AI Reading List: Textbooks
Below is a portion of my informal list of readings related to Artificial Intelligence (AI). This started out as a very short list created for use in conjunction with an academic presentation and has now grown much larger. Please let me know if you have any corrections, additions, suggestions, etc. It is very idiosyncratic and not meant to be comprehensive. Please feel free to share with others.
Artificial Intelligence (AI) Reading List, by Philip Rubin
Textbooks:
Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong. Mathematics for Machine Learning. Cambridge: 2020. (See PDF.)
Stuart Russell and Peter Norvig. Artificial Intelligence: A Modern Approach. Fourth Edition. Pearson: 2020. (See PDF.)
Andriy Burkov. The Hundred-Page Machine Learning Book. Andriy Burkov: 2019. (See online version.)
R. S. Sutton and A. G. Barto. Reinforcement Learning: An Introduction. 2nd ed. The MIT Press: 2017. (See online information.)
Ian Goodfellow, Yoshua Bengion, and Aaron Courville. Deep Learning. The MIT Press: 2016. (See online version.)
Michael Nielsen. Neural Networks and Deep Learning. 2015-2019. (Online only.)
Shai Shalev-Shwartz and Shai Ben-David. Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press: 2014. (See PDF.)
Nils J. Nilsson. The Quest for Artificial Intelligence: A History of Ideas and Achievements. Cambridge University Press: 2009. Web Version. 2010. (See PDF.)
Melanie Mitchell. An Introduction to Genetic Algorithms. The MIT Press: 1996. (See PDF.)