deeplearning-math.github.io

Advanced Topics in Deep Learning

This course is a continuition from Math 6380P, Fall 2018, 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 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 will be helpful. it-yourself (DIY) and critical thinking (CT) are the most important things in this course. Enrolled students should have some programming experience with modern neural networks, such as PyTorch, Tensorflow, MXNet, Theano, and Keras, etc. Otherwise, it is recommended to take some courses on Statistical Learning (Math 4432 or 5470), and Deep learning such as Stanford CS231n with assignments, or a similar course COMP4901J by Prof. CK TANG at HKUST.