Fall 2004
Department of Computing Science
University of Alberta
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 |