Schedule

CSCI 446
Artificial Intelligence
Fall 2018

Schedule | Assignments | Course Syllabus | Paper and Presentation Information | Moodle

This page lists the dates of all the lectures with links to slides and readings (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.

#	Date	Topic	Slides	Videos	Reading	Assignment Posted
0	Mon. 8/27	Introduction to AI	PDF	Closed Captioning Terminator Vision Stuffed Animal Vision Robot Soccer 1 Robot Soccer 2 Google Car Laundry Petman Stapler Fetcher Pacman Demo	Ch 1	Math Self Diagnostic: Practice Project 0: Unix/Python Tutorial
1	Wed. 8/29	Uninformed Search	PDF	Reflex Agent Odd Reflex Agent Replanning Agent Mastermind 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	Homework 1: Search
2	Fri. 8/31	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 3.5-3.7	Project 1: Search
-	Mon. 9/3	NO CLASS - Labor Day Holiday
3	Wed. 9/5	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
4	Fri. 9/7	CSP II	PDF	Iterative Improvement - N Queens Iterative Improvement - Coloring	Ch 6.2-6.5	Homework 2: Constraint Satisfaction Problems
5	Mon. 9/10	Propositional Logic	PDF		Ch 7.1-7.4
-	Wed. 9/12	Industry Advisory Board Meeting - NO CLASS
6	Fri. 9/14	Inference in Propositional Logic	PDF		Ch 7.5-7.7	Homework 3: Logical Inference
7	Mon. 9/17	First Order Logic		PDF		Ch 8
8	Wed. 9/19	Inference in First Order Logic		PDF Examples		Ch 9	Assignment 4: Logical Inference \| Solution
9	Fri. 9/21	Adversarial Search	PDF	Mystery Pacman Minimax vs Expectimax (Min) Minimax vs Expectimax (Exp) Depth Limited - d=2 Depth Limited - d=10 Thrashing Thrashing Fixed Smart Ghosts Coordinating Smart Ghosts Coordinating (Zoomed In)	Ch 5.1-5.4	Assignment 2: Search Assignment 3: Constraint Satisfaction Problems	Homework 4: Games
10	Mon. 9/24	Expectimax Search and Utilities	PDF	Adversarial Ghost, Expectimax Pacman Adversarial Ghost, Minimax Pacman Random Ghost, Expectimax Pacman Random Ghost, Minimax Pacman	Ch 5.5-5.8	Project 2: Multi-Agent Pacman
11	Wed. 9/26	Markov Decision Processes (MDPs)	PDF	Gridworld	Ch 17.1-17.3	Homework 5: MDPs
12	Fri. 9/28	MDP II	PDF		Ch 15.2
13	Mon. 10/1	Reinforcement Learning	PDF	AIBO Walk - Initial AIBO Walk - Training AIBO Walk - Finished Toddler Walk Crawler Q-Learning, Gridworld Q-Learning, Crawler	Ch 21	Homework 6: Reinforcement Learning
14	Wed. 10/3	Reinforcement Learning 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		Project 3: Reinforcement Learning
15	Fri. 10/5	Probability	PDF	Ghostbusters, no probability Ghostbusters, with probability	Ch 13.1-13.5	Assignment 4: Logic
-	Mon. 10/8	Project Discussion Review for Exam I	DOCX			Project 1: Paper Project 2: Presentation
-	Wed. 10/10	Exam I
16	Fri. 10/12	Markov Models	PDF	Ghostbusters, Basic Dynamics Ghostbusters, Circular Dynamics Ghostbusters, Whirlpool (Center) Dynamics		Homework 7: Probability
17	Mon. 10/15	Go over exam				Project 4: Ghostbusters
18	Wed. 10/17	Hidden Markov Models (HMMs)	PDF	Ghostbusters, Whirlpool (Center) Dynamics Ghostbusters, Circular Dynamics Ghostbusters, Whirlpool (Center) Dynamics	Ch 15.2
19	Fri. 10/19	Particle Filters and Applications of 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 15.3-15.5	Homework 8: Bayes Nets
20	Mon. 10/22	Bayesian Networks I: Representation	PDF		Ch 14.1
21	Wed. 10/24	Bayesian Networks II: Independence	PDF		Ch 14.2-14.3
22	Fri. 10/26	Bayesian Networks III: Inference	PDF		Ch 14.4
23	Mon. 10/29	Bayesian Networks IV: Sampling	PDF		Ch 14.5
24	Wed. 10/31	Decision Diagrams and the Value of Perfect Information	PDF VPI-PDF	Ghostbusters, Probability Ghostbusters, VPI	Ch 16.5-16.6	Homework 9: Decision Diagrams
25	Fri. 11/2	Naive Bayes	PDF		Ch 20.1-20.2
26	Mon. 11/5	Perceptrons	PDF		Ch 18.6	Homework 10: Machine Learning
27	Wed. 11/7	Kernels and Clustering	PDF	Multiclass Perceptron Pacman Apprentice	Ch 18.8	Project 5: Classification
-	Fri. 11/9	Review for Exam II	DOCX
-	Mon. 11/12	NO CLASS - Veteran's Day Holiday
-	Wed. 11/14	Exam II	PDF
28	Fri. 11/16	Keith Bocian, Matrix Imputation Philosophical Issues in AI	KB-PPT
29	Mon. 11/19	Terra Miller, Deep Convolutional Networks Ngoc Ha, Generative Adversarial Networks	NH-PPT TM-PPT
-	Wed. 11/21	NO CLASS - Thanksgiving Break
-	Fr. 11/23	NO CLASS - Thanksgiving Break
30	Mon. 11/26	Colter McClure, Fuzzy Logic Sam Lloyd, Robotics	SL-PPT CM-PPT
31	Wed. 11/28	Chris O'Neill, Natural Language Processing Jessica Jones, Case Based Reasoning	CO-PPT JJ-PPT
32	Fri. 11/30	Tyler Fricks, Long Short Term Memory ANNs Cole Daily, Decision Trees	TF-PPT CD-PPT
33	Mon. 12/3	Josh Baldwin, Rule Based Systems James Keenan, Natural Language Processing	JB-PPT JK-PPT
34	Wed. 12/5	Nathan Cassel, Autonomous Cars Future Directions in AI
35	Fri. 12/7	Zachariah Valenzuela, Artificial Emotions Review for Final Exam	PDF
-	Wed. 12/12	Final Exam - 8:00-10:00AM

Date

Topic

Slides

Videos

Reading

Assignment Posted

Mon. 8/27

Introduction to AI