Lecture notes

• The current version of the lecture notes (in english) can be found here.
• A zoomed version for accessibility can be found here.
• The lecture notes of the former course on Machine Learning (before 2015), in french, can be downloaded here. Warning : it does not fit anymore the current content of the lectures.

Slides

Below are the slides projected during some lectures :

• Some chapters on statisical Machine Learning
  • Vector quantization : read only sections about "notation" and "controlling the quantization accuracy"
  • Ensemble methods
  • Risks
  • Preprocessing/dimensionality reduction
  • Deep learning

• Chapters on Reinforcement Learning
  • Bandits
  • Reinforcement learning

• Chapters on Bayesian Machine Learning
  • 1. Introduction
  • 2. Theoretical Foundations
  • 3. Example of Naive Bayes
  • 4. Latent Variables and EM
  • 5. Discrete Markov Models
  • 6. Continuous Markov Models
  • 7. Gaussian Processes
  • 8. Sampling techniques