CS 371 - Introduction to Artificial Intelligence
Course Syllabus |
Class | Month | Day | Topic | Readings (parenthesized reading are optional) |
1 | August | 27 | Introduction, "What is AI?", agents, agent architectures, and environments | Skim Ch. 1, read 2.1-2.3 Deep Blue article |
2 | 29 | Introduction: PEAS agent description, environment properties Lego Mindstorm NXT sensors and actuators |
rest of Ch. 2, 3.1-3.3, HW1 Starter Code and Documentation | |
3 | 31 | Uninformed Search: breadth-first search, depth-first search | 3.4 | |
4 | September | 3 | Uninformed Search: depth-limited search, iterative-deepening, tradeoffs | |
5 | 5 | Robotics Project Intro | ||
6 | 7 | Uninformed Search: repeated state detection, in-class implementation | ||
7 | 10 | Informed Search: heuristic search, best-first search, uniform cost search, greedy search | rest of Ch. 3 | |
8 | 12 | Informed Search: admissibility, iterative deepening A* Stochastic Local Search: iterative improvement |
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9 | 14 | Stochastic Local Search: simulated annealing, annealing schedules | 4.1, Skim Science Simulated Annealing article | |
10 | 17 | Stochastic Local Search: challenge problem | ||
11 | 19 | Stochastic Local Search: challenge problem (continued) | 10.1-10.5.1 | |
12 | 21 | |||
13 | 24 | Machine Learning: Dynamic Programming | "Solving the Dice Game Pig: an introduction to dynamic programming and value iteration", Sections 1-2 | |
14 | 26 | 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 | |
15 | 28 | Machine Learning: Value Iteration (cont.) | ||
16 | October | 1 | Machine Learning: In-class problem exercise (Jack's Car Rental) | |
17 | 3 | Machine Learning: In-class problem exercise (Jack's Car Rental) | ||
18 | 5 | Machine Learning: In-class problem exercise (Robot Navigation) | 7.1-7.4 (through "A simple knowledge base"), "Clue Deduction: an introduction to satisfiability reasoning" sections 1-2 | |
19 | 10 | Knowledge Representation & Reasoning: Propositional Logic, syntax, semantics, truth assignments, models, (un)satisfiability, validity, entailment, equivalence. | 7.5, "Clue Deduction: an introduction to satisfiability reasoning" sections 3-6 | |
20 | 12 | Knowledge Representation & Reasoning: conjunctive normal form, resolution theorem proving, various logic problems | "Clue Deduction: an introduction to satisfiability reasoning" section 8 | |
21 | 15 | Knowledge Representation & Reasoning: Clue project knowledge base | ||
22 | 17 | Knowledge Representation & Reasoning: stochastic local search for boolean satisfiability, WalkSAT and variants | 7.6.2 (local search algorithms) | |
23 | 19 | Knowledge Representation & Reasoning: in-class SAT solver programming | ||
24 | 22 | Robotics Project Progress Presentations | ||
25 | 24 | Game-Tree Search: Mancala problem intro, minimax | 5.1-5.2 | |
26 | 26 | Game-Tree Search: heuristic evaluation, alpha-beta pruning | 5.3-5.4 | |
27 | 29 | Game-Tree Search: time management | H. Baier, M. Winands. Time Management for Monte-Carlo Tree Search in Go. (skim time management strategies) | |
28 | 31 | Game-Tree Search: expectiminimax, in-class development | 5.5 | |
29 | November | 2 | Robotics Project Progress Presentations | |
30 | 5 | Game-Tree Search: Monte Carlo Tree Search (MCTS), UCT | L. Kocsis, C. Szepesvari. Bandit based Monte-Carlo Planning. | |
31 | 7 | Data Mining: k-Nearest-Neighbor Classification | Hastie, T., Tibshirani, R., and Friedman, J. The Elements of Statistical Learning: data mining, inference, and prediction., Chapter 1 | |
32 | 9 | Data Mining: Linear Models and Least Squares Classification | ||
33 | 12 | Data Mining: Multilayer Feed-Forward Neural Networks, Backpropagation (Malec) | R&N 18.7 | |
34 | 14 | Data Mining: Locally Weighted Regression/Learning, Curse of Dimensionality (Pennella) | Atkeson, Moore, and Schaal. Locally Weighted Learning. See also companion website. | |
35 | 16 | Data Mining: Decision Tree Learning (Becker) | R&N 18.3 | |
36 | 19 | Data Mining: Naïve Bayesian Classification and k-Fold Cross-Validation (Ianiro) | R&N p. 499, section 20.2.2; Wikipedia articles: Naïve Bayesian Classification, k-Fold Cross-Validation; The Elements of Statistical Learning 7.10 | |
37 | 26 | Data Mining: Clustering algorithms (e.g. k-means, vector quantization) (Lalvani) | The Elements of Statistical Learning 14.3 | |
38 | 28 | Data Mining: Boosted Ensembles of Decision Stumps, Adaboost (Taylor) | R&N 18.10, The Elements of Statistical Learning 10.1 | |
39 | 30 | Data Mining: Support Vector Machines (Mendoza) | R&N 18.9, The Elements of Statistical Learning 12.2-12.3, LIBSVM website and guide | |
40 | December | 3 | Data Mining: Random Forests (Crear) | The Elements of Statistical Learning 15, Wikipedia article |
41 | 5 | Review; Course Evaluations | ||
42 | 7 | Final 4th Hour Requirement Robotics Presentations |