Course Description
The main goal of this course is to study the generalization ability of a number of popular machine learning algorithms such as boosting, support vector machines and neural networks. Topics include Vapnik-Chervonenkis theory, concentration inequalities in product spaces, and other elements of empirical process theory.
Course Info
Learning Resource Types
notes
Lecture Notes
assignment
Problem Sets
d2 represents Talagrand’s convex-hull distance on the cube. (Image by Prof. Dmitry Panchenko.)
