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 VapnikChervonenkis theory, concentration inequalities in product spaces, and other elements of empirical process theory.
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As Taught In:  Spring 2007 
Level: 
Graduate

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