After successful completion of the course, students are able to:
This lecture introduces theoretical foundations and advanced topics in machine learning. We analyse learning algorithms and show provable guarantees, such as (probabilistic) bounds on the predictive performance.
Tentative topics:
A mix of introductory online lectures (recorded and/or live), recorded online talks (like conference and summer school tutorials), and exercises with formaitve feedback and some live online sessions where the assigments are discussed.
3ects -> 75h20h (re)viewing lectures and lecture materials10h (re)viewing background material 10h exercises20h coursework15h Final project
The final grade consists of