Course: STAT 37201=DATA 37200
Title: Learning, Decisions, and Limits
Instructor: Frederic Koehler, Haifeng Xu
Class Schedule: Sec 1: TR 12:30 PM–1:50 PM in Crerar Library 011
Description: This is a graduate course on theory of machine learning. While ML theory has multiple branches in general, this course is designed to cover basics of online learning, along with basics of reinforcement learning. It aims to establish the foundation for students who are interested in conducting research related to online decision making, learning, and optimization. The course will introduce formal formulations for fundamental problems/models in this space, describe basic algorithmic ideas for solving these models, rigorously discuss performances of these algorithms as well as these problems’ fundamental limits (e.g., minmax/lower bounds). En route, we will develop necessary toolkits for algorithm development and lower bound proofs.