M2 course, ENS Lyon: Machine Learning

Machine Learning

Lecturer

Aurélien Garivier

Course description

The aim of this course is to introduce the main problems and theoretical aspects of Machine Learning.
The focus will be mainly on supervised classification, with a few extensions on non-supervised learning (clustering) and regression.
Each course will be the opportunity of a focus on a particular technique or tool of general interest (such as deviation inequalities, statistical tests, stochastic optimization, etc.).

Course outline and slides

Prerequisite

Basic knowledge of probability theory, linear algebra and analysis over the reals

Evaluation

In addition to homework and in-class exercices, students will chose between

In both case, they will prepare a written report and an oral presentation. The final grade will be a function of all these.

Maybe useful information

Bibliography

  1. Understanding Machine Learning, From Theory to Algorithms, by Shai Shalev-Shwartz and Shai Ben-David
  2. A Probabilistic Theory of Pattern Recognition, by Luc Devroye, Laszlo Györfi and Gabor Lugosi
  3. The Elements of Statistical Learning, by Trevor Hastie, Robert Tibshirani and Jerome Friedman
  4. Introduction to Nonparametric Estimation, by Alexander Tsybakov
  5. Lectures notes on advanced Statistical Learning , by Martin Wainwright

Notebooks

  1. Introduction to ML: synthetic example
  2. Presentation of the MNIST dataset and nearest-neighbor classification
  3. Dimensionality reductions: numerical experimentation on the MNIST dataset
  4. Reinforcement Learning: Retail Store Management

Slides for 2018-2019