CS 5100: Foundations of Artificial Intelligence (Fall 2021)

Schedule

Date	Topic	Notes	Reading	Assignment out/due
9/9	Introduction	Course Introduction A brief history of AI AI in today's world Course Details In-Class Questionnaire	Chapter 1	Python/autograder Tutorial (PA0) introduces you to Python and the autograder. Also look at official Python Tutorial.
9/13	Agents and Problem Domains	Agents and Their Problems	Ch 2
9/16	Uninformed Search	Search I	Ch 3.1 -- 3.4	PA1 out
9/20	Informed Search	Search II	Ch 3.5 --3.6, 4.1
9/23	Informed Search (cont.)
9/27	Adversarial Search		Ch 5.1 -- 5.3, 5.5	PA1 Due; PA2 out
9/30	Adversarial Search (cont.)		Ch 12.1 -- 12.5
10/4	Uncertainty and Probability
10/7	Graphical Models/Bayes Nets		Ch 13.1 -- 13.2	Project description out
10/11	No class (Columbus/Indigenous People' Day)
10/14	Exact Inference		Ch 13.3	PA 2 Due 10/13
10/18	Exam 1
10/21	Approximate Inference		Ch 13.4
10/25	Markov models		Ch 14.1 -- 14.3	PA3 out
10/28	Markov decision processes (MDPs)		Ch 17.1 -- 17.2	Project proposal due
11/1	Planning with MDPs		Ch 5.4 (MCTS), (optional: SB 3.1--3.3, 3.5--3.6, SB 4.1--4.4)
11/4	Reinforcement learning		Ch 22
11/8	Reinforcement learning (cont.)		(optional: SB 6.5)
11/11	No class (Veterans' Day)
11/15	Intro to machine learning		Ch 19.1 -- 19.2
11/18	More supervised learning		Ch 19.4 -- 19.7	PA3 due 11/20; PA4 out
11/23	Deep Learning		Ch 21
11/25	No class (Thanksgiving break)
11/29	Exam 2
12/2	Deep Reinforcement Learning			PA 4 Due
12/6	Project Presentations
12/9	Project Presentations
12/13	Project Presentations
12/14	Project Reports Due			Report due at 11:59 PM -- This is a hard deadline, no extensions
12/16	Open discussion (virtual)

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, 4th Ed., Russell and Norvig.