Course Reference

software | homeworks & projects | handouts

  • Days: Tuesday / Thursday
  • Time: 2:00 p.m. - 3:20 p.m.
  • Room: CS 219
  • Instructor: Rina Dechter

Course Description

One of the main challenges in building intelligent systems is the ability to reason under uncertainty, and one of the most successful approaches for dealing with this challenge is based on the framework of Bayesian belief networks. Intelligent systems based on Bayesian networks are currently being used in a number of real-world applications including diagnosis, sensor fusion, on-line help systems, credit assessment, and data mining.

The objective of this class is to provide an in-depth exposition of knowledge representation and reasoning under uncertainty using the framework of belief networks.  Both theoretical underpinnings and practical considerations will be covered, with a special emphasis on constructing graphical models and on exact and approximate inference algorithms. Additional topics include learning belief network parameters from Data, dynamic belief networks, reasoning about actions and planning under uncertainty.


  • Familiarity with basic concepts of probability theory.
  • Knowledge of basic computer science, algorithms and programming principles.
  • Previous exposure to AI is desirable but not essential.

Tentative Syllabus





Week 1

  • Introduction: Reasoning about beliefs using Logic and Probability (Pearl Chapters 1-2)



  • Basic Bayes inference


Week 2

  • Bayesian network representation I:Independence Properties Syntax and Semantics (Pearl Chapter 3)



  • Bayesian network representation II: Directed graphical models of independence


Week 3

  • Bayesian network representation III: Undirected graphical models of independence



  • Knowledge Engineering of Bayesian networks


Week 4

  • Exact inference using variable elimination methods



  • Complexity of inference tree-width


Week 5

  • Distributed inference I: Polytrees and jointrees (Pearl chapter 4)



  • Distributed inference II: Polytrees and jointrees


Week 6

  • Conditioning schemes Hybrids of inference and conditioning time-space tradeoffs



  • Canonical models & local representation techniques


Week 7

  • Maximal a posteriori computations (MPE, MAP) and their applications (Pearl chapter )5



  • Approximate inference: Stochastistic methods Variables elimination methods Iterative belief propagation


Week 8

  • Learning Bayesian networks


Week 9

  • Decision and control I: Influence diagrams Maximizing expected utility Dynamic Bayesian networks (Pearl chapter 6)



  • Thanksgiving


Week 10

  • Assorted topics: Markov decision processes Inference: Policy iteration, value iteration Causality and action Students presentation


Readings (partial list)


  • Judea Pearl. Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, 1990.
  • Finn V. Jensen. An introduction to Bayesian networks. UCL Press, 1996.
  • Robert G. Cowell, A. Philip Dawid, Steffen L. Lauritzen, David J. Spiegelhalter Probabilistic Networks and Expert Systems Springer-Verlag, 1999
  • Castillo, E.; Gutierrez, J.M.; Hadi, A.S., Expert Systems and Probabilistic Network Models, Springer-Verlag 1997

Free Software

Related Links


There will be homework assignments and students will also be engaged in projects.

Grading Policy:

Homeworks and projects (50%), midterm (50%)