University of Toronto - Winter 2017
Department of Computer Science

CSC 384: Introduction to Artificial Intelligence

Lecture Slides and Readings


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The course material will be covered primarily in lectures and tutorials. Some examples will be done in class only, and will not appear in these notes. It is your responsibility to take notes in class to augment  these slides with the extra pertinent information presented during class.

The recommended text book also contains material that will help clarify the topics covered in the lectures.

Topic Readings
Russell and Norvig (R&N)
Class
Slides
Notes
Introduction
What is AI
Chapter 1 presents a more complete and very interesting overview of the history and goals of AI research.

Chapter 2 also contains some interesting ideas about one way to think about the structure of AI systems.

01-Introduction
01-Introduction (4 pp)

Uninformed and Heuristic Search Chapter 3 presents the search techniques covered in the lectures.

Chapter 4 can be read for enrichment at this point. We'll return to some ideas in this chapter later in the course.

02-Uninformed Search
02-Uninformed Search (4pp)

02-Heuristic Search
02-Heuristic Search (4pp)

02-Heuristic Search Tutorial
02-Heuristic Search Tutorial(4pp)

[Sheila:] These are interim slides and may be updated slightly.

Why Watson incorrectly responded "What is Toronto?" (from a friend on the Watson team)

Ariel Felner's discussion of Dijkstra's Algorithm vs UCS

Properties of uninformed search from RN 3rd edition (with their algorithms).

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