6.867 Machine Learning

This introductory course on machine learning will give an overview of many techniques and algorithms in machine learning, beginning with topics such as simple perceptrons 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 and why they work. The underlying theme in the course is statistical inference as this provides the foundation for most of the methods covered.

• M. Jordan and C. Bishop Introduction to Graphical Models (draft version for MIT only access, do not distribute).
• C. Bishop. Neural Networks for Pattern Recognition
  
• Duda, Hart, Stork. Pattern Classification (3rd edition).
Other reading material such as papers will be made available electronically.

Lectures are Tue/Thu 2:30-4pm in 37-212.

There are two (identical) weekly tutorial sections:

Wed	11am-12pm	38-166
Fri	3pm-4pm	36-156

Contact Information

Instructor	Professor Tommi Jaakkola tommi@ai.mit.edu NE43-735 253-0440 Office hours: Thursday 5-6pm (NE43-735)
Teaching Assistant	Greg Shakhnarovich gregory@ai.mit.edu NE43-V611 253-8170 Pre-final week: Monday 12/2 - office hours 11am-noon, 3-5 pm Tuesday 12/3 - review session 5-7pm in NE43-941 (note that the Lecture on Tuesday takes place as usual) Wednesday 12/4 - office hours 11am-noon,4-6pm Thursday 12/5 - final in class 2:30-4pm