CSCI 446/546
Artificial Intelligence
Fall 2021

Montana Tech
Computer Science Department



SCHEDULE

This page lists the dates of all the lectures with links to slides and readings (if any). The links will go "live" once the material is posted. 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. Lectures will be live in class and posted for viewing or download from this page.

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 that was sent to you by email. You should have all received an invitation email to sign up. Once you've done that, you should be able to link to the homework assignments directly at this website.

#DateTopicSlidesLecturesVideo ExamplesReadingsAssignment Posted
1 Mon. 8/23 Course Overview Video HW 0: Math Self Diagnostic

Prog 0: Tutorial
2 Wed. 8/25 Introduction to Artificial Intelligence PDF Video
Closed Captioning
Terminator Vision
Stuffed Animal Vision
Robot Soccer 1
Robot Soccer 2
Google Car
Laundry
Petman
Stapler Fetcher
Pacman Demo
Ch 1, 2
3 Fri. 8/27 Uninformed Search PDF Video
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
4 Mon. 8/30 Informed Search PDF Video
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!
Ch 3.5-3.6
5 Wed. 9/1 Constraint Satisfaction Problems (CSPs) I PDF Video
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 Fri. 9/3 CSPs II PDF Video Iterative Improvement - N Queens
Iterative Improvement - Coloring
Ch 6.2-6.5
- Mon. 9/6 NO CLASS - Labor Day Holiday
7 Wed. 9/8 Adversarial Search PDF Video Mystery Pacman
Minimax - Optimal Opponent
Expectimax - Non-Optimal Opponent
Thrashing
Thrashing Fixed
Smart Ghosts Coordinating
Smart Ghosts Coordinating (Zoomed In)
Depth Limited - d=2
Depth Limited - d=10
Ch 5.2-5.5 HW 3: Games

Prog 2: Multi-Agent Search
8 Fri. 9/10 Expectimax and Utilities I PDF Video 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. 9/13 Expectimax and Utilities II PDF Video Ch 5.5-5.8
- Wed. 9/15 NO CLASS - Industry Advisory Board Meeting
10 Fri. 9/17 Markov Decision Processes (MDPs) I PDF Video Gridworld Ch 17.1-17.3 HW 4: Markov Decision Processes
11 Mon. 9/20 MDPs II PDF Video
12 Wed. 9/22 MDPs III PDF Video
13 Fri. 9/24 Reinforcement Learning (RL) I PDF Video AIBO Walk - Initial
AIBO Walk - Training
AIBO Walk - Finished
Toddler Walk
Crawler
Q-Learning, Gridworld
Q-Learning, Crawler
Ch 21 HW 5: Reinforcement Learning

Prog 3: Reinforcement Learning
14 Mon. 9/27 RL II PDF Video 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. 9/29 Review for Exam I PDF Video Paper
- Fri. 10/1 Midterm Exam I
- Mon. 10/4 Go Over Exam I Video
15 Wed. 10/6 Probability I PDF Video Ch. 13.1-13.5
16 Fri. 10/8 Probability II and Bayes Nets PDF Video
17 Mon. 10/11 Bayes Nets: Representation, Independence PDF Video Ch. 14.1-14.2, 14.4 HW 6: Bayes Nets: Representation and Independence
18 Wed. 10/13 Bayes Nets: Independence, Inference PDF Video Ch. 14.3
19 Fri. 10/15 Bayes Nets: Sampling PDF Video Ch. 14.4-14.5 HW 7: Bayes Nets: Inference and Sampling
20 Mon. 10/18 Decision Networks and the Value of Perfect Information PDF Video Ch. 16.5-16.6
21 Wed. 10/20 Hidden Markov Models (HMMs) PDF Video Ch. 15.2, 15.5 HW 8: VPI and Hidden Markov Models
22 Fri. 10/22 Particle Filtering and HMMs PDF Video Ch. 15.2, 15.6 Prog 4: Ghostbusters
23 Mon. 10/25 Applications of HMMs and Naive Bayes PDF Video Ch. 15.2, 15.6 HW 9: Particle Filtering and Naive Bayes
24 Wed. 10/27 Naive Bayes PDF Video Ch. 20.1-20.2.2
- Fri. 10/29 Review for Exam II PDF Video
- Mon. 11/1 Midterm Exam II
- Wed. 11/3 Go Over Exam II
25 Fri. 11/5 Perceptrons and Logistic Regression PDF Video Ch. 18.6.3 HW 10: Perceptrons

Prog 5: Machine Learning
26 Mon. 11/8 Optimization and Neural Networks PDF Video Ch. 18.8 HW 11: Gradient Descent and Neural Networks
27 Wed. 11/10 Neural Networks and Decision Trees PDF Video
28 Fri. 11/12 Kernels and Clustering PDF Video
29 Mon. 11/15 Propositional Logic I PDF Video Ch. 7 HW 12: Propositional and Predicate Logic
30 Wed. 11/17 Propositional Logic II PDF Video
31 Fri. 11/19 Predicate Logic I PDF Video Ch. 8
32 Mon. 11/22 Predicate Logic II PDF Video Ch. 9
- Wed. 11/24 NO CLASS - Thanksgiving Break
- Fri. 11/26 NO CLASS - Thanksgiving Break
33 Mon. 11/29 Philosophical Issues PDF Video Ch. 21
34 Wed. 12/1 Future Directions PDF Video Ch. 22
- Fri. 12/3 Review for Final Exam PDF Video
- Fri. 12/10, 11:30-1:30 FINAL EXAM


Page last updated: September 27, 2021