News

• The project has now been posted.
• Homework 5 has been posted Friday March 28. It is due Friday April 4.
• The midterm will take place Wednesday March 19, 5:30-7:30pm. It covers all material up until the break. Sample exams from 2005 and 2006 are available. the exam is open book.
• Homework 4 has been posted Wednesday March 5, 2008. It is due Wednesday March 12.
• Homework 3 has been posted Monday February 11. It is due Monday February 18, in class.
• Doina's office hours are changing starting on the week of January 21 (see the updated schedule below).
• Homework 2 has been posted Friday January 18, 10am. It is due Friday January 25, in class.
• Notes for lecture 7 have been posted and notes for lecture 6 have been updated Friday January 18, 10am.
• Notes for lecture 6 have been posted Tuesday January 15, 10:15pm.
• Notes for lecture 5 have been posted Sunday January 13, 11:15pm.
• Notes for lecture 3 have been updated Friday, January 11, 10:30am.
• Notes for lecture 4 have been posted Friday, January 11, 00:30am.
• Homework 1 has been posted Wednesday January 9, 10:30am. It is due Wednesday, January 16, in class.
• Notes for lecture 2 have been updated and notes for lecture 3 have been posted on Jan. 8, 11:45pm.
• The class is starting Friday, January 4.

General Information

When: Monday, Wednesday, Friday, 10:35-11:25am.

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 uncertainty and for 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. For details, see the syllabus and tentative schedule.

Instructor

Office Hours: Monday 11:30-12:00 and 5:00-6:00 and Friday 2:30-3:30. Note that office hours may be cancelled on some days, so check Doina's calendar beforehand. Meetings at other times are available by appointment only

Phone: (514) 398-6443
E-mail: dprecup at cs dot mcgill dot ca

Teaching assistant

References

1. Lecture notes are available on the schedule web page.
2. The class material is based on selected chapters from the following textbooks:
   • An Introduction to Probabilistic Graphical Models by Michael Jordan. Hard copies of the relevant chapters will be made available to registered students.
   • Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto. MIT Press, 1998.
   • Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig, Second Edition. Part V and VI. Prentice Hall, 2002.
   • Information Theory, Inference and Learning Algorithms by David MacKay. Cambridge University Press, 2003.
   • Pattern Recognition and Machine Learning by Christopher Bishop. Springer, 2006.
   The relevant portions will be indicated on the schedule web page as needed.
3. Other materials (e.g. additional research papers) will be posted on the schedule web page as needed.