CSCI 446/546
Artificial Intelligence
Fall 2019

Montana Tech
Computer Science Department


This page lists the dates of all the lectures with links to slides and readings (if any). Readings are in the optional textbook Artificial Intelligence: A Modern Approach (3rd edition) by Russell and Norvig. It's likely that you can find a pdf version of this or an earlier edition online. You may want to print out the slides before lecture so you can write and highlight on them during lecture.

Once again this year, we will be using the UC Berkeley CS 188 course materials. Their course from last fall is available here. You are welcome to look through any of their materials, though our class will differ in some subtle ways. Particularly when it comes to submitting homework, use the instructions on our website, not UCB's.

You will need to go to Gradescope and create an account. You can enroll in CSCI 446 using the code MZJJYY. Once you've done that, you should be able to link to the homework assignments directly at You should have all received an invitation email to sign up.

#DateTopicSlidesVideosReadingAssignment Posted
1 Mon. 8/26 Course Overview HW 0: Math Self Diagnostic

Prog 0: Tutorial
2 Wed. 8/28 Introduction to AI PDF Closed Captioning
Terminator Vision
Stuffed Animal Vision
Robot Soccer 1
Robot Soccer 2
Google Car
Stapler Fetcher
Pacman Demo
Ch 1, 2
3 Fri. 8/30 Uninformed Search PDF Reflex Agent
Odd Reflex Agent
Mastermind Agent
Replanning Agent
DFS Empty Maze
BFS Empty Maze
DFS Water Maze
BFS Water Maze
UCS Empty Maze
DFS Varying Water Maze
BFS Varying Water Maze
UCS Varying Water Maze
Ch 3.1-3.4 HW 1: Search

Prog 1: Search
- Mon. 9/2 NO CLASS - Labor Day Holiday
4 Wed. 9/4 Informed Search PDF UCS Empty Maze
UCS Pacman Small Maze
Greedy Empty Maze
Greedy Pacman Small Maze
A* Pacman Small Maze
Guess the Algorithm!
Guess the Algorithm!
Guess the Algorithm!
Guess the Algorithm!
Guess the Algorithm!
Ch 3.5-3.6  
5 Fri. 9/6 Constraint Satisfaction Problems (CSPs) PDF DFS Coloring
Backtracking Coloring
Backtracking with Forward Checking Coloring
Arc Consistency, n-Queens
Backtracking with Forward Checking Coloring - Complex Graph
Backtracking with Arc Consistency Coloring - Complex Graph
Ch 6.1 HW 2: Constraint Satisfaction Problems
6 Mon. 9/9 CSPs II PDF Iterative Improvement - N Queens
Iterative Improvement - Coloring
Ch 6.2-6.5
7 Wed. 9/11 Adversarial Search PDF Mystery Pacman
Depth Limited - d=2
Depth Limited - d=10
Thrashing Fixed
Smart Ghosts Coordinating
Smart Ghosts Coordinating (Zoomed In)
Ch 5.2-5.5 HW 3: Games

Prog 2: Multi-Agent Search
8 Fri. 9/13 Expectimax and Utilitites PDF Minimax vs Expectimax (Min)
Minimax vs Expectimax (Exp)
Adversarial Ghost, Expectimax Pacman
Adversarial Ghost, Minimax Pacman
Random Ghost, Expectimax Pacman
Random Ghost, Minimax Pacman
Ch 16.1-16.3
9 Mon. 9/16 Expectimax and Utilitites PDF Ch 5.5-5.8
10 Wed. 9/18 MDPs I PDF Gridworld Ch 17.1-17.3 HW 4: Markov Decision Processes
- Fri. 9/20 NO CLASS - Industry Advisory Board Meeting
11 Mon. 9/23 MDPs I PDF Ch 17.1-17.3
12 Wed. 9/25 MDPs II PDF Ch 17.1-17.3
13 Fri. 9/27 Reinforcement Learning (RL) PDF AIBO Walk - Initial
AIBO Walk - Training
AIBO Walk - Finished
Toddler Walk
Q-Learning, Gridworld
Q-Learning, Crawler
Ch 21 HW 5: Reinforcement Learning

