Reading should be done after the associated lecture. Demonstrations during the lecture will be done in the Julia language using IJulia notebooks.

Date Topic Reading Due
Sep 25 Overview DMU 1
Sep 27 Probabilistic Representations DMU 2.1
Oct 2 Probabilistic Inference DMU 2.2
Oct 4 Parameter Learning DMU 2.3
Oct Midterm 1 Problem Review Session, 930am - 1020am, @ Gates B03 (bring your questions!)
Oct 9 Midterm 1 @ T.B.D (in class time 130pm-250pm)
Oct 11 Structure Learning DMU 2.4 Project 0
Oct 16 Decision Theory and Games DMU 3.1-3.3
Oct 18 Markov Decision Processes DMU 4.1-4.4 Project 1
Oct 23 Approximate Dynamic Programming DMU 4.5-4.7
Oct 25 Exploration and Exploitation DMU 5.1
Oct 30 Model-Based Reinforcement Learning DMU 5.2-5.3
Nov 1 Model-Free Reinforcement Learning DMU 5.4-5.5
Nov 6 Deep Reinforcement Learning Slides
Nov 8 State Uncertainty DMU 6.1-6.2
Nov 10 Project 2
Nov 13 Exact POMDP Methods DMU 6.3
Nov 15 Offline POMDP Methods DMU 6.4 Proposal due at 1159pm
Nov 16 Online POMDP Methods (Take-Home Quiz Released After Lecture) DMU 6.5
Nov 20 Thanksgiving week
Nov 24 Thanksgiving week
Nov 27 Take-home Quiz
Nov Midterm 2 Problem Review Session, 930am - 1020am, @ Thornton 102 (bring your questions!)
Nov 29 Midterm 2 @ T.B.D (in class time 130pm-250pm)
Dec 4 Guest Lectures
Optimal Aircraft Rerouting during Commercial Space Launches [Rachael Tompa] Slides
The Value of Inferring the Internal State of Traffic Participants for Autonomous Freeway Driving [Zach Sunberg] Slides
MDP Policy Compression through Neural Networks [Kyle Julian] Slides
Dec 6 Guest Lectures
Cooperative Control Using Deep Reinforcement Learning [Max Egorov / Jayesh Gupta] Slides or folder
Modeling Human Driver Behavior with Imitation Learning [Jeremy Morton] Slides
Making POMDPs Great Again [Louis Dressel] Slides
Dec 11 No class Final Project Paper
Dec 13 No class Peer Review*

Note: The dates for the various topics are approximate. Some topics may span multiple lectures. However, the schedule for the midterms will stay fixed. *Peer review can be submitted remotely online.