Topics in statistics: statistical learning theory

Lectures: 3 sessions / week, 1 hour / session

Permission of instructor is required. Helpful courses (ideal but not required): Theory of Probability (18.175) and either Statistical Learning Theory and Applications (9.520) or Machine Learning (6.867)

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. We will develop a number of technical tools that will allow us to give qualitative explanations of why these learning algorithms work so well in many classification problems.

Topics of the course include Vapnik-Chervonenkis theory, concentration inequalities in product spaces, and other elements of empirical process theory.

The grade is based upon two problem sets and class attendance.

One-dimensional Concentration Inequalities

Vapnik-Chervonenkis Theory and More

Concentration Inequalities

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