Machine Learning is the study of algorithms that improve automatically through experience. Topics covered typically include Bayesian learning, decision trees, genetic algorithms, Markov models and neural networks. The course goals include:

To expose students to concepts and methods in machine learning.
To give students a basic set of machine learning tools applicable to a variety of problems.
To teach students critical analysis of machine learning approaches so that the student can determine when a particular technique is applicable to a given problem.

Course Information

prerequisites: Significant prior programming experience equivalent to EECS 214 (formerly EECS 311) or graduate standing
location/time of lectures: Tech Lecture Room 3, Mon, Wed 2pm - 3:20pm
Recitation 1: Tech MG28, Fri 2pm - 3pm
Recitation 2: Tech LG52, Fri 2pm - 3pm
Recitation 3: Tech F279, Fri 2pm - 3pm * DOCTORAL SECTION *
textbook: No required textbook
reading: Selected papers and book chapters. See course calendar.

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name: Bryan Pardo
office location: 3.323 Ford Building
office phone number: 847 491 7184
office hours:
Wed 3:30pm - 4:00pm (in Professor's office)
Fri 3:00 - 4:00pm (in Tech F279)

Teaching Assistant 1

name: Ethan Manilow
office location: Room 3.317 West Lounge, Ford Building
office hours:Fri 3:00pm - 5:00pm

Teaching Assistant 2

name:Bongjun Kim
office location: Room 3.317 West Lounge, Ford Building
office hours:Tue 4:00pm - 6:00pm