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.