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
  • Questionnaire
 Chapter 1  Python/autograder Tutorial (PA0) introduces you to Python and the autograder. Also look at official Python Tutorial.
9/12 Agents and Problem Domains 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 --3.6, 4.1
9/22 Informed Search (cont.) Search III  
9/26 Adversarial Search Competition in games Ch 5.1 -- 5.3, 5.5  PA1 Due 9/27; PA2 out
9/29 Adversarial Search (cont.)

Ch 12.1 -- 12.5
10/3 Uncertainty and Probability
 
10/6 Graphical Models/Bayes Nets Probabilistic modeling Ch 13.1 -- 13.2 Project description out
10/10 Exact Inference
Ch 13.3
PA 2 Due 10/11
10/13 Approximate Inference
Ch 13.4
  
10/17 Exam 1

10/20 Markov models Sequential modeling Ch 14.1 -- 14.3 PA3 out
10/24 Markov decision processes (MDPs) Incorporating actions Ch 17.1 -- 17.2 Project proposal due
10/27 Planning with MDPs
Ch 5.4 (MCTS), (optional: SB 3.1--3.3, 3.5--3.6, SB 4.1--4.4)  
10/31 Reinforcement learning Learning for MDPs Ch 22
11/3 Reinforcement learning (cont.)
(optional: SB 6.5)
11/7 Intro to machine learning Supervised learning Ch 19.1 -- 19.2 PA3 due 11/8; PA4 out
11/10 More supervised learning Ch 19.4 -- 19.7
11/14 Deep Learning  
 Ch 21  
11/17 Deep Reinforcement Learning


11/21 Exam 2
 
11/24 No class (Thanksgiving break)


11/28 Advanced topics

PA 4 Due 11/29
12/1 Project Presentations 


12/5
 Project Presentations


12/8
 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, 4th Ed., Russell and Norvig.