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.