning and deep learning.

We are currently collaborating with the University of Évry Paris, the University of Aberystwyth, UK, and Tokyo Institute of Technology, Japan.