Theoretical Machine Learning (Fall 2026)

Schedule is subject to small adjustments.
Lecture notes will be posted before each lecture (and might be updated slightly soon after the lecture).

Date	Topics	Lecture Notes and Recommended Reading
08/28	Introduction, statistical learning, online learning, no free lunch theorem, online-to-batch conversion	Lecture notes 1 Sections 3-5 of R&S
09/04	Uniform convergence, Rademacher complexity, finite class Classification: growth function, VC dimension, Sauer's lemma
09/11	Regression: covering number, Dudley entropy integral, chaining technique, Pseudo-dimension
09/18	Regression: fat-shattering dimension Case study on neural nets: dimension-independent covering number spectral complexity, margin
09/25	Online learning: empirical process with dependent data, sequential Rademacher complexity, finite class bound Online classification: zero-covering number
10/02	Online classification: Littlestone dimension Online regression: covering number, chaining, fat-shattering dimension Online algorithms for finite classes: Halving and Hedge
10/09	Fall Recess
10/16	Online algorithms for infinite classes with bounded Littlestone dimension, Perceptron, Online Convex Optimization, Follow-the-Regularized-Leader
10/23	From values to algorithms Learning with partial information, multi-armed bandits, EXP3, Explore-then-Exploit
10/30	UCB, Lower bound for multi-armed bandits Partial monitoring: classification theorem
11/06	Partial monitoring: algorithms and lower bounds
11/13	Student presentations
11/20	Student presentations
11/27	Thanksgiving
12/04	Student presentations