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Course material for the 2022 PSL intensive ML week

Machine learning preparatory week @PSL


Machine learning part (from Monday to Friday)

  1. Machine learning: history, application, successes
  2. Introduction to machine learning
  3. Supervised machine learning models
  4. Scikit-learn: estimation and pipelines
  5. Optimization for linear models
  6. Optimization for machine learning
  7. Deep learning: convolutional neural networks
  8. Unsupervised learning

Spark and Machine Learning (Wednesday and Friday afternoons)

Slides from Dario Colazzo

Ethics and Fairness (Wednesday morning)

Slides from Thierry Kirat

Practical works

Links open Colab notebooks. You may also clone this repository and work locally.

  1. Monday: Python basics
  2. Tuesday: Practice of Scikit-learn
  3. Thursday: Optimization and the Corrected notebook
  4. At home: Classification with PyTorch and GPUs Notebook 1 Notebook 2 Notebook 3



The slides and notebooks were originally written by Pierre Ablin, Mathieu Blondel and Arthur Mensch.

Some material of this course was borrowed and adapted:


All the code in this repository is made available under the MIT license unless otherwise noted.

The slides are published under the terms of the CC-By 4.0 license.