Master 2024/2025

Theoretical Methods of Deep Learning

Type: Elective course (Math of Machine Learning)

When: 2 year, 2 module

Open to: students of one campus

Language: English

Course Syllabus

Abstract

Deep Learning (DL) is a highly promising and popular applied science that, at present, is poorly understood theoretically. We know that neural networks work well, but cannot fully explain why. Nevertheless, in the last few years, there has been a rapid growth of publications that shed light on the new mathematics underlying DL, and we see now many interesting connections between DL and other fields such as approximation theory, differential equations, information theory, random matrix theory and statistical physics. This course aims to introduce students to these cutting-edge developments.