CSC 2532 Winter 2021: Statistical Learning Theory

This course covers several topics in classical machine learning theory. We will try to answer questions like:

Topics may include: Asymptotic statistics, Uniform Convergence, Generalization, Complexity measures, Kernel Methods, Online Learning, Sampling. More details can be found in syllabus. Please also sign up for Piazza.

This class requires a good informal knowledge of probability theory, linear algebra, real analysis (at least Masters level). Homework 0 is a good way to check your background.


Instructors: Murat A. Erdogdu

Teaching Assistants: TBA, TBA

Time & Location:

Section Room Lecture time
L0101 online M 10-12

Suggested Reading

No required textbooks. Suggested reading will be posted after each lecture (See lectures below).

Lectures and (tentative) course outline

Week Day Topics and Lecture notes Lectures Timeline
1 1/11 Introduction & Warm-up: Gaussian Mean Estimation lecture 1 syllabus
2 1/18 Exponential Families and Information Inequality lecture 2 -
3 1/25 Asymptotic statistics lecture 3 hw1 out
4 2/01 Uniform convergence & Generalization lecture 4 -
5 2/08 Covering with epsilon-nets lecture 5 hw1 due & hw2 out
7 2/22 Rademacher complexity I lecture 6 -
8 3/01 Rademacher complexity II lecture 7 hw2 due & hw3 out
9 3/08 Combinatorial Measures of Complexity lecture 8 -
10 3/15 Midterm (in class)   hw 3 due
11 3/22 Chaining and Dudley’s theorem lecture 9 project proposal due
12 3/29 Kernel Methods I lecture 11 (lec10 is postponed)
13 4/05 Kernel Methods II lecture 12 Final reports due


Homework # Out Due TA Office Hours
Homework 0 - V0 1/11 - -
Homework 1 - V0 1/23 2/09 23:00 2/04 2pm-4pm
Homework 2 - V2 2/09 2/25 23:00 2/24 3pm-4pm
Homework 3 - V1 3/01 3/12 23:00 3/10 3pm-4pm

Latex template can be found here.


Your project goal is to read and write a comprehensive review of a theoretical machine learning paper, and understand the main building blocks.

Project Inspiration: You can go through recent papers on COLT, NeurIPS, ICML, ICLR, JMLR to get project ideas and pick a paper to review. Several research directions from last year can be found here, but the list is by no means comprehensive. If you have suggestions, let me know.

Latex template for reports can be found here.

Computing Resources

For the homework assignments, we will use Python, and libraries such as NumPy, SciPy, and scikit-learn.