Theoretical foundations of Machine Learning

Objective

This course is a global introduction to machine learning methods. If the differences between statistics and machine learning might seem tedious, as some techniques are used and developed in both fields, the basic idea is that statistics focuses on the estimation of some parameters - and their interpretation -, while maching learning focuses on prediction. The course will present the theoretical basis of machine learning, then several examples of the mots popular prediction algorithm, and we will conclude on the formal modelling of ethical questions. On one side, we shall understand how to mathematically analyse algorithm performances, and on the other hand, through practical session in Python how these methods can be used in practice.

Planning

1. Introduction, formal models of machine learning.

2. Plug-in methods

3. Selection of models/variables, cross-validation

4. Empirical Risk Minimization 

5. Decision Trees

6. Neural Nets

7. Ethics (Privacy & Fairness)