Schedule

Date Topic Notes Reading Assignment out/due
9/5/2013 Introduction, Intelligent Agents Intro, Ch 3 Ch 2
9/12/2013 Classical search, optimization Ch 3, Ch 4, quiz 1 Ch 3, Ch 4.1 -- 4.3 HW 1 out
If you're new to python, see: the Lynda tutorial
9/19/2013 Adversarial search, CSP Ch 5, Ch 6 Ch 6.1 -- 6.3
9/26/2013 CSP, Probability Ch 6, Ch 14, quiz 3 Ch 5.1 -- 5.4 HW 1 due, HW 2 out (actually, it was out late 9/27/2013)
10/3/2013 Bayesian networks On blackboard in class, quiz 4 Ch 13
10/10/2013 Bayes networks On blackboard in class, quiz 5 Ch 14.1 -- 14.5 HW 2 due
10/17/2013 HMMs Ch 15.1 -- 15.3, Ethan Schreiber's one-page writeup on d-separation HW 3 out
10/24/2013 Midterm Exam, MDPs, reinforcement learning Here is a Java applet that illustrates q-learning in a very simple gridworld environment. This one illustrates value iteration. This one illustrates reinforcement learning applied to the inverted pendulum task. This one illustrates a robot learning to flip pancakes using reinforcement learning. This one is a video of q-learning (w/ dyna updates) in a grid world. HW 4 out
10/31/2013 MDPs, Reinforcement Learning SB 3.1--3.3, 3.5--3.6, SB 4.1--4.4, SB 6.5 HW 3 due
11/7/2013 ML Ch 18, Torralba's notes on Boosting Ch 18.1--18.2, 18.6--18.9 HW 4 due !!! HW4 due date postponed until 5pm 11/8 !!!, HW 5 out
11/14/2013 Statistical ML Ch 20
11/21/2013 HW 5 due
11/28/2013 THANKSGIVING
12/5/2013


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.