Class Schedule

CSE 6392.004
Reasoning with Uncertainty for Data Interpretation, Modeling, and Decision Making
Tentative Lecture and Assignment Schedule
Fall Semester 2006 - TTh 7:00 - 8:20
Class	Date	Readings	Lecture Topics	Assignments
1	08/29	Slides	Introduction and Representations
2	08/31	FLL_Fuzzy_Logic_Tutorial , Slides	Fuzzy Logic
3	09/05		Bayesian & Non-Bayesian Belief Systems
4	09/07	RN13 , Slides	Bayesian Reasoning
5	09/12	RN14 , RN_Slides_14a	Bayesian Networks
6	09/14	Hal2.4 , Hal3.6 , Slides	Dempster Shafer Theory
7	09/19	Coh4-4.4, Slides	Significance Testing
8	09/21		Confidence Intervals
9	09/26	Slides	Optimal Estimation and Filtering
10	09/28	WB95	Kalman Filter	Homework 1 due
11	10/03		Sampling Methods
12	10/05	DFG1	Monte Carlo Methods & Bootstrap Filters
13	10/10		Particle Filters
14	10/12	Slides	Hidden Markov Models
15	10/17	RJ86	Learning Hidden Markov Models
16	10/19		Learning Hidden Markov Models
17	10/24	Slides ((c) S. Russell)	Markov Decision Processes	Project 1 (Code: Linux , Solaris (Gamma), Cygwin (under Windows XP) , OSX ) due
18	10/26		Dynamic Programming
19	10/31		Monte-Carlo Dynamic Programming
20	11/02		MDPs
21	11/07	LCK95	POMDPs	Homework 2 due
22	11/09		Multi-Agent Systems
23	11/14		Game Theory
24	11/16		Game Theory
25	11/21		Game Theory
	11/23		Thanksgiving - No Class
26	11/28		Auctions
27	11/30		Student Presentations
28	12/05		Student Presentations
29	12/07		Student Presentations	Project 2 due
30	12/14		Final Project Presentations (8:15 pm - 10:45 pm)

Readings

• RN13: S. Russel, P. Norvig, "Artificial Intelligence: A Modern Approach", 2nd Edition, Chapter 13 "Uncertainty".
• RN14: S. Russel, P. Norvig, "Artificial Intelligence: A Modern Approach", \ 2nd Edition, Chapter 14 "Probabilistic Reasoning".
• Hal2.4: J. Halpern, "Reasoning about Uncertainty", Section 2.4 "Dempster-Shafer Belief Functions".
• Hal3.6: J. Halpern, "Reasoning about Uncertainty", Section 3.6 "Conditioning Belief Functions".
• Coh4-4.4: P. Cohen, "Empirical Methods for Artificial Intelligence", MIT Press, 1995, Chapter 4 - 4.4, "Hypothesis Testing and Estimation".
• WB95: G. Welch, G. Bishop, "An Introduction to the Kalman Filter", Technical Report 95-041, Universitry of North Carolina at Chapel Hill, 1995 (Updated 2004).
• DFG1: A. Doucet, N. De Freitas, N. Gordon, "Sequential Monte Carlo methods in practice", Springer, 2001, Chapter 1 "An Introduction to Sequential Monte Carlo Methods".
• RJ86: L.R. Rabiner, B.H. Juang, "An Introduction to Hidden Markov Models", IEEE ASSP Magazine (3)1:4-16, 1986.
• LCK95: M. Littman, A. Cassandra, and L. Pack Kaelbling, "Learning Policies for Partially Observable Environments: Scaling Up", Proceedings of the Twelfth International Conference on Machine Learning, 1995
• RN17.6 S. Russel, P. Norvig, "Artificial Intelligence: A Modern Approach", 2nd Edition, Chapter 17.6-17.7 "Game Theory".