Fall 2014
Foundations of Machine Learning

Course#: CSCI-GA.2566-001
Instructor: Mehryar Mohri
Graders/TAs: Vitaly Kuznetsov, Andres Munoz Medina, Scott Yang.
Mailing List

Course Description

This course introduces the fundamental concepts and methods of machine learning, including the description and analysis of several modern algorithms, their theoretical basis, and the illustration of their applications. Many of the algorithms described have been successfully used in text and speech processing, bioinformatics, and other areas in real-world products and services. The main topics covered are:

It is strongly recommended to those who can to also attend the Machine Learning Seminar. Those interested in further pursuing the study of machine learning could also attend the Advanced Machine Learning class.

Location and Time

Warren Weaver Hall Room 109,
251 Mercer Street.
Mondays 5:10 PM - 7:00 PM.


Familiarity with basics in linear algebra, probability, and analysis of algorithms.

Projects and Assignments

There will be 3 to 4 assignments and a project. The final grade is essentially the average of the assignment and project grades. The standard high level of integrity is expected from all students, as with all CS courses.



The following is the required textbook for the class. It covers all the material presented (and a lot more):

Technical Papers

An extensive list of recommended papers for further reading is provided in the lecture slides.


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