Généralités

This master class on learning theory covers the classical PAC framework for learning, stochastic gradient descent, tensor methods. We also touch upon topics from the recent literature on mean field methods for NN and the double descent phenomenon.

Teacher: Nicolas Macris: nicolas.macris@epfl.ch  

Teaching Assitant: Perrine Vantalon - perrine.vantalon@epfl.ch

Courses: Mondays 8h15-10h Room INM202; Exercises: Tuesdays 17h15-19h Room INR219.

We will use this moodle page to distribute homeworks, solutions, and lecture material each week. As well as use the discussion and questions forum. Dont hesitate to actively use this forum.

MIDTERM: there will be a midterm on monday March 30 at 8h15 - 10h in room INM202. This will count 30% towards the final grade. Its open book (you can bring your notes, printed material, the UML book, or download material on your laptop before hand and have wifi switched off). 

EXAM: the final exam during official exam session will count for 70% of the final grade. Its open book (you can bring your notes, printed material, the UML book, or download material on your laptop before hand and have wifi switched off). 

Textbooks and notes:

• Understanding Machine Learning (UML) by Shalev-Shwartz and Ben David
  
• A paper on double descent phenomenon
  
• Introduction to Tensor Decompositions and their Applications in Machine Learning (Ranbaser, Shchur, Gunneman)
• One lecture on two-layer neural networks