In this course, we will take a look at Machine Learning in order to consolidate the theoretical, algorithmic and practical basics.
Learning methods based on bagging, boosting and stacking will then be presented. Following this, and depending on the time remaining, short introductions to the following topics will be given: PAC-Bayes theory, which is a very suitable theoretical framework for ensemble methods, metric learning and domain adaptation.

The aim of this course is to assimilate good experimental practices in order to be able to compare methods correctly. It also aims to present different aspects of Machine Learning via set methods.

Lectures

• Poly

• Slides I
• Slides II

Practical Sessions

• Fondamentaux
• Datasets
• Prep. Data

• Correction
• Code

Project