The primary resources for this course are the lecture slides and homework assignments on the front page.

Previous Offerings

A full version of this course was offered in Fall 2022, Fall 2021, Fall 2020, Fall 2019, Fall 2018, Fall 2017 and Spring 2017. Lecture videos from Fall 2021 are available here; those from Fall 2020 are available here; those from Fall 2019 are available here; those from Fall 2018 are available here; those from Fall 2017, here; those from Spring 2017, here. An abbreviated version of this course was offered in Fall 2015.

Courses

• Geoff Hinton on Coursera
• Andrew Ng on Coursera
• David Silver's course on reinforcement learning
• Nando de Freitas' course on machine learning
• Andrej Karpathy's course on neural networks

Relevant Textbooks

• Deep Learning
• Sutton & Barto, Reinforcement Learning: An Introduction
• Szepesvari, Algorithms for Reinforcement Learning
• Bertsekas, Dynamic Programming and Optimal Control, Vols I and II
• Puterman, Markov Decision Processes: Discrete Stochastic Dynamic Programming
• Powell, Approximate Dynamic Programming

Misc Links

• Data visualization handout