CS 4100/5100: Foundations of Artificial Intelligence (Fall 2018)

Schedule

Date	Topic	Notes	Reading	Assignment out/due
9/6	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.
9/10	Agents, Problem Domains and Search	Agents and Their Problems	Ch 2	PA1 out
9/13	Uninformed Search	Search I	Ch 3.1 -- 3.4
9/17	Informed Search	Search II	Ch 3.5 --3.4, 4.1
9/20	Constraint Satisfaction		Ch 6
9/24	CSP & Adversarial Search		Ch 5.1 -- 5.4	PA1 Due on 9/26; PA2 out
9/27	Adversarial Search		Ch 13.1 -- 13.5
10/1	Probability		16.1 -- 16.6
10/4	Utility/Decision theory			PA 2 Due 10/5
10/8	No class! (Holiday)
10/11	Midterm I
10/15	Markov decision processes (MDPs)		17.1 -- 17.3	Project description; PA3 out
10/18	Planning with MDPs		(optional: SB 3.1--3.3, 3.5--3.6, SB 4.1--4.4)
10/22	Reinforcement learning		Ch 21
10/25	Reinforcement learning (cont.)		(optional: SB 6.5)
10/29	Graphical Models and Inference		Ch 13.3 -- 13.5	PA 3 Due 10/30
11/1	Markov Models		Ch 15.1 -- 15.3
11/5	Bayes Nets (cont.)		Ch 14.1 -- 14.5	Project proposal due
11/8	Intro to machine learning		Ch 18.1 -- 18.2	PA4 out
11/12	No class! (Veteran's Day)
11/15	Perceptrons and classification		Ch 18.3 -- 18.6
11/19	More supervised learning		Ch 18.7, 18.10 -- 18.11	PA 4 Due on 11/21
11/22	No class! (Thanksgiving)
11/26	Deep Learning
11/29	Midterm II
12/3	Project Presentations
12/12	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.