Class |
Date |
Topic |
Reading |
Homework |
Comments |
|
| 1 | Tu 8/31 | Course overview; What is AI? | Ch. 1, Lisp Ch. 1, McCarthy paper | HW1(P) out | Slides | |
| 2 | Th 9/2 | Agents/Lisp | Ch. 2, Lisp Ch. 2-3, Graham article |
|
Slides
Lisp debugging handout Fibonacci example handout Emacs reference card |
|
| 3 | Tu 9/7 | Problem solving as search; Lisp | Ch. 3.1-3.3, Lisp Ch. 4-5, App. A | Slides | ||
| 4 | Th 9/9 | Uninformed search | Ch. 3.4-3.7 | (see 9/7 for slides); example from class: neg.lisp; example from Lisp session: graph.lisp; graph1.lisp; graph2.lisp | ||
| 5 | Tu 9/14 | Informed search | Ch. 4.1-4.2, Lisp Ch. 7 | HW1 due; HW2 (PW) out |
Slides | |
| 6 | Th 9/16 | Local search, genetic algorithms | Ch. 4.3,4.5-4.6 | (see 9/14 for slides); words.lisp; | ||
| 7 | Tu 9/21 | Constraint satisfaction | Ch. 5 | Slides | ||
| 8 | Th 9/23 | Game playing | Ch. 6.1-6.2 | Slides | ||
| 9 | Tu 9/28 | Game playing II | Ch. 6.3-6.8 | (See 9/24 for slides) | ||
| 10 | Th 9/30 | Knowledge-based agents; project overview | Ch. 7 | Project teams formed; Project description out; HW2 due; HW3 (W) out; Problems from textbook | Slides | |
| 11 | Tu 10/5 | Propositional logic (review) |
Slides | |||
| 12 | Th 10/7 | First-order logic | Ch. 8 | Slides; Mastermind project description; mm.lisp; mm-solver.lisp | ||
| 13 | Tu 10/12 | Logical inference | Ch.9 | Slides | ||
| 14 |
Th 10/14 |
Philosophy and history of AI |
Ch. 26, 27, Turing article; Searle article; Three Laws of Robotics (Wikipedia) |
HW3 due |
Chronology of AI | |
| 15 | Tu 10/19 | State-space and partial-order planning |
Ch. 10.3 | HW4 (PW) out Problems from textbook |
Slides | |
| 16 | Th 10/21 | Partial-order and hierarchical planning | Ch. 11.1-11.3, 12.2 | (See 10/20 for slides) | ||
| 17 | Tu 10/26 | MIDTERM (covers material through class #14) |
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| 18 | Th 10/28 | Probabilistic reasoning |
Ch. 13 |
Slides | ||
| 19 | Tu 11/2 | Bayesian networks |
Ch. 14 | HW4 due; HW5 out (from 3rd edition) |
||
| 20 | Th 11/4 | Machine learning I: decision trees | Ch. 18.1-18.3 | Project design due |
Slides | |
| 21 | Tu 11/9 | Machine learning II: k-nearest neighbor, naive Bayes, learning Bayes nets | Ch. 20.1-20.4 | train-biases.lisp, Bias #1 training data, Bias #2 training data, Bias #3 training data; Slides, kNN slides | ||
| 22 | Th 11/11 | Machine learning III: neural networks, support vector machines, clustering | Ch. 20.5-20.8 | Slides | ||
| 23 | Tu 11/16 | Markov decision processes; probabilistic planning | Ch. 15.1, 16.1-16.3, 17.1-17.2 | Tournament dry run #1: Fixed-size and scalability challenges; Slides | ||
| 25 | Th 11/18 | Buffer - used for earlier topics that expanded beyond their
allotted time or to introduce a new ML topic |
HW5 due; HW6 out | |||
| 24 | Tu 11/23 | Reinforcement learning | Ch. 21.1-21.3 | Basic RL slides; TD slides | ||
| Thu 11/25 | Happy Thanksgiving -- enjoy your turkey! | |||||
| 26 | Tu 11/30 |
Multi-agent systems I |
Ch. 16.4, 17.6-17.7 |
Slides Data for test biased choosers and probabilistic choosers (#4): test-bias1.txt; test-bias2.txt; test-bias3.txt; test-bias4.txt; train-bias4.txt; train-bias4.lisp |
||
| 27 | Th 12/2 | Multi-agent systems II |
Slides: See 12/1 | |||
| 28 | Tu 12/7 | Singularity Debate | HW6 due |
Tournament dry run #2: Learning challenge | ||
| 29 | Th 12/9 | Tournament | Tournament | |||
| -- | Th 12/16 | FINAL EXAM 1:00 - 3:00pm | Project and final report due |
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