Resources
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
- Python home (get Python)
- Python reference
- Python in Nutshell (requires UW login)
- CSE VM
- Linux Pocket Guide (requires UW login)
UW Services and Support