Prog 3: Reinforcement Learning
14 Mon. 9/30 RL II PDF Q-Learning Auto, Cliff Grid
Q-Learning Manual, Bridge Grid
Q-Learning Epsilon-Greedy, Crawler
Q-Learning Exploration Function, Crawler
Q-Learning, Tiny Pacman
Q-Learning, Trained Pacman
Q-Learning, Tricky Pacman
Approximate Q-Learning, Pacman
Ch 21
- Wed. 10/2 Review for Exam I PDF Paper


- Fri. 10/4 Exam I Equations
- Mon. 10/7 Go over exam
15 Wed. 10/9


PDF Ghostbusters, no probability
Ghostbusters, with probability
Ch. 13.1-13.5  
16 Fri. 10/11 Probability
17 Mon. 10/14 Bayes Nets: Representation PDF Ch. 14.1-14.2, 14.4 HW 6: Bayes Nets: Representation and Independence
18 Wed. 10/16 Bayes Nets: Representation  
19 Fri. 10/18 Bayes Nets: Independence PDF Ch. 14.3  
20 Mon. 10/21 Bayes Nets: Inference PDF Ch. 14.4 HW 7: Bayes Nets: Inference and Sampling
21 Wed. 10/23 Bayes Nets: Sampling PDF Ch. 14.4-14.5
22 Fri. 10/25 Decision Networks and VPI PDF Ghostbusters, Probability
Ghostbusters, VPI
Ch. 16.5-16.6 HW 8: VPI and Hidden Markov Models
23 Mon. 10/28 Hidden Markov Models (HMMs) PDF Pacman Sonar - No Beliefs
Ghostbusters, Basic Dynamics
Ghostbusters, Circular Dynamics
Ghostbusters, Whirlpool (Center) Dynamics
Ghostbusters, Circular Dynamics
Pacman Sonar - With Beliefs
Ch. 15.2, 15.5 Prog 4: Ghostbusters
24 Wed. 10/30 Particle Filtering and HMMs PDF Ghostbusters, Exact Filtering
Ghostbusters, Particle Filtering, Moderate Number Particles
Ghostbusters, Particle Filtering, Huge Number Particles
Ghostbusters, Particle Filtering, One Particle
Robot Localization, Sonar
SLAM Mapping
Ch. 15.2, 15.6HW 9: Particle Filtering and Naive Bayes
25 Fri. 11/1 Naive Bayes PDF
Exam Outline
Ch. 20.1-20.2.2
- Mon. 11/4 Midterm II
26 Wed. 11/6 Perceptrons and Logistic Regression PDF Multiclass Perceptron
Pacman Apprentice
Ch. 18.6.3 HW 10: Perceptrons

Prog 5: Machine Learning
27 Fri. 11/8 Optimization and Neural Networks PDF Tensorflow Playground Ch. 18.8 HW 11: Gradient Descent and Neural Networks
- Mon. 11/11 NO CLASS - Veteran's Day Holiday
28 Wed. 11/13 Neural Networks and Decision Trees PDF
29 Fri. 11/15 Dalton Caron - Neural Networks PDF
30 Mon. 11/18 Mariia Korol - Natural Language Processing PDF
31 Wed. 11/20 Eli Hodges - Genetic Algorithms PDF
32 Fri. 11/22 Jacob Vesco - Computer Vision PDF
33 Mon. 11/25 Blaine Berrington - AI in Chemistry PDF
- Wed. 11/27 NO CLASS - Thanksgiving Holidy
- Fri. 11/29 NO CLASS - Thanksgiving Holiday
34 Mon. 12/2 Philosophical Issues and Future Directions
Review for Final Exam
35 Wed. 12/4 Burak Adam - Moravec's Paradox PDF
- Fri. 12/6 Greg Marlowe - Robotics PDF
- Fri. 12/13 11:30-1:30 Final Exam PDF

Page last updated: December 06, 2019