CSCE 582 Spring 2010 Syllabus

CSCE 582 Spring 2010: Syllabus

The textbook is:

Jensen, Finn V. Bayesian Networks and Decision Graphs. New York: Springer-Verlag, 2007 (ISBN-10 0-387-68281-3; ISBN-13: 978-0-387-68281-5) (Approximately $72 at amazon.com. This text is referred to as [J07].) Other recommended books on the topic are:

Adnan Darwiche. Modeling and Reasoning with Bayesian Networks Cambridge, UK: Cambridge University Press, 2009 (ISBN 978-0-521-88438-9).
Daphne Koller and Nir Friedman. Probabilistic Graphical Models: Principles and Techniques. Cambridge, MA: The MIT Press, 2009 (ISBN 978-0-262-0139-2).
Timo Koski adn John M. Noble. Bayesian Networks: An Introduction. Chichester, UK: Wiley, 2009 (ISBN 978-0-470-74304-1).
Uffe B. Kjaerulff and Anders L. Madsen. Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis. New York: Springer-Verlag, 2008 (ISBN 978-0-387-74100-0).
Kevin B. Korb and Ann E. Nicholson. Bayesian Artificial Intelligence. London: Chapman & Hall and CRC Press, 2004 (ISBN 1-58488-387-1).
Jensen, Finn V. Bayesian Networks and Decision Graphs. New York: Springer-Verlag, 2001 (ISBN 0-387-95259). (This text is referred to as [J01].)
Robert G. Cowell, A. Philip Dawid. Steffen L. Lauritzen, and David J. Spiegelhalter. Probabilistic Networks and Expert Systems. New York: Springer, 1999 (ISBN 0-387-98767-3).
Jensen, Finn V. An Introduction to Bayesian Networks. New York: Springer-Verlag, 1996 (ISBN 0-387-91502-8). (This text is referred to as [J96].)
Richard E. Neapolitan. Probabilistic Reasoning in Expert Systems: Theory and Algorithms. New York: Wiley, 1990 (ISBN 0-471-61840-3).
Judea Pearl. Probabilistic Reasoning in Intelligent Systems. San Francisco: Morgan-Kaufmann, 1988 (ISBN 0-934613-73-7).

The main (approximately 80% of the time) instructional delivery strategy for this course is lectures. Discussions based on student presentations, videos, quizzes or in-class exercises, and a possible invited talk will make up the remaining 20% of time. The first day of classes is Friday, August 20, 2010. The last day to withdraw without failure is Thursday, October 7, 2010. The last day of classes is Friday, December 3, 2009. The final exam for the course is Saturday, December 11, 2010, at 1400 (2pm) in the classroom (SWGR 2A27). This is the regularly scheduled time for courses taught from 1325 to 1415 on Mondays, Wednesdays and Fridays. (See the university exam schedule.) The syllabus is tentative.

Week Begins	Lecture Topics	Readings
1: August 20	Probability and Reasoning	Ch.1 [J07]; instructor's slides
2: August 23, 25, 27	Probability and Reasoning;	Ch. 1 [J07]
3: August 30, September 1, 3	Causal and Bayesian Networks	Ch.2 [J07]
4: September 8, 10	Causal and Bayesian Networks	Ch.2 [J07]
5: September 13, 15, 17	Building Models: Capturing the Structure and Determining the Conditional Probabilities	Sections 3.1 and 3.2 [J07]
6: September 20, 22, 24	Building Models: Capturing the Structure and Determining the Conditional Probabilities and Midterm	Sections 3.1 and 3.3 [J07] and Notes on the Stratum Method
7: September 27, 29; October 1	Building Models: Advanced Modeling Methods and Special Features	Sections 3.3 and 3.4 [J07]
8: October 4, 6, 8	Video on Probabilistic Relational Models (Daphne Koller) and Building Models: Advanced Modeling Methods and Special Features	Video; Sections 3.3 and 3.4 [J07]
9: October 11, 13	Belief Updating in Bayesian Networks	Ch.4 [J07]
10: October 18, 20, 22	Belief Updating in Bayesian Networks: Structure Based Algorithms	Sections 4.1-4.6 [J07]
11: October 25, 27, 29	Belief Updating in Bayesian Networks: Structure Based Algorithms	Sections 4.1-4.6 [J07]
12: November 1, 3, 5	Belief Updating in Bayesian Networks: Space-Time Tradeoff and Iterative Methods; Assignment of papers for graduate student presentations	Sections 4.7-4.9 [J07]
13: November 8, 10, 12	Graphical Languages for Decision Problems	Ch.9 [J07]
14: November 15, 17, 19	Topics in Decision Problems	Sections of Chs. 10 and 11 [J07]
15: November 22	Student Presentations	TBA
16: November 29; December 1, 3	Student Presentations (if needed); the EM Algorithm; Learning of BN Structure	Topics from Chs. 5, 6, 7 [J07]