Probabilistic Reasoning in AI (COMP-526)
Partial solutions to homework 6 are now posted here.
Solutions to homework 5 are now posted here.
Homework 6 is now posted here. It
is due Thursday April 8, midnight.
Grades for the first midterm are now posted here.
Slides for lecture 18 have been posted Tuesday, March 23, 4:45pm.
An exam web page is available with the list of
topics, and possible dates.
The web page has been updated with materials for EM and temporal
models, lecture slides for lectures 17, 19, and homework 5 (due
Friday, March 26, in class).
The midterm tomorrow is in Leacock 26, 5:45pm. You are allowed
one double-sided cheat sheet. Partial homework solutions are now
Homework 4 is now posted on the web page. It is due next Monday,
February 16. Note that homework 3 (programming junction tree
algorithm) will be posted tomorrow. Homework 4 covers material for
the exam, which is why it has been posted first.
Due to requests, I have posted my sketchy junction tree slides on the
web site. I strongly suggest that you look at them together with
chapter 17 of the textbook, which offers all the details.
All class materials have been updated. I have posted all lecture
notes and also materials from the textbook for the algorithms covered
so far. I also posted homework 2, due Friday, Feb. 6,
Slides for lecture 5 have been been updated on Friday, Jan. 16. The
placeholders have been replaced with actual slides. Also, the
textbook materials are now linked from the schedule web page
- I have sent an e-mail to my own class mailing list a couple of
days ago. It should have arrived at your mail.mcgill.ca account. If
you haven't got it, please send me an e-mail so I can update my
Where: Burnside Hall, Room 1B24.
When: Monday, Wednesday, Friday, 1:35-2:25pm.
What: One of the primary goals of AI is the design, control
and analysis of agents or systems that behave appropriately in a
variety of circumstances. Good decision making often requires the
existence of knowledge or beliefs about the agent's environment, as
well as about its own abilities to observe and change the environment,
and about its own goals and preferences. In this course we will
examine computational approaches for modeling the environment and
solving decision problems. We will focus mainly on probabilistic
models of reasoning, and on sequential decision making.
The course is intended for advanced undergraduate students and for
graduate students, and will provide an introduction to the on-going
research in the field of reasoning under uncertainty, which has been
very active during the last decade. We will cover the following
- The principles of Bayesian inference
- Belief networks
- Syntax and semantics
- Exact and approximate inference
- Learning methods
- Hidden Markov Models
- Dynamic Bayes nets
- Kalman filters
- Basics of utility theory
- Markov Decision Processes
- Dynamic programming methods
- Structured state and action spaces
- Temporal-difference learning algorithms
- Generalization and function approximation
- Partially Observable Markov Decision Processes
- Exact solution methods
- Approximate methods
School of Computer Science
Office: McConnell 326
Meetings at other times by appointment only
IMPORTANT: E-mail is the quickest way to
reach me and get your questions answered.
Office Hours: Monday and Friday, 4-5pm, McConnell 112. Meetings at other times by appointment only
- Lecture notes and other relevant materials are available on this
Last modified: Tue Apr 13 11:57:29 EDT 2004