Explore regularized least squares in this comprehensive lecture by Lorenzo Rosasco from MIT, University of Genoa, and IIT. Delve into key concepts including loss functions, optimality conditions, quadratic programming, and gradient descent. Learn about the class of numbers, super vectors, and perception as part of the 9.520/6.860S Statistical Learning Theory and Applications course. Gain valuable insights into statistical learning theory and its practical applications over the course of 80 minutes.
Overview
Syllabus
Introduction
Loss function
Optimality Condition
Quadratic Programming
Class of Numbers
SuperVectors
Perception
Gradient Descent
Taught by
MITCBMM
