Description
This introductory course on machine learning will give an overview of many concepts, techniques, and algorithms in machine learning, beginning with topics such as classification and linear regression and ending up with more recent topics such as boosting, support vector machines, hidden Markov models, and Bayesian networks. The course will give the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how, why, and when they work. The underlying theme in the course is statistical inference as it provides the foundation for most of the methods covered.
General Information
Prerequisites
(6.041 or 18.05) and 18.06; 6.034 is helpful
Lectures
Tue. and Thu. 9:30AM - 11AM in 54-100
Staff mailing list
6867-staff-2012@lists.csail.mit.edu
Instructor in charge
Leslie Pack Kaelbling
Recitations
They will be on Fridays, at 12, 1, 2, and 3. They are optional and you can attend any session(s) you want to. There will be two types of recitations; see Syllabus for details. You may ignore the recitation time that you have been assigned by the registrar.
Office hours: 34-501
Monday 4PM - 6PM
Wednesday 6PM - 10PM
Wednesday 6PM - 10PM
Name | Office Hours | |
---|---|---|
Tomas Lozano-Perez | When? Where? | |
Chris Amato | When? Where? | |
Leslie Kaelbling | When? Where? | |
Simon LUI | When? Where? |
Homework
Homework
Due Date
11/15/2012
11/15/2012
10/30/2012
10/25/2012
10/25/2012
10/03/2012
10/03/2012
10/03/2012
Lecture Notes
Lecture Notes
Lecture Date
Nov 27, 2012
09/13/2012
10/01/2012
10/17/2012
10/16/2012
10/26/2012
11/12/2012
Nov 26, 2012