CS 4100: Foundations of Artificial Intelligence (Fall 2020)

Schedule

Date	Topic	Notes	Reading	Assignment out/due
9/11	Introduction	Course Introduction A brief history of AI AI in today's world Course Details In-Class Questionnaire		Python/autograder Tutorial (PA0) introduces you to Python and the autograder. Also look at official Python Tutorial.
9/15	Agents, Problem Domains and Search	Agents and Their Problems	Ch 2
9/18	Uninformed Search	Search I	Ch 3.1 -- 3.4	PA1 out
9/22	Informed Search	Search II	Ch 3.5 --3.6, 4.1
9/25	Constraint Satisfaction Problems (CSP)		Ch 6
9/29	CSP & Adversarial Search		Ch 5.1 -- 5.4	PA1 Due; PA2 out
10/2	Adversarial Search
10/6	Uncertainty and Probability		Ch 13.1 -- 13.5
10/9	Graphical Models		Ch 14.1 -- 14.5	PA 2 Due
10/13	Bayes Nets
10/16	Inference		Ch 15.1 -- 15.3
10/20	Utility/Decision theory		Ch 16.1 -- 16.3	Project description
10/23	Markov decision processes (MDPs)		Ch 17.1 -- 17.3	PA3 out
10/27	Planning with MDPs		(optional: SB 3.1--3.3, 3.5--3.6, SB 4.1--4.4)
10/30	Reinforcement learning		Ch 21	Project proposal due
11/3	Reinforcement learning (cont.)		(optional: SB 6.5)
11/6	Intro to machine learning		Ch 18.1 -- 18.2	PA3 due; PA4 out
11/10	More supervised learning		Ch 18.4 -- 18.7
11/13	ML (continued)		Ch 18.3
11/17	Deep Learning		Ch 18.10 -- 18.11	PA 4 Due
11/20	Deep reinforcement learning
11/24	Ethics in AI
11/27	No class (Thanksgiving break)!
12/1	Ethics in AI
12/4	Project Presentations
12/8	Project Presentations
12/15	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.