This course is inspired by Stanford Stats 385, Theories of Deep Learning, taught by Prof. Dave Donoho, Dr. Hatef Monajemi, and Dr. Vardan Papyan, as well as the IAS@HKUST workshop on Mathematics of Deep Learning held during Jan 8-12, 2018. The aim of this course is to provide graduate students who are interested in deep learning a variety of mathematical and theoretical studies on neural networks that are currently available, in addition to some preliminary tutorials, to foster deeper understanding in future research.

Prerequisite: There is no prerequisite, though mathematical maturity on approximation theory, harmonic analysis, optimization, and statistics, etc. will be helpful.