Schedule

Date Topic Notes Reading Assignment out/due
9/8/2016 Introduction, Intelligent Agents Course Introduction. In addition to the Python/autograder tutorial linked to the right, feel free to have a look at the official Python Tutorial. Ch 2 Python/autograder Tutorial (optional, but recommended). This introduces you to Python and the autograder.
9/15/2016 Search Graph Search and Heuristic Search.
Ch 3, Ch 4.1 -- 4.3 PA 1 out
9/22/2016 Constraint Satisfaction Constraint Satisfaction
 Ch 6
9/29/2016 Adversarial search Adversarial search
 Ch 5.1 -- 5.4 PA 1 due, PA 2 out
10/6/2016 Intro to probability and decisions Probability and decisions
 Ch 13.1 -- 13.5, 16.1-16.6
10/13/2016 Markov decision processes (MDPs) MDPs
 SB 3.1--3.3, 3.5--3.6, SB 4.1--4.4
Optional: AIMA 17.1-17.3 		PA 2 due, PA 3 out
10/20/2016 Reinforcement Learning RL
 SB 6.5 Project out
10/27/2016 Markov Models, Hidden Markov Models and Particle Filters HMMs
 Ch 15.1 -- 15.3 PA 3 due, PA 4 out
11/3/2016 Graphical Models and Inference Bayes Nets
 Ch 13.3 -- 13.5, Ch 14.1 -- 14.5 Project proposal due
11/10/2016 Graphical Models and Learning Naive Bayes and Perceptrons
 Ch 20.1-20.2 PA 4 due, PA 5 out
11/17/2016 Machine Learning Decision Trees
 Ch 18
11/24/2016 No class!! Thanksgiving

12/1/2016 Review Review
PA 5 due
12/8/2016 Final Exam

The final exam will be held at our regular class time (6pm) in the regular classroom. It will be 3 hours long. The exam will be open book/notes, but you cannot use your cell phone or a computer (closed internet). Non-programmable calculators are allowed (but you won't need them).

12/14/2016 Final project due

Project due

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.