University: Massachusetts Institute of Technology
Instructors: Prof. Dmitry Panchenko
Course Number: 18.465
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.
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)
Talagrand, M. "Concentration of Measure and Isoperimetric Inequalities in Product Spaces." Publ Math IHES 81 (1995): 73-203.
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There are two problem sets for this course. The grade is based upon these problem sets and class attendance.