Remember: there is no excuse for ignorance of the assigned reading material. Visit this page frequently. Bring a friend. Start a grassroots political movement.

Date	Topic	Reading	Out	In
Jan 8	Introduction and Overview	Chapter 1	7641 Group Project
Jan 10	Supervised Learning Review: Neural Networks & Decision Trees	Chapters 3 & 4
Jan 15	Instance Based Learning	Chapter 8	Assignment 1
Jan 17	Boosting Support Vector Machines	handouts		Team Formation (1/18)
Jan 22	Support Vector Machines Boosting	handouts
Jan 24	Bayesian Learning	Chapter 6
Jan 29	Bayesian Learning			Informal Proposal (1/28)
Jan 31	Bayesian Learning
Feb 5	Computational Learning Theory	Chapter 7
Feb 7	Computational Learning Theory			Formal Proposal (2/8) Keywords (2/8) Assignment 1 (2/8)
Feb 12	Addressing Overfitting Information Theory	Chapter 5 handouts
Feb 14	Randomized Optimization	Chapter 9	Assignment 2
Feb 19	Randomized Optimization	handouts
Feb 21	Overflow
Feb 26	Midterm Exam
Feb 28 Drop Day is 2/29	Exam Review			Suggestions re: Formal Proposals (2/29)
March 4	Clustering	handouts
March 6	Expectation Maximization impossibility results (clustering and NFL)	Chapter 6 (again) handouts	Assignment 3	Assignment 2 (3/9)
March 11	Feature Selection	handouts
March 13	Feature Transformation
March 18 March 23	Spring Break
March 20	Hidden Markov Models
March 25	Markov Decision Processes			Progress Report
March 27	POMDPs		Assignment 4
April 1	Reinforcement Learning	Chapter 13		Assignment 3 (4/2 extended to 4/7)
April 3	Reinforcement Learning	handouts		Submitted Papers (4/6)
April 8	Reinforcement Learning
April 10	Game Theory			Review of Final Papers (4/14)
April 15	Game Theory
April 17	Game Theory			Final Project Material (4/20)
April 22 April 24	Final Presentations			Review of Final Presentations (4/25) Assignment 4
April 28-May 2	Final Exam Thurs May 1, 11:30-2:20pm