The resources available for this course are many and varied. As AI is still a quickly developing field, the serious student will continuously seek out new material and follow current literature. For this course the following will get you started.
Academic Integrity and Conduct
Books:
Stuart Russell & Peter Norvig, Artificial Intelligence: A Modern Approach, Prentice-Hall, Fourth Edition (2020) [R&N].
Melanie Mitchell, Artificial Intelligence: A Guide for Thinking Humans, Farrar, Straus, and Giroux. 2019.
Richard Sutton & Andrew Barto, Reinforcement Learning: An Introduction Second Edition, MIT Press. 2018 (limited chapters freely available online) [S&B]
Barocas, Hardt, and Narayanan. Fairness and Machine Learning. 2019. [B&H&N]
Murphy, Kevin P. Probabilistic Machine Learning: An introduction, MIT Press. 2012,2022. [M]
Mausam, Andrey Kolobov. Planning with Markov Decision Processes: An AI Perspective Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan and Claypool Publishers. June 2012. (free online version if accessed from UW) [M&K]
Computing and Python
Linux Pocket Guide (requires UW login)
Python home (get Python)
Python in Nutshell (requires UW login)
UW Services and Support