The school provides tutorials and practical sessions on basic and advanced topics of machine learning by leading researchers in the field. The summer school is intended for students, young researchers and industry practitioners with an interest in machine learning and a strong mathematical background.

The school addresses the following topics: Learning Theory, Bayesian inference, Monte Carlo Methods, Sparse Methods, Reinforcement Learning, Robot Learning, Boosting, Kernel Methods, Bayesian Nonparametrics, Convex Optimization and Graphical Models.

Detailed information can be found at the summer school homepage.