INSTRUCTORS: Joelle Pineau, Sylvie Ong
TAs: Mahdi Milani Fard, Yuri Grinberg, Athena Kardehi Moghaddam, Guillaume Saulnier
Term: Winter 2012.
When: Monday / Wednesday 2:35-3:55pm
Where: Macdonald 280

Course syllabus (including info on TAs, office hours, contact information.)

Course schedule (including lecture slides, homeworks, solutions.)

Announcements

• Jan.9 First day of classes.
  

Description

We will cover selected topics in Artificial Intelligence. We will study modern techniques for computers to make good (in some cases optimal) decisions that are applicable throughout an enormous range of industrial, civil, medical, financial, robotic and information systems. We will not attempt to cover the entire range of AI sub-areas in detail, but will survey several key themes.

Course Outline

• Introduction to AI. Brief history. Different agent architectures. (1 lecture).
• Search: uninformed and heuristic search, A*, local search and optimization. (2 lectures).
• Constraint satisfaction problems. (1 lecture).
• Game playing and adversarial search. (1 lecture).
• Knowledge representation. Logical reasoning. Propositional logic. Planning (4 lectures).
• Reasoning under uncertainty. Bayes rule. Belief networks. (3 lectures).
• Supervised learning methods. Decision trees. Neural networks. (3 lectures).
• Decision making. Utility theory. Reinforcement learning. Game theory. (4 lectures).
• Special topics: Robotics, Natural Language Processing, Medical applications (3 lectures)
• Wrap-up. (1 lecture).