ECE 543: Statistical Learning Theory (Spring 2018)

Maxim Raginsky (maxim at illinois dot edu)
Teaching Assistants:
  • Georgios Rovatsos (rovatso2 at illinois edu)
  • Surya Sankagiri (ss19 at illinois dot edu)
  • Puoya Tabaghi (tabaghi2 at illinois dot edu)
About this course
Regular weekly schedule
Academic integrity policies


January 16

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.
Required readings will be drawn from:
Grades will be based on regular written homeworks (40% total), two ninety-minute exams (20% each), and a project (20%).

Regular weekly schedule

Tue Thu 2:00-3:20 pm, 2015 ECE Building
Office hours:
instructor day/time TBA, 162 CSL
Due Thursdays, by the end of the day, uploaded to Compass (details to follow soon).
Homeworks are released at least one week before the due date.
Under normal circumstances, graded homework should be returned within 10 days of submission.