CMPUT 366 - Intelligent Systems

Fall 2004
Department of Computing Science
University of Alberta


Instructor: Dale Schuurmans, Ath409, x2-4806, dale@cs.ualberta.ca

Course outline

Introduction
Lecture 1 Introduction to Artificial Intelligence Thur Sep 9
Part 1 Reasoning
Lecture 2 Automating reasoning: formal inference Tues Sep 14
Lecture 3 Correct & exhaustive reasoning Thur Sep 16
Lecture 4 Constraint satisfaction search Tues Sep 21
Lecture 5 Problem solving search Thur Sep 23
Lecture 6 Automated planning Tues Sep 28
Lecture 7 Planning algorithms Thur Sep 30 A1 due
Lecture 8 General first order representation Tues Oct 5 P0 due
Lecture 9 a Planning in logic, First order inference Thur Oct 7
Part 2 Knowing
Reading Knowledge representation (R&N2 Ch. 10)
Part 3 Interpreting
Lecture 10 Automating interpretation systems Tues Oct 12
Lecture 11 Probability modelling Thur Oct 14 A2 due
Lecture 12 Structured probability models Tues Oct 19
Lecture 13 Efficient probabilistic inference Thur Oct 21
Lecture 14 Inference in complex models Tues Oct 26
Midterm break
Lecture 15 Interpreting senses (perception) Tues Nov 2
Lecture 16 a Interpreting natural language Thur Nov 4 A3 due
Part 4 Behaving
Lecture 17 Optimal behavior: Decision theory Tues Nov 9
Remembrance Day
Lecture 18 Optimal sequential decision making Tues Nov 16
Lecture 19 Optimal behavior: Game theory Thur Nov 18
Lecture 20 Scaling up: Partial observability Tues Nov 23 A4 due
Reading Robotics and control (R&N2 Ch. 25)
Part 5 Learning
Lecture 21 Types of learning problems Thur Nov 25
Lecture 22 Function learning algorithms Tues Nov 30
Lecture 23 Generalization theory / Overfitting Thur Dec 2
Conclusion
Lecture 24 Course review Tues Dec 7 Project due
Mon Dec 20 Final exam 2-5pm, CSC B-10