CS 4100: Foundations of Artificial Intelligence (Fall 2017)

Schedule

Date	Topic	Notes	Reading	Assignment out/due
9/8	Introduction	Course Introduction A brief history of AI AI in today's world Course Details In-Class Questionnaire		Python/autograder Tutorial introduces you to Python and the autograder. Also look at official Python Tutorial.
9/12	Agents, Problem Domains and Search	Agents and Their Problems	Ch 2
9/15	Uninformed Search	Search I	Ch 3.1 -- 3.4	PA1 out
9/19	Informed Search	Search II	Ch 3.5 --3.4, 4.1
9/22	Constraint Satisfaction		Ch 6
9/26	Constraint Satisfaction (cont.)		Ch 6	PA1 Due on 9/27
9/29	Adversarial Search		Ch 5.1 -- 5.4	PA2 out
10/3	Probability Refresher		Ch 13.1 -- 13.5
10/6	Utility/Decision theory		16.1 -- 16.6	PA 2 due on 10/8
10/10	Midterm I
10/13	Markov decision processes (MDPs)		17.1 -- 17.3
10/17	Planning with MDPs		(optional: SB 3.1--3.3, 3.5--3.6, SB 4.1--4.4)	Project description
10/20	Reinforcement learning		Ch 21	PA3 out
10/24	Reinforcement learning (cont.)		(optional: SB 6.5)
10/27	Markov Models		Ch 15.1 -- 15.3
10/31	Graphical Models and Inference		Ch 13.3 -- 13.5
11/3	Bayes Nets		Ch 14.1 -- 14.5	PA 3 Due
11/7	Bayes Nets (cont.)			Project proposal due; PA4 out
11/10	Bayes Nets (cont.)
11/14	Intro to machine learning
11/17	Supervised learning
11/21	MidTerm II
11/24	Thanksgiving
11/28	Perceptrons and classification
12/1	Project Presentations
12/5	TBD			PA 4 Due
12/13	Project Reports Due			Report due at 11:59 PM -- This is a hard deadline, no extensions

Important note: unless noted otherwise, all readings and assignments are due on the day that they appear in the schedule.

Unless noted otherwise, all readings are from Artificial Intelligence: A Modern Approach, 3rd Ed., Russell and Norvig.