Schedule

Date Topic Notes Reading Assignment out/due
1/20 Introduction Course Introduction
  • A brief history of AI
  • AI in today's world
  • Course Details
  • In-Class Questionnaire
Python/autograder Tutorial (PA0) introduces you to Python and the autograder. Also look at official Python Tutorial.
1/25 Agents, Problem Domains and Search Agents and Their Problems Ch 2
1/27 Uninformed Search Search I
 Ch 3.1 -- 3.4 PA1 out
2/1 Informed Search Search II
 Ch 3.5 --3.6, 4.1
2/3 Constraint Satisfaction Problems (CSP)
Ch 6
2/8 CSP & Adversarial Search
Ch 5.1 -- 5.4
2/10 Adversarial Search

PA1 Due 2/11; PA2 out
2/15 No class (President's day)


2/17 Uncertainty and Probability
Ch 13.1 -- 13.5
2/22 Graphical Models
Ch 14.1 -- 14.3
2/24 Bayes Nets

Project description out
3/1 Inference
Ch 14.4 -- 14.5 PA 2 Due
3/3 Markov models
Ch 15.1 -- 15.3
3/8 Markov decision processes (MDPs)
Ch 17.1 -- 17.3 PA3 out
3/10 Planning with MDPs
(optional: SB 3.1--3.3, 3.5--3.6, SB 4.1--4.4) Project proposal due
3/15 Online planning in MDPs
TBD
3/17 Reinforcement learning
Ch 21
3/22 Reinforcement learning (cont.)
(optional: SB 6.5)
3/24 No Class (Care Day)

3/29 Intro to machine learning  
 Ch 18.1 -- 18.2
3/31 More supervised learning
Ch 18.4 -- 18.7 PA4 out
4/5 ML (continued)
Ch 18.3
4/7 Deep Learning
Ch 18.10 -- 18.11 PA3 due
4/12 No Class (Care Day)


4/14 Deep reinforcement learning

PA 4 Due 4/16
4/19
 Project Presentations


4/21
 Project Presentations


4/27
 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.