Csc2547 / Sta4273: Topics in Statistical Learning Theory

Murat A. Erdogdu, University of Toronto, Winter 2019

Objectives

Your project goal is to make a significant contribution to understanding a machine learning related problem. An ideal project will begin with an interesting observation, later explained through theory, and end with a thorough empirical analysis. Several research directions can be found below, but the list is by no means comprehensive, and your project topic need not be drawn from it. You will review relevant literature, find interesting research directions, and either develop novel methodology, or explain an observed behavior related to a learning algorithm.

Collaboration policy

You may work on the project alone or in a group of three; the standards for a group project will be higher. We strongly encourage you to come to office hours to discuss your project ideas, progress, and difficulties with the course staff.

Evaluation

Evaluation will be based on two reports:

  • Progress report 15%: 1 page, to be submitted on Feb 28 stating your preliminary results, findings.

  • Final report 25%: 2 pages, to be submitted on Mar 28 stating your final results. You must use this latex template for your project reports.

Project Inspiration

You can go through recent papers on COLT, NeurIPS, ICML, JMLR to get project ideas. Several research directions can be found below, but the list is by no means comprehensive. If you have suggestions, let me know.

Sampling and optimization

Theory of deep learning