
Topics in Machine Learning
(Fall 2016)

[Course description]
[Announcements]
[Lectures]
[Assignments]
[Reading material]
Course description
Course staff:
 Instructor: Ohad Shamir
 Teaching Assistant: Itay Safran (itay.safran at weizmann dot ac dot il)
Time and Location: Tuesday 13151600, Ziskind building, room 1
Syllabus:
This course will provide a selfcontained introduction to some of the activelyresearched 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.
Announcements
(8/1) Slightly modified formulation of question 1, assignment 4, to clarify that the regret should be with respect to any vector u of norm at most B.
(1/1) Assignment 4 uploaded, due January 15th.
(1/1) Assignment 2 is graded and can be picked up from Itay Safran's mailbox
(12/12) Assignment 1 is graded and can be picked up from Itay Safran's mailbox
(12/12) Assignment 3 uploaded, due January 1st. The first two problems require material that will be taught in next week's class (December 18)
Assignment 2 deadline is extended till Thursday, December 13th, 3:30PM (the assignments can be placed in Itay Safran's mailbox on the 2nd floor of Ziskind building).
Assignment 2 uploaded, due December 11th.
Updated the guidance in question 3. You should consider r' in [r_m,r^*] in the first part, and instances of distance less than r_m in the second.
Assignment 1 uploaded, due November 15th.
Assignments
Reading material
The course does not follow any specific text, but much of the first twothirds is covered by the following:
Additional Sources include:
 Kearns and Vazirani: An Introduction to Computational Learning Theory, MIT Press, 1994
 CesaBianchi and Lugosi: Prediction, Learning and Games, MIT Press, 2006
 Hastie, Tibshirani and Friedman: The elements of statistical learning, Springer, 2009
 Introduction to Online Optimization, Sébastien Bubeck, Microsoft Research