Schedule

Date Topic Notes Reading Assignment out/due
1/7 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.
1/10 Agents, Problem Domains and Search Agents and Their Problems Ch 2 PA1 out
1/14 Uninformed Search Search I
 Ch 3.1 -- 3.4
1/17 Informed Search Search II
 Ch 3.5 --3.6, 4.1
1/21 No class! (MLK day)


1/24 Constraint Satisfaction
Ch 6 PA1 Due on 1/28 (9am) 
1/28 CSP & Adversarial Search
Ch 5.1 -- 5.4 PA2 out
1/31 Adversarial Search


2/4 Uncertainty and Probability
Ch 13.1 -- 13.5
2/7 Graphical Models and Inference
Ch 14.1 -- 14.5 PA 2 Due 2/11 (9am)
2/11 Bayes Nets
 
2/14 Markov Models
Ch 15.1 -- 15.3 Project description
2/18 No class! (President's Day)


2/21 Utility/Decision theory
Ch 16.1 -- 16.3
2/25 Midterm I


2/28 Markov decision processes (MDPs)
17.1 -- 17.3 PA3 out
3/4 No class! (Spring Break)


3/7 No class! (Spring Break)
 
3/11 Planning with MDPs (optional: SB 3.1--3.3, 3.5--3.6, SB 4.1--4.4)
3/14 Reinforcement learning  
 Ch 21 Project proposal due
3/18 Reinforcement learning (cont.)
(optional: SB 6.5)
3/21 Intro to machine learning
Ch 18.1 -- 18.2
3/25 Perceptrons and classification
Ch 18.4 -- 18.7 PA3 due 3/26;PA4 out
3/28 More supervised learning
Ch 18.3
4/1 Deep Learning
Ch 18.10 -- 18.11
4/4
 Midterm II


4/8
 Project Presentations


4/11
 Project Presentations

PA 4 Due on 4/12
4/15
 No class! (Patriots' Day)


4/23 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.