CSCI 446
Artificial Intelligence
Fall 2015

Montana Tech
Computer Science & Software Engineering

Schedule  |  Assignments  |  Course syllabus  |  Moodle

SCHEDULE

This page lists the dates of all the lectures with links to slides and examples from the lecture (if any). Readings are in the book Artificial Intelligence: A Modern Approach (3rd edition) by Russell and Norvig. To get the most out of lectures, skim the reading beforehand (or at least look at the pictures!). You may also want to print out the slides before lecture so you can write and highlight on them during lecture. After the lecture, go back and read the pages carefully and do the book exercises.

#DateTopicSlidesReadingLinks
0 Mon 8/24 Introduction to AI PDF Ch 1 Berkeley, Intro
1 Wed 8/26 Agents and State Spaces
Asmt. 1, AIMA Code Base		 PDF Ch 2 Reflex agent acting optimally  Reflex agent acting badly Agent that replans
2 Fri 8/28 Uninformed Search PDF Ch 3.1-3.4 Berkeley, Uninformed Search  DFS and BFS step-by-step  Empty, DFS  Empty, BFS  Water, DFS  Water, BFS  Shallow/deep, DFS  Shallow/deep, BFS  Shallow/deep, UCS 
3 Mon 8/31 Heuristic Search
Asmt. 2, Search		 PDF Ch 3.5-3.7 Berkeley, Informed Search  A* step-by-step  Empty, UCS  Empty, Greedy  Empty, A*  Pacman, UCS  Pacman, UCS  Pacman, UCS  Shallow/deep, Greedy  Shallow/deep, A* 
4 Wed 9/2 Non-Classical Search PDF Ch 4 Berkeley, Adversarial + local search  Alpha-Beta step-by-step  Mystery Pacman  Pacman, grim  Pacman, lucky  Depth limited 2  Depth limited 10  Smart ghosts  Smart ghosts 2 
5 Fri 9/4 Adversarial Search PDF Ch 5.1-5.4 Berkeley, Expectimax and Utilities  Minimax Pacman  Expectimax Pacman  Random ghost vs. minimax Pacman  Random ghost vs. expectimax Pacman  Adversarial ghost vs. minimax Pacman  Adversarial ghost vs. expectimax Pacman 
- Mon 9/7 No Class: Labor Day Holiday
- Wed 9/9 No Class: Industry Advisory Board Meeting
6 Fri 9/11 Stochastic Search PDF Ch 5.5-5.8
7 Mon 9/14 Constraint Satisfaction I
Asmt. 3, Constraint Satisfaction		 PDF
PDF		 Ch 6.1-6.3 Berkeley, CSP I  Coloring, DFS  Coloring, backtracking  Berkeley, CSP II  Simple graph, forward checking  Complex graph, forward checking  Complex graph, arc consistency 
8 Wed 9/16 Constraint Satisfaction II,
Local Search		 PDF  PDF  Ch 6.4-6.5 Iterative improvement, n-queens  Iterative improvement, coloring 
9 Fri 9/18 Propositional Logic PDF Ch 7.1-7.4
10 Mon 9/21 Inference in Propositional Logic PDF Ch 7.5-7.7
11 Wed 9/23 First Order Logic PDF Ch 8
12 Fri 9/25 Inference in First Order Logic
Asmt. 4, Logical Inference		 PDF Ch 9
13 Mon 9/28 Fuzzy Logic PDF
14 Wed 9/30 Classical Planning PDF Ch 10
15 Fri 10/2 Uncertainty, Probability PDF Ch 13.1-13.4 Berkeley, Probability (low audio)  Berkeley, Probability  Ghostbusters, no probability  Ghostbusters, with probability  Berkeley, Probability (first 30m) 
- Mon 10/5 Review for Exam I Exam I Outline
- Wed 10/7 Exam I
16 Fri 10/9 Go over exam
Bayes Theorem
Asmt. 5, Uncertain Reasoning
Asmt. 7, AI Topic Paper
Asmt. 8, AI Topic Presentation
Project		 Ch 13.5-13.6
17 Mon 10/12 Bayesian Networks,
Asmt. 5, Uncertain Reasoning		 Ch 14.1-14.3 Berkeley, Bayes' Nets: Representation  Berkeley, Bayes' Nets: Independence  Berkeley, D-separation  Berkeley, Bayes' Nets: Inference  Step-By-Step: Elimination of One Variable  Step-By-Step: Variable Elimination  Berkeley, Bayes' Nets: Sampling  Step-By-Step: Sampling  Step-By-Step: Gibbs' Sampling 
- Wed 10/14 No Class: Grace Hopper Conference
- Fri 10/16 No Class: Grace Hopper Conference
20 Mon 10/19 Inference Under Uncertainty PDF
PDF		 Ch 14.4-14.7
21 Wed 10/21 Decision Making PDF
PDF
PDF		 Ch 16
22 Fri 10/23 Inductive Learning - Decision Trees PDF Ch 18.1-18.6
23 Mon 10/26 Inductive Learning - Rules PDF Ch 18.7-18.11
24 Wed 10/28 Instance Based Learning
Asmt. 6, Machine Learning 		PDF Ch 19
25 Fri 10/30 Artificial Neural Networks PDF Ch 20
26 Mon 11/2 Genetic Algorithms PDF
27 Wed 11/4 Philosophical Issues, Future Directions PDF Ch 26, 27
28 Fri 11/6 Robots Kits!
- Mon 11/9 Review for Exam II Exam II Outline
- Wed 11/11 No Class: Veterans Day Holiday
- Fri 11/13 Exam II
- Mon 11/16 Go over exam
- Wed 11/18 Project Discussion
- Fri 11/20 Neuroevolution - Josh Lee PDF
- Mon 11/23 Swarm Intelligence - Ross Moon PDF
- Wed 11/25 No Class: Thanksgiving
- Fri 11/27 No Class: Thanksgiving
- Mon 11/30 Artificial Neural Networks - Luke Schuler PDF
- Wed 12/2 Support Vector Machines - Ross Mitchell PDF
- Fri 12/4 Genetic Algorithms - Joy Reistad PDF
- Mon 12/7 Review for Final Exam Exam III Outline
- Wed 12/9 Final Exam, 8am-11am, CBB 105

Page last updated: December 07, 2015