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Machine Learning and AI: Find Textbooks

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Below are some helpful Machine Learning textbooks. 
Thanks Prof. Matt Gormley, Brynn Edmunds, and Daniel Bird for your help compiling these lists! 
Most of our book collections are electronic. By clicking on links included in the lists, you will be able to access full-text through a CMU-specific EZproxy. All you need to do is to login with your Andrew ID. 
Suggestions for new book purchases? Email me at

Machine Learning Textbooks

Frequently used: 


Machine Learning. Mitchell, T. (1997).  New York: McGraw-Hill.

Machine Learning: A Probabilistic Perspective. Murphy, K. (2012).  Cambridge, Mass.: MIT Press.

Pattern Recognition and Machine Learning. Bishop, C. (2006). New York: Springer.


The elements of statistical learning : data mining, inference, and prediction. Hastie, T., Tibshirani, R., & Friedman, J. (2001).  New York: Springer.

Deep Learning. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Cambridge, Massachusetts: The MIT Press.

Reinforcement Learning: An IntroductionSutton, R., & Barto, A. (1998). Cambridge, Mass.: MIT Press. 

Reinforcement Learning: An Introduction (2nd edition, online draft)Sutton, R., & Barto, A. (2018)


Understanding Machine Learning: From Theory to Algorithms. Shalev-Shwartz, S., & Ben-David, S. (2014). New York, NY, USA: Cambridge University Press.


Machine Learning Textbooks - others


Machine Learning: A Concise Introduction. Knox, S. (2018).  Hoboken, New Jersey: Wiley.

Optimization for Machine Learning. Nowozin, S., Sra, S., & Wright, S. (2012).  Cambridge, Mass.: MIT Press.

Unsupervised learning algorithms. Aydin, K., & Celebi, M. (2016). Cham: Springer. doi:10.1007/978-3-319-24211-8 

An Introduction to Statistical Learning: With Applications in R. James, G. et al. (2017). New York, NY: Springer New York.

Introduction to statistical machine learning. Sugiyama, M. (2016). Amsterdam: Elsevier.

Probabilistic Graphical Models: Principles and Techniques. Koller, D., & Friedman, N. (2009). Cambridge, Mass.: MIT Press.

Machine learning : a Bayesian and optimization perspective. Theodoridis, S. (2015). Amsterdam, [Netherlands]: Academic Press.

Machine learning : an algorithmic perspective Marsland, S. (2015).   (2nd ed.). Boca Raton: CRC Press.

Probabilistic Graphical Models: Principles and Applications. Sucar, L. (2015). Probabilistic Graphical Models Principles and Applications . London: Springer London. doi:10.1007/978-1-4471-6699-3

Learning From Data.  (Used by CalTech MOOC) (2012). 

Introduction to pattern recognition and machine learning. Murty, M., & Devi, V. (2015).  New Jersey: World Scientific.

Python machine learning by example : easy-to-follow examples that get you up and running with machine learning. Liu, Y. (2017). Birmingham, [England] ;: Packt Publishing.

An Introduction to Machine Learning. 2nd ed. 2017. [Online]. Kubat, M. (2017). Cham: Springer International Publishing.

Foundations of machine learning. Mohri, M., Rostamizadeh, A., & Talwalkar, A. (2012).  Cambridge, MA: MIT Press.

An Elementary Introduction to Statistical Learning Theory. Kulkarni, S., & Harman, G. (2011). Hoboken, NJ, USA: John Wiley & Sons, Inc. doi:10.1002/9781118023471

Probability for Statistics and Machine Learning Fundamentals and Advanced Topics. DasGupta, A. (2011). New York, NY: Springer New York. doi:10.1007/978-1-4419-9634-3

Introduction to machine learning. Alpaydin, E. (2014). (Third edition.). Cambridge, Massachusetts: MIT Press.

Principles and theory for data mining and machine learning. Clarke, B., Fokoué, E., & Zhang, H. (2009). Berlin ;: Springer.

Encyclopedia of Machine Learning.Sammut, C., & Webb, G. (2010). Boston, MA: Springer US. doi:10.1007/978-0-387-30164-8