Machine learning preparatory week @PSL

Lectures

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

Teachers

• Zaccharie Ramzi (ENS, CNRS)
• Hugo Richard (ENSAE)
• Dario Colazzo (Dauphine Université)
• Thierry Kirat (Dauphine Université)

Acknowledgements

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

Some material of this course was borrowed and adapted:

• The slides from “Deep learning: convolutional neural networks” are adapted from Charles Ollion and Olivier Grisel’s advanced course on deep learning (released under the CC-By 4.0 license).
• The first notebooks of the scikit-learn tutorial are taken from Jake Van der Plas tutorial.

License

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.