This course objective is to provide a rigorous theoretical introduction to the field of Statistical Learning. It will cover several topics in supervised and unsupervised frameworks. Non asymptotic methods for the extraction of information in random settings will be analyzed through tools from probability, statistics and optimization.

This course is heavily theory oriented but a few practical sessions on R or Python will be provided during PC.