Schedule

Date Topic Notes Reading Assignment out/due
9/6/2017 Introduction Course Introduction
  • A brief history of AI
  • AI in today's world
  • Course Details
  • In-Class Questionnaire
  • Agents and Their Problems
 Ch 2 ~ 3 Python/autograder Tutorial introduces you to Python and the autograder. Also look at official Python Tutorial.
9/11/2017 Uninformed Search Strategies Ch. 3.1 ~ 3.4 PA1 Out
9/13/2017 Heuristic Search Strategies Ch 3.5 ~ 3.7
9/18/2017 Local Search Ch 4
9/20/2017 Constraint Satisfaction Ch 6
9/25/2017 CSP II and Adversarial Search Ch 5 PA1 Due; PA2 out
9/27/2017 Adversarial Search II Ch 5
10/2/2017 Planning Pllanning techniques Ch 10
10/4/2017 Probability Refresher and Review before Midterm Introduction to dealing with uncertainty; Ch 13.1 & 13.2 PA2 Due
10/9/2017 Columbus Day
10/11/2017 Midterm I
10/16/2017 Midterm I Answers Review
10/18/2017 Decision Making: MDPs
10/23/2017 Reinforcement Learning I Ch23 PA 3 out
10/25/2017 Reinforcement Learning II Q learning Ch 23
10/30/2017 Bayes Net I Ch 16
11/1/2017 Bayes Net II Ch 17 PA 3 Due; PA 4 out
11/6/2017 Project Proposal Presentation
11/8/2017 Reasoning over time Ch 15
11/13/2017 Machine Learning PA 4 Due
11/15/2017 Midterm II
11/20/2017 Midterm II Review
11/22-11/26/2017 Thanksgiving
11/27/2017 Applications Modeling Human Behavior, Affective Computing and Virtual Humans
11/29/2017 Applications Personality and Hurricane
12/4/2017 Project Presentations See Piazza for schedule of presenters
12/5/2017 PacMan Code due Projects doing PacMan must deliver their code by 12/5 at 5pm
12/6/2017 Project Presentations See Piazza for schedule of presenters
12/13/2017 Project Reports Due Report and all code due at 9 AM December 13 - 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.