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 (PA0) introduces you to Python and the autograder. Also look at official Python Tutorial.
1/9 Agents, Problem Domains and Search Agents and Their Problems Ch 2
1/14 Uninformed Search Search I
Ch 3.1 -- 3.4 PA1 out
1/16 Informed Search Search II
Ch 3.5 --3.6, 4.1
1/21 Constraint Satisfaction Problems (CSP)
Ch 6
1/23 CSP & Adversarial Search
Ch 5.1 -- 5.4 PA1 Due 1/24; PA2 out
1/28 Adversarial Search


1/30 Uncertainty and Probability (Sabbir)

Ch 13.1 -- 13.5
2/4 Graphical Models 
Ch 14.1 -- 14.5
2/6 Bayes Nets

PA 2 Due 2/7
2/11 Review
 

2/13 Inference
  Project description
2/18 Midterm I


2/20 Markov Models
Ch 15.1 -- 15.3
2/25 Utility/Decision theory
Ch 16.1 -- 16.3 PA3 out
2/27 Markov decision processes (MDPs)
Ch 17.1 -- 17.3
3/3 No class! (Spring Break)


3/5 No class! (Spring Break)
 
3/10 Planning with MDPs (optional: SB 3.1--3.3, 3.5--3.6, SB 4.1--4.4)
3/12 Reinforcement learning  
Ch 21 Project proposal due
3/17 Reinforcement learning (cont.)
(optional: SB 6.5) PA3 due 3/26; PA4 out
3/19 Intro to machine learning
Ch 18.1 -- 18.2
3/24 Perceptrons and classification (Sabbir)
Ch 18.4 -- 18.7
3/26 More supervised learning
Ch 18.3
3/31 ML (continued)

PA 4 Due
4/2
Midterm II


4/7
Deep Learning
Ch 18.10 -- 18.11
4/9
Project Presentations


4/14
Project Presentations


4/21 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.