Spring 2011
Foundations of Machine Learning

Course#: G22.2566-001
Instructor: Mehryar Mohri
Grader: Chris Alberti
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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:

Note: except from a few common topics only briefly addressed in G22.2565-001, the material covered by these two courses have no overlap. It is strongly recommended to those who can to also attend the Machine Learning Seminar.

Location and Time

Room 109 Warren Weaver Hall,
251 Mercer Street.
Mondays 5:00 PM - 6:50 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.



There is no single textbook covering the material presented in this course. Here is a list of books recommended for further reading:

Technical Papers

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


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