Course Syllabus
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CS 371 - Introduction to Artificial Intelligence
Course Syllabus |
| Class | Date | Topic | Readings (parenthesized reading are optional) |
| 1 | 1/21 | Introduction, "What is AI?"; Uninformed search: problem definition | Course Information page; (Skim R&N Ch. 1, 2); R&N 3.1-3.3; Deep Blue article |
| 2 | 1/23 | Uninformed Search: breadth-first search, depth-first search, recursive depth-first search, depth-limited search, iterative-deepening depth-first search | 3.4, HW1 Starter Code and Documentation, (Peg Solitaire Demonstration) |
| 3 | 1/28 | Uninformed Search: repeated state detection, in-class implementation | |
| 4 | 1/30 | Informed Search: heuristic search, best-first search, uniform cost search, greedy search | rest of Ch. 3 except 3.5.5 (memory-bounded search) |
| 5 | 2/4 | Stochastic Local Search: hill descent with uphill step probability, simulated annealing, parameter tuning, demonstrations, challenge problem implementation | 4.1, (4.2), Skim Science Simulated Annealing article |
| 6 | 2/6 | Stochastic Local Search: second challenge problem | |
| 7 | 2/11 | Game-Tree Search: minimax, alpha-beta pruning | 5.1-5.3; game-tree search slides; (FYS 187-4 videos: game trees, alpha-beta pruning) |
| 8 | 2/13 | Game-Tree Search: heuristic evaluation, in-class heuristic development | 5.4; (FYS 187-4 videos: heuristic/static evaluation, heuristic features) |
| 9 | 2/18 | Game-Tree Search: time management | H. Baier, M. Winands. Time Management for Monte-Carlo Tree Search in Go. (skim time management strategies) |
| 10 | 2/20 | Game-Tree Search: expectiminimax | 5.5; Poker Squares rules/play grid, Learning and Using Hand Abstraction Values for Parameterized Poker Squares, (slides); (FYS 187-4 video: chance node, expectimax, and optimal Pig play reasoning), (optional: utility slides (esp. 8, 11, 14-15, 22-24) of AI and Ethics talk) |
| 11 | 2/25 | Game-Tree Search: Monte Carlo Tree Search (MCTS), UCT | Section 2 through 2.3 iof L. Kocsis, C. Szepesvari. Bandit based Monte-Carlo Planning.; Sections 1 (through 1.1) and 3.1 of C. Browne et al. A Survey of Monte Carlo Tree Search Methods |
| 12 | 2/27 | Machine Learning: Dynamic Programming | "Solving the Dice Game Pig: an introduction to dynamic programming and value iteration", Sections 1-2; (For reference in class presentation: Pig slides) |
| 13 | 3/4 | Machine Learning: Dynamic Programming (cont.), Value Iteration | "Solving the Dice Game Pig: an introduction to dynamic programming and value iteration", remainder; Russell & Norvig sections 17.1-17.2 |
| 14 | 3/6 | Machine Learning: Value Iteration (cont.) | |
| 15 | 3/18 | In-class Midterm | |
| 16 | 3/20 | Q-Learning: definitions, backup diagram, update rule, application to Pig Solitaire | section 6.5, of Reinforcement Learning: an introduction |
| 17 | 3/25 | Q-Learning: application to Pig Solitaire (continued) | |
| 18 | 3/27 | Q-Learning: application to Pig Solitaire (completed) | |
| 19 | 4/1 | Data Science and Machine Learning: Supervised, Unsupervised, and Reinforcment Learning | ml-cs-core-1.ipynb |
| 20 | 4/3 | Data Science and Machine Learning: Underfitting/overfitting, bias-variance tradeoff, performance measures, train/validation/test datasets | ml-cs-core-2.ipynb |
| 21 | 4/8 | Data Science and Machine Learning: Data Preprocessing, ML Concepts/Tradeoffs, Error Sources; Q-Learning Demonstrations | ml-cs-core-3.ipynb |
| 22 | 4/10 | Data Science and Machine Learning: Data Preprocessing, ML Concepts/Tradeoffs, Error Sources; Q-Learning Demonstrations | ml-cs-core-3.ipynb |
| 23 | 4/15 | Data Science and Machine Learning: Neural Networks, Q-Learning Demonstrations | ml-cs-core-4.ipynb |
| 24 | 4/17 | Data Science and Machine Learning: Neural Networks (cont.), ML and Ethics | ml-cs-core-4.ipynb |
| 25 | 4/22 | Probabilistic Reasoning: Bayesian Networks, Gibbs Sampling, a Markov Chain Monte Carlo (MCMC) algorithm for reasoning on Bayesian networks | 14.1-14.2, An Introduction to Monte Carlo Techniques in AI - Part II, 14.5.2, Pearl, Judea. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Revised 2nd Printing, Morgan Kaufmann, San Francisco, California, USA, 1988, Table 1 (p. 197), section 4.4.3 (pp. 210-216, up to "Justifying the Computations") |
| 26 | 4/24 | Probabilistic Reasoning (cont.) | |
| 27 | 4/29 | Course Evaluations, ML Exercises | |
| 28 | 5/1 | 4th Hour Project Demos | |
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Final Exam Schedules
on Registrar page under "Resources" (Fall,
Spring)
Final: Monday, May 5th, 8:30-11:30AM |