ML Resources

For now, this is a reading list, designed for my students.

In English:

• The stanford class https://cs231n.github.io/ is excellent !
• The NYU Center for Data Science class https://www.youtube.com/watch?v=0bMe_vCZo30&list=PLLHTzKZzVU9eaEyErdV26ikyolxOsz6mq and possibly the notes related to the course: https://atcold.github.io/pytorch-Deep-Learning/
• The main reference (general Machine Learning): Pattern Recognition and Machine Learning, Christopher Bishop, 2006 (easily found – you don’t even need sci-hub for this one !)
• More of an information-theoretic perspective: Information Theory, Inference, and Learning Algorithms, David J.C. MacKay (same remark, just google it)
• https://scikit-learn.org/stable/user_guide.html  this is more than a simple documentation for using packages. These help pages always start with a documented description of the algorithms implemented in each class’ function. Links to the original papers are provided. And when you click down from classes to nested classes to functions, you find a usual documentation with arguments lists, etc.
• Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, Aurélien Géron https://github.com/yanshengjia/ml-road/blob/master/resources/
• https://www.deeplearningbook.org/ An MIT Press book -- Ian Goodfellow and Yoshua Bengio and Aaron Courville

In French:

• Machine Learning avec Scikit-Learn - 2e éd. - Mise en oeuvre et cas concrets, Aurélien Géron**

Suggestions by the distinguished Guillaume Charpiat (I think he has good taste):
https://www.lri.fr/~gcharpia/deeppractice/ressources.html

Suggestions by Isabelle Guyon: