Machine Learning Theory

(STATS214/CS229M, Fall 2023)
Resources: We will have no official course text, but you may find the following resources useful: Prerequisites:

Course Policies: A detailed overview of course policies (including grading and assignments) can be found in the course syllabus.

Course description

The goal of this course is to give a mathematical framework for understanding machine learning. We will explore the following questions: How do machine learning algorithms work? How can we formally quantify their success? How much data do they need in order to learn? This is a theoretical, proof-based course, and our focus will be on algorithms with rigorous guarantees wherever they are to be had.

Lectures and Reading
Below is a preliminary schedule (subject to change), including the readings relevant to each lecture.