Class |
Date |
Topic |
Reading |
Homework |
Comments |
|
1 | Th 9/1 | Course overview; What is AI? | Ch. 1; Lisp Ch. 1; McCarthy paper | Pretest and HW1(PW) out | Lisp "cheat sheet"
Survey Slides |
|
2 | Tu 9/6 | Agents/Lisp | Ch. 2; Lisp Ch. 2-3; Graham article |
|
Slides
|
|
3 | Th 9/8 | Problem solving as search; Lisp | Ch. 3.1-3.3; Lisp Ch. 4-5, App. A | Pretest due | Slides | |
4 | Tu 9/13 | Uninformed search | Ch. 3.4 | Slides
|
||
5 | Th 9/15 | Informed search | Ch. 3.5-3.7, Lisp Ch. 7 | HW1 due; HW2(PW) out |
Slides | |
6 | Tu 9/20 | Local search, genetic algorithms | Ch. 4.1-4.2 | Student feedback #1 |
(Slides: See 9/15)
In-class Lisp functional programming example (with bug fixed) Marie's code (has more variations) |
|
7 | Th 9/22 | Constraint satisfaction | Ch. 6; Vipin Kumar, "Algorithms for Constraint Satisfaction Problems: A Survey" | Slides | ||
8 | Tu 9/27 | Game playing | Ch. 5.1-5.3 | Slides
Walking robots! |
||
9 | Th 9/29 | Game playing II | Ch. 5.4-5.9 | (Slides: See 9/27) | ||
10 | Tu 10/4 | Knowledge-based agents; start propositional logic | Ch. 7.1-7.3 | HW2 due; HW3(W) out; Project description out | Slides | |
11 | Th 10/6 | Propositional and first-order logic | Ch. 7.4-7.8, 8 | Slides | ||
12 | Tu 10/11 | (First-order logic, continued) | Ch. 9 | Project teams formed | ||
13 | Th 10/13 | Logical inference | Slides | |||
14 |
Tu 10/18 |
Philosophy and history of AI |
Turing article Searle article Summary of Kurzweil's book Kevin Kelly's critique |
HW4(W) out | ||
15 | Th 10/20 | Logical inference, continued; knowledge representation | Ch. 12.1-12.2, 12.5-12.6 | HW3 due | Slides | |
16 | Tu 10/25 | State-space and partial-order planning | Ch. 10.1-10.2, 10.4.2-10.4.4 | Slides | ||
17 | Th 10/27 | MIDTERM (covers material through class #14) |
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18 | Tu 11/1 | Probabilistic reasoning |
Ch. 13 |
HW4 due; HW5(W) out | Guest lecturer: Yasaman Haghpanah
Slides |
|
19 | Th 11/3 | Bayesian networks |
Ch. 14.1-14.4 | Slides | ||
20 | Tu 11/8 | Machine learning I: Decision trees | Ch. 18.1-18.4 | Project design due | Slides
train-biases.lisp train-bias1.txt train-bias2.txt train-bias3.txt train-bias4.txt |
|
21 | Th 11/10 | Machine learning II: K-nearest neighbor, naive Bayes, learning Bayes nets | Ch. 20.1-20.2 | Dr. desJardins out of town -- guest lecturer: Kevin
Winner
Slides |
||
22 | Tu 11/15 | Swarm systems | Dr. desJardins out of town -- guest lecturer: Dr. Don Miner | |||
23 | Th 11/17 | Decision making under uncertainty | Ch. 15.1-15.2, 16.1-16.3 | HW6(W) out; Tournament dry run #1 (HW5 due Fri 11/18 @ 2:30pm) |
Slides | |
24 | Tu 11/22 | Probabilistic planning | Ch. 17.1-17.3 | (Slides: see 11/17) | ||
Th 11/24 | Happy Thanksgiving -- enjoy your turkey! | |||||
25 | Tu 11/29 | Reinforcement learning | Ch. 21.1-21.3 | Slides | 26 | Th 12/1 |
Multi-agent systems / Game Day |
Slides |
27 | Tu 12/6 | Swarm systems | Ch. 16.4, 17.5-17.6 | Guest lecturer: Dr. Don Miner
Slides |
||
28 | Th 12/8 | TBA/catchup | HW6 due; Tournament dry run #2 |
test-bias1.txt
test-bias2.txt test-bias3.txt test-bias4.txt | ||
29 | Tu 12/13 | Tournament | Tournament | |||
-- | Th 12/15 | FINAL EXAM (1:00-3:00) | ||||
-- | Mo 12/19 | Project and final report due |