Description

This course discusses advanced topics in machine learning. Each week, one technical paper will be presented and discussed by one or several students. I will be also giving various tutorials on selected topics to stimulate the discussion. Occasionally, external speakers will also be invited. An expected outcome of the seminar is research publications or software in areas related to machine learning.

Location and Time

Room 813 Warren Weaver Hall,
251 Mercer Street.
Tuesdays 1:25 PM - 3:15 PM.

Prerequisite

Familiarity with basics in linear algebra, probability, and analysis of algorithms. Interest in theoretical and applied machine learning. Prior acquaintance with machine learning concepts as presented or discussed in the following courses: Previous classes in machine learning ("Foundations of Machine Learning", "Machine Learning and Pattern Recognition", or the Ph.D. seminar in machine learning) is a plus.

Coursework

Participants are expected to study and present one or several technical papers. The final grade will be based on participation, the quality of the presentations, and potential publications.