Course Content
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.
Practical Sessions
You will find the different practical sessions associated with each part of the course. Unfortunately we will not be able to deal with all of them during the sessions but you can do them on your side, at home
The first part contains main files and datasets used for all the practical sessions
TP 1 - An experimental protocol for supervised learning
Project
The following files contain the project associated to this course.