Seminar Event Detail

Student AIM Seminar

Date:  Friday, September 29, 2017
Location:  1084 East Hall (4:10 PM to 5:00 PM)

Title:  Basics of Statistical Learning Theory

Abstract:   Statistical learning theory provides the general framework for analyzing the performance of supervised learning algorithms. In this talk, I will illustrate some basic concepts that are essential for the study of learning rate of an algorithm, using a simple example, where a weird distribution is successfully learned by a surprisingly simple algorithm. And at the end, I will briefly discuss the implication of Devroye's No Free Lunch Theorem, and how the fast learning rate is possible for kernel support vector machine.


Speaker:  Yitong Sun
Institution:  University of Michigan

Event Organizer:   Audra McMillan


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