DATA 37200: Learning, Decisions, and Limits (Winter 2026)
Basic Information
Class Location: JCL 011Class Time: Tu/Thu 12:30 to 1:50 pm
Instructor: Frederic Koehler
- Office: Room 303, 5460 S. University Avenue
- Office Hour: TBA
Course Material: There will not be any official textbook, but the slides and links to reading materials will be posted on the course schedule after each lecture. You can see last year's version of the course. This year's class will be updated a bit from last year.
Learning Objectives: (1) Understand basic toolkits for online learning and online decision making, as a complement to offline learning paradigm; (2) Prepare students to understand state-of-the-art RL algorithms, such as RLHF and AlphaGo training.
Course 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.
This is primarily a theory course, and lecture-based. That said, we will focus primarily on proofs over coding, although there may be a small amount of coding to deepen understanding. Prerequisites include linear algebra (at the level of CMSC 25300 or its equivalent), algorithms (CMSC 27200 or its equivalent) and probability (STAT 25100 or its equivalent). If not sure, consult with the instructor. Note that no background on learning theory is required.Lectures and Readings
| Lec No. | Lectures | Readings |
|---|---|---|
| 1 (Jan 6) | Intro and MAB |
Homework
| Due date | Homework | Note |
|---|
Requirements and Grading
Grades consist of two components: (1) ~3 mostly proof-based hw assignments (30% ); (2) in-person midterm and final (70%).
Late Homework Policy : Each student is allowed one late homework for at most two days from the due date. You may choose whichever homework to use this chance (or not use it). No additional late homework will be accepted.