
d2 represents Talagrand’s convex-hull distance on the cube. (Image by Prof. Dmitry Panchenko.)
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.
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As Taught In: | Spring 2007 |
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Graduate
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Learning Resource Types
notes
Lecture Notes
assignment
Problem Sets