Princeton University |
Computer Science 402 |
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Numbers under the R&N column refer to chapters or sections of the Russell & Norvig text (3rd edition). Other additional required or optional readings and links are also listed below.
This syllabus is constantly evolving as the semester progresses, so check back often (and let me know if it seems not to be up to date).
# |
Date |
Topic |
Readings (required) |
Other (optional) readings and links |
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R&N |
other |
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| 1 | Th 9/12 | General introduction to AI. | 1 | AI Growing Up by James Allen (but skip or skim page 19 to end). |
AAAI website with
LOTS of readings on AI in general, AI in the news, etc. Computing Machinery and Intelligence by Alan Turing. |
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| 2 | Tu 9/17 | Uninformed (blind) search | 3.1-3.4 | |||
| 3 | Th 9/19 | Informed (heuristic) search | 3.5-3.6 | |||
| 4 | Tu 9/24 |
Local search; Searching in games |
4.1 5 (ok to skip 5.5-5.6) |
"The Chess Master and the Computer" by Garry Kasparov Play checkers with Chinook |
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| 5 | Th 9/26 | Propositional logic | 7.1-7.4 | |||
| 6 | Tu 10/1 | Theorem proving and the resolution algorithm | 7.5 | handout on converting to CNF | "The Logic Theory Machine" by Allen Newell and Herbert A. Simon (1956) -- the first AI paper on theorem proving. | |
| 7 | Th 10/3 | Practical methods of solving CNF sentences | 7.6 | Clause Learning in SAT by R. Tichy, T. Glase | Heavy-Tailed Phenomena in Satisfiability and Constraint Satisfaction Problems by C. Gomes, B. Selman, N. Crato, H. Kautz | |
| 8 | Tu 10/8 |
Applications of solving CNF sentences, including planning; Cursory look at first-order logic; Uncertainty and basics of probability |
7.7; 10.1; 10.4.1; 8.1-8.3 (ok to skim); 13.1-13.3 |
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| 9 | Th 10/10 | Independence and Bayes rule | 13.4-13.5 | "What is the chance of an earthquake?" (article on interpreting probability, by Freedman & Stark) | ||
| 10 | Tu 10/15 | Bayesian networks: semantics | 14.1-14.3 | "Introduction to probabilistic topic models" by David Blei | brief tutorial on Bayes nets (and HMM's), with links for further reading | |
| 11 | Th 10/17 | Exact and Approximate inference with Bayesian networks | 14.4-14.5 | |||
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12 13 14 |
Tu 10/22 Th 10/24 Tu 11/5 |
Uncertainty over time (temporal models; HMM's); Kalman filters | 15.1-15.4 | formal derivations | ||
| 15 | Th 11/7 | Kalman Filters, DBN's, particle filters; | 15.5 | |||
| 16 | Tu 11/12 |
DBNs; particle filtering; begin decision theory; MDP |
17.1 |
The basics of utility theory: 16.1-16.3 from R\&N; |
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17 18 |
Th 11/14 Tu 11/19 |
Markov decision processes: Bellman equations, value iteration, policy iteration | 17.1-17.4.1 |
Sutton &
Barto's excellent book on reinforcement learning and MDP's Value Iteration Demo |
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| 19 | Th 11/21 |
Machine Learning Decision trees |
18.1-18.4 | |||
| 20 | Tu 11/26 |
Neural networks Theory of learning |
18.6-18.7 18.5 |
generalization error theorem proved in class | A demo of LeNet, a neural network for optical-character recognition, is available here. Click the links on the left to see how it does on various inputs. The figure shows the activations of various layers of the network, where layer-1 is the deepest. (For more detail, see the papers on the LeNet website, such as this one.) original "Occam's Razor" paper | |
| 21 | Tu 12/3 |
Theory of learning Support-vector machines |
18.5 18.9 | tutorial on SVM's | ||
| 22 | Th 12/5 | Bagging and Boosting | 18.10 | margins "movie" | introductory chapter from Boosting: Foundations and Algorithms by Schapire & Freund | |
| 23 | Tu 12/10 |
Clustering Learning Bayes net and HMM parameters |
20.1-20.3 | |||
| 24 | Th 12/12 | Reinforcement learning in MDP's | 21.1-21.4 |
Sutton &
Barto's excellent book on reinforcement learning and MDP's Learning to play keepaway in robocup soccer using reinforcement learning. Scroll down to find flash demos. |
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