The textbook is:

- 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] |