Introduction to Machine Learning: A Statistical Learning Theory Approach

Problem Set 2 Posted

Tuesdays, 9:00-11:00, Ziskind Room 1

This half-course will provide a basic introduction to the fundamental concepts of Machine Learning, through the eyes of Statistical Learning Theory. The purpose is to both gain an appreciation and understanding of Machine Learning, and to introduce Statistical Learning Theory and the PAC-framework as a theoretical tool for rigorously studying Machine Learning.

In this brief half-course, we will focus almost exclusively on supervised classification, although the concepts we will discuss are relevant in a much wider context.

Specific Topics:

The following topics will be covered at the beginning of the second semester as a continuation to the material covered in the course:


There will be 2 homework assignments, each counting towards 20% of the final grade.

Problem Set 1: Theoretical Problems (corrected)

Note two corrections in the original version.

Problem Set 2: Experimentation

Required code and data file (code and data updated March 1st: details).


There is no set book for the course, nor are there any books covering precisely the material presented in the form presented. A good reference covering much of the theoretical material is the following survey: Some other books you might find relevant include: You might also find lecture notes in the following courses useful:


Tuesday December 23rd (Boaz):
Tuesday December 30th (Boaz):
Validating a Hypothesis and Concentration Bounds for the Binomial.
Tuesday January 6th:
Tuesday January 13th:
Tuesday January 20th:
Tuesday January 27th:
Tuesday February 3rd:

Last modified: Sun Mar 01 13:06:42 Jerusalem Standard Time 2009