Topics in Machine Learning
(Fall 2016)

[Course description] [Announcements] [Lectures] [Assignments] [Reading material]

Course description

Course staff:

Time and Location: Tuesday 1315-1600, Ziskind building, room 1

Syllabus: This course will provide a self-contained introduction to some of the actively-researched areas in machine learning today. It will cover theoretical principles and challenges as well as practical algorithms. The focus will be on supervised and discriminative learning, where the goal is to learn good predictors from data while making few or no probabilistic assumptions. Along the way, we will introduce and use tools from probability, game theory, convex analysis and optimization.

Prerequisites: There are no formal prerequisites. However, the course requires mathematical maturity, and students are expected to be familiar with linear algebra and probability, as taught in undergraduate computer science or math programs.




Reading material

The course does not follow any specific text, but much of the first two-thirds is covered by the following: Additional Sources include: