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) |
Slides | Notes |
|---|---|---|---|
| Term Specific Information Introduction What is AI? |
Chapter 1 presents a more complete and
very interesting overview of the history and goals of AI research. Chapter 2 gives more detail about viewing AI systems as agents interacting with various environments. |
Introduction (1pp) Introduction (4pp) |
A lovely NYT article that contrasts Deep Blue's style of game play to that of Alpha Go and that, unfortunately, may be behind a paywall for some. Gary Kasparov's take on recent advances in gaming is also worth a read and can be found here: Sheila McIlraith weighs in on Watson's handling of the Toronto question. |
| Uninformed and Heuristic Search | Chapter 3 presents the search techniques covered in the lectures
(in a somewhat different way)
Chapter 4 can be read for enrichment. |
Uninformed Search (1pp) (revised 1/15) Heuristic Search, Part 1 (4pp) (revised 1/19) Heuristic Search, Part 2 (1pp) Heuristic Search, Part 2 (4pp) |
A more detailed analysis of the state space of sliding tile puzzles can be found here.
Some practice problems (revised 1/13) |
Backtracking Search (CSPs) | Chapter 6.1, 6.2, 6.3 |
Backtracking Search - Part 1 (Annotated) Backtracking Search - Part 2 (Annotated) Backtracking Search - Part 3 (Annotated, updated 2/6) |
Andrew Moore's CSP animations | Game Tree Search |
Chapter 5.1, 5.2, 5.3 (R&N,3rd ed) Chapter 5.7 also makes for interesting reading. |
Game Tree Search - Part 1 (Annotated)
Game Tree Search - Part 2 (Annotated, updated 2/29) |
Knowledge Representation and Reasoning |
Chapter 7-9 and 12 (R&N 3rd ed) Chapter 7-10 (R&N 2nd ed) |
KRR - Part 1 (Annotated) KRR - Part 2 (Annotated, updated 3/11) |
Gordon Novak has some good KR Problem Sets on his web page:
|
| Representing and Reasoning under Uncertainty | Chapter 13 and 14. |
Probability Review (1pp)
Probability Review (4pp) (Updated 3/17)
Bayesian Networks, Continued (1pp)
Reasoning over Time (Hidden Markov Modelss) (1pp)
Intro to Bias in Classification (1pp)
|
A couple of probability review problems.
VE and D-Separation problems
Additional notes on sampling from BNs (Note that sampling is NOT covered on the take home final) |