Course Syllabus
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CS 371 - Introduction to Artificial Intelligence
Course Syllabus |
| Class | Date | Topic | Readings (parenthesized reading are optional) |
| 1 | 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 | 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 | Uninformed Search: repeated state detection, in-class implementation | ||
| 4 | Informed Search: heuristic search, best-first search, uniform cost search, greedy search | rest of Ch. 3 except 3.5.5 (memory-bounded search) | |
| 5 | 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 | Stochastic Local Search: second challenge problem | ||
| 7 | 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 | Game-Tree Search: heuristic evaluation, in-class heuristic development | 5.4; (FYS 187-4 videos: heuristic/static evaluation, heuristic features) | |
| 9 | Game-Tree Search: time management | H. Baier, M. Winands. Time Management for Monte-Carlo Tree Search in Go. (skim time management strategies) | |
| 10 | 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) | |
| 11 | 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 | 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 | 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 | Machine Learning: Value Iteration (cont.) | ||
| 15 | In-class Midterm | ||
| 16 | Q-Learning: definitions, backup diagram, update rule, application to Pig Solitaire | section 6.5, of Reinforcement Learning: an introduction | |
| 17 | Q-Learning: application to Pig Solitaire (continued) | ||
| 18 | Q-Learning: application to Pig Solitaire (completed); Data Science and Machine Learning: Introduction with Linear Regression, Jupyter Notebooks | 01-intro.ipynb, 02-jupyter-notebook-basics.ipynb | |
| 19 | Data Science and Machine Learning: Introduction to Python Operators, Values, and Data Structures; Kaggle | 03-python-ops-vals-ds.ipynb, 04-kaggle.ipynb | |
| 20 | Data Science and Machine Learning: Introduction to Python Control Flow, Functions, and Errors; Nonlinear Regression | 05-python-control-fns-errors.ipynb, 06-nonlinear-regression.ipynb | |
| 21 | Data Science and Machine Learning: Python Iterators, List Comprehensions, and Generators; Logistic Regression and Categorical Data | 07-python-iters-list-comps-gens.ipynb, 08-logistic-reg-cat-data.ipynb | |
| 22 | Data Science and Machine Learning: Python Modules, Strings, and Data Science Packages; k-Nearest Neighbor Classification and The Importance of Separate Test Data | 09-python-import-strings-ds.ipynb, 10-knn-test-data.ipynb | |
| 23 | Data Science and Machine Learning: Decision Trees, Random Forests, and Gradient Boosted Decision Trees, Numpy | 12-decision-trees.ipynb, 13-numpy.ipynb | |
| 24 | Data Science and Machine Learning: Neural Networks, Clustering | 14-neural-networks.ipynb, 16-clustering.ipynb | |
| 25 | 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 | Probabilistic Reasoning (cont.), AI and Ethics | ||
| 27 | Course Evaluations, AI and Ethics | ||
| 28 | 4th Hour Project Demos | ||
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Final Exam Schedules
on Registrar page under "Resources" (Fall,
Spring)
Final: Tuesday, May 7th, 1:30 PM - 4:30 PM |