Textbooks

• [Bishop] Pattern Recognition and Machine Learning by Christopher Bishop (2007)
• [Goodfellow] Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville (2016)
• [Murphy] Machine Learning: A Probabilistic Perspective by Kevin Murphy (2012)
• [Murphy'22] Probabilistic Machine Learning: An Introduction, by Kevin P. Murphy (2022)
Chapters from these four books are cited as optional reference materials for the slides.
There are several other related references. [click to expand the list]
The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie, Robert Tibshirani and Jerome Friedman (2009)
Information Theory, Inference, and Learning Algorithms, by David MacKay (2003)
Bayesian Reasoning and Machine Learning, by David Barber (2012).
Understanding Machine Learning: From Theory to Algorithms, by Shai Shalev-Shwartz and Shai Ben-David (2014)
Foundations of Machine Learning, by Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar (2018)
Dive into Deep Learning, by Aston Zhang, Zachary Lipton, Mu Li, and Alexander J. Smola (2019)
Mathematics for Machine Learning, by Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong (2019)
A Course in Machine Learning, by Hal Daume III (2017)
Hands-on Machine Learning with Scikit-Learn and TensorFlow, by Aurelien Geron (2017)
Machine Learning, by Tom Mitchell (1997)
Introduction to Data Mining, by Pang-Ning Tan, Michael Steinbach, Anuj Karpatne, and Vipin Kumar (2020)
Machine Learning, Dynamical Systems and Control, by Steven L. Brunton and J. Nathan Kutz (2019)