|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 email@example.com