# Machine learning preparatory week @PSL

## Lectures

- Machine learning: history, application, successes
- Introduction to machine learning
- Supervised machine learning models
- Scikit-learn: estimation and pipelines
- Optimization for linear models
- Optimization for machine learning
- Deep learning: convolutional neural networks
- Unsupervised learning
- Introduction to Relational Database Management Systems (video)

## Practical works

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

- Monday: Python basics
- Tuesday: Practice of Scikit-learn
- Wednesday: Optimization
- Thursday: Classification with PyTorch and GPUs
- Friday: Databases in practice with PostgreSQL and Python, Solutions

## Teachers

- Pierre Ablin (ENS, DMA)
- Mathieu Blondel (Google Brain)
- Arthur Mensch (ENS, DMA)
- Pierre Senellart (ENS, DI)

## Acknowledgements

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.