Homework 3 is posted, due by the end of the day on Mar 8.
NO CLASS on Feb 8; a make-up lecture will be scheduled for the second half of February
Homework 2 is posted, due by the end of the day on Feb 15.
Homework 1 is posted, due by the end of the day on Feb 1. Submission instructions can be found on the coursework page.
Welcome! Watch this space for all important course-related announcements.
The first lecture is this Tuesday, January 16.
The first homework will be assigned on Thursday, January 25, due next Thursday, February 1. Each student must submit their own individual solutions on Compass2g, details to be announced soon.
About this course
What is this?
Statistical learning theory is a burgeoning research field at the intersection of probability, statistics, computer science, and optimization that studies the performance of computer algorithms for making predictions on the basis of training data. The following topics will be covered: basics of statistical decision theory; concentration inequalities; supervised and unsupervised learning; empirical risk minimization; complexity-regularized estimation; generalization bounds for learning algorithms; VC dimension and Rademacher complexities; minimax lower bounds; online learning and optimization. Along with the general theory, we will discuss a number of applications of statistical learning theory to signal processing, information theory, and adaptive control.
Due Thursdays, by the end of the day, uploaded to Compass (submission instructions).
Homeworks are released at least one week before the due date.
Under normal circumstances, graded homework should be returned within 10 days of submission.