Throughout the course, we put the mathematical concepts into action with large-scale applications from machine learning, signal processing, and statistics.
This course describes theory and methods for Reinforcement Learning (RL), which revolves around decision making under uncertainty. The course covers classic algorithms in RL as well as recent algorithms under the lens of contemporary optimization.
This course describes theory and methods for decision making under uncertainty under partial feedback.
This course provides an overview of recent developments in online learning, game theory, and variational inequalities and their point of intersection with a focus on algorithmic development. The primary approach is to lay out the different problem classes and their associated optimal rates.