The Machine Learning pillar focuses on fundamental principles and algorithms for machine learning. Our core competences lie in probabilistic graphical networks, topological data analysis and artificial neural networks. This includes structure learning, inference, approximation algorithms, uncertainty quantification, model validation, and prediction. Group members have experience from basic research on foundational questions in the theory of machine learning all the way to algorithm implementation and machine learning applications. We actively collaborate with the algorithms, statistics, visualization, and bioinformatics pillars.