Topics in Modern Machine Learning

This is an advanced machine learning course covering some of the topics of interest in modern machine learning. After a boot camp on the first day (on statistical learning, machine learning models and optimization for machine learning), the rest of the week will be dedicated to introducing modern topics, including: implicit regularization, sketching, reinforcement learning, machine learning for inverse problems, optimal transport for machine learning, fairness in machine learning.

Each presentation, held in the morning, will be introductory and self-contained, with an associated practical session in the afternoon. The last day will be dedicated to a workshop with invited speakers.

The course is advanced and assumes students to have already taken an introductory ML course. Similarly, basic programming skills, probability and linear algebra/calculus knowledge is assumed. More advanced tools will be introduced in class in a self-contained manner.