Stochastic Systems and Modern Machine Learning Theory

(3 Credits)

This course provides a comprehensive introduction to the mathematical foundations of stochastic systems and stochastic controls in discrete time. The course also explores the applications of stochastic controls in the theoretical developments of modern machine learning, including reinforcement learning, generative diffusion models, deep neural network training, and fine-tuning large language models. | Prerequisites: PhD standing or Permission from FRE Department. Co-requisites: (Recommended but not required): ECE-GY 6253 or ECE-GY 6233 (or similar).