The goal of this class is to introduce you to the theoretical foundations of machine learning, including learning models and generalization bounds. We will cover the following topics:


A prerequisite for this class is basic knowledge of probability and some previous exposure to machine learning.


There is no textbook for this class. Some suggested references are: I may hand out specific readings in class. The readings will be either based on my own notes, or chapters from the upcoming book on learning theory by Shai Ben-David and Shai Shalev-Shwartz.