Fall 2004

Department of Computing Science

University of Alberta

Introduction | |||||
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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 |
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Lecture 8 | General first order representation | Tues Oct 5 | P0 due |
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Lecture 9 a | Planning in logic, First order inference | Thur Oct 7 | |||

Part 2 | Knowing | ||||

Reading |
Knowledge representation | (R&N2 Ch. 10) |
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Part 3 | Interpreting | ||||

Lecture 10 | Automating interpretation systems | Tues Oct 12 | |||

Lecture 11 | Probability modelling | Thur Oct 14 | A2 due |
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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 |
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Lecture 15 | Interpreting senses (perception) | Tues Nov 2 | |||

Lecture 16 a | Interpreting natural language | Thur Nov 4 | A3 due |
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Part 4 | Behaving | ||||

Lecture 17 | Optimal behavior: Decision theory | Tues Nov 9 | |||

Remembrance Day |
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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 |
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Reading |
Robotics and control | (R&N2 Ch. 25) |
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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 |
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Mon Dec 20 | Final exam |
2-5pm, CSC B-10 |