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Moritz Hardt and Benjamin Recht . 2021. Patterns, predictions, and actions: A story about machine learning. mlstory.org. arXiv 2102.05242 cs.LG. [book.ml, ebook] url url google scholar books
Aston Zhang, Zack C. Lipton, Mu Li and Alex J. Smola. 2020. Dive into Deep Learning. (web book). [book.ml] url google scholar books
E. Stevens and L. Antiga. 2020. Deep Learning with Pytorch. Manning Publications Company. [book.ml] url pdf google scholar books
Andrew Ng. 2017. Machine Learning Yearning. Online Draft. [book.ml] url pdf google scholar books
Y. Goldberg and G. Hirst. 2017. Neural Network Methods in Natural Language Processing. Morgan & Claypool Publishers. [book.language, book.ml, ebook, missing] url pdf google scholar books
Ian J. Goodfellow, Yoshua Bengio and Aaron Courville. 2014. Deep Learning. (online draft). [book.ml, ebook] url pdf google scholar books
Shai Shalev-Shwartz and S. Ben-David. 2014. Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press. [book.ml, ebook, perceptron] url pdf google scholar books
Ethem Alpaydın. 2014. Introduction to Machine Learning. The MIT Press. [book.ml] pdf google scholar books
Andrew Gelman, et al. 2013. Bayesian Data Analysis. Chapman and Hall/CRC. [book.ml] pdf pdf url google scholar books
G. James, D. Witten, T. Hastie and R. Tibshirani. 2013. An Introduction to Statistical Learning: with Applications in R. Springer. [book.ml, ebook] pdf url url google scholar books
B. Lantz. 2013. Machine Learning with R. Packt Publishing. [book.ml, ebook] url pdf google scholar books
Hal Daumé III. 2013. A course in machine learning. Self Published. [book.ml, ebook, nlpcourse] url pdf google scholar books
D. Barber. 2012. Bayesian Reasoning and Machine Learning. Cambridge University Press. [book.ml, ebook] pdf url url google scholar books
M. Mohri, A. Rostamizadeh and A. Talwalkar. 2012. Foundations of Machine Learning. MIT Press. [book.ml, ebook, perceptron] url pdf google scholar books
K.P. Murphy. 2012. Machine Learning: A Probabilistic Perspective. Mit Press. [book.ml, ebook] pdf url url pdf google scholar books
Y.S. Abu-Mostafa, M. Magdon-Ismail and H.T. Lin. 2012. Learning from Data: A Short Course. AMLBook.com. [book.ml, perceptron] url url pdf google scholar books
S. Sra, S. Nowozin and S.J. Wright. 2012. Optimization for Machine Learning. MIT Press. [book.ml, ebook] url pdf google scholar books
G. Montavon, G. Orr and K.R. Müller. 2012. Neural Networks: Tricks of the Trade. Springer Berlin Heidelberg. [book.ml, ebook] url pdf google scholar books
Richard S. Sutton and Andrew G. Barto. 2012. Introduction to reinforcement learning. MIT Press. (draft 2nd ed.). [bridge, rl, book.ml] url url pdf google scholar books
H. Du. 2010. Data Mining Techniques and Applications: An Introduction. Cengage Learning. [book.ml] url google scholar books
C. Szepesvari. 2010. Algorithms for Reinforcement Learning. Morgan & Claypool. [rl, book.ml] url pdf pdf google scholar books
M. Elad. 2010. Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing. Springer. [book.ml, perceptron, ebook] url pdf google scholar books
D. Koller and N. Friedman. 2009. Probabilistic Graphical Models: Principles and Techniques. Mit Press. [book.ml, ebook] url pdf pdf google scholar books
Trevor Hastie, Robert Tibshirani and Jerome Friedman. 2009. The Elements of Statistical Learning. Springer. [book.ml, ebook] pdf url pdf google scholar books
Alexander Johannes Smola and S.V.N. Vishwanathan. 2008. Introduction to Machine Learning. Cambridge University Press. [book.ml, ebook] pdf pdf google scholar books
D.P. Bertsekas. 2007. Approximate Dynamic Programming. In Dynamic Programming and Optimal Control, no v. 2. Athena Scientific. [rl, book.ml] pdf pdf google scholar
G. BakIr and Neural Information Processing Systems Foundation . 2007. Predicting Structured Data. MIT Press. [book.ml] url pdf google scholar books
Christopher M. Bishop. 2006. Pattern Recognition and Machine Learning. Springer. [book.ml, ebook] pdf pdf url google scholar books
N. Cesa-Bianchi and G. Lugosi. 2006. Prediction, Learning, and Games. Cambridge University Press. [book.ml, ebook] url pdf google scholar books
O. Chapelle, B. Schölkopf and A. Zien, editors. 2006. Semi-supervised learning. MIT Press. [book.ml] google scholar books
J. Nocedal and S. Wright. 2006. Numerical Optimization. Springer. [book.ml, ebook] url pdf pdf google scholar books
C. E. Rasmussen and C. K. I. Williams. 2006. Gaussian Processes for Machine Learning. MIT Press. [book.ml, missing, ebook] url pdf google scholar books
Larry Wasserman. 2006. All of Nonparametric Statistics. Springer. [book.ml] google scholar books
I. Guyon, S. Gunn, M. Nikravesh and L.A. Zadeh. 2006. Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing). Springer-Verlag New York, Inc. Secaucus, NJ, USA. [book.ml, ebook] pdf google scholar books
Ian H. Witten and Eibe Frank. 2005. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann. (Ercument-2011-11-01). [ML, book.ml, missing, ebook] pdf google scholar books
S.P. Boyd and L. Vandenberghe. 2004. Convex Optimization. Cambridge University Press. [book.ml, ebook, comp542] url url google scholar books
W. N. Venables and D. M. Smith. 2004. An Introduction to R. [book.ml] google scholar
Ethem Alpaydın. 2004. Introduction to Machine Learning. Prentice Hall. [book.ml] google scholar books
David G. Stork and Elad Yom-Tov. 2004. Computer Manual in Matlab to accompany Pattern Classification, 2nd ed.. Wiley. [book.ml] google scholar books
David G. Stork. 2004. Solution Manual to Accompany Pattern Classification, 2nd ed.. [book.ml] google scholar books
T. Jebara. 2004. Machine Learning: Discriminative and Generative. Kluwer Academic Publishers. [book.ml] google scholar books
Edwin Thompson Jaynes. 2003. Probability Theory: The Logic of Science. Cambridge. [book.ml] pdf pdf google scholar books
David J. C. Mackay. 2003. Information Theory, Inference, and Learning Algorithms. Cambridge. [book.ml, ebook] pdf url djvu url google scholar books
Larry Wasserman. 2003. All of Statistics. Springer. [book.ml] google scholar books
W. N. Venables and B. D. Ripley. 2002. Modern Applied Statistics with S. Springer. [book.ml] google scholar books
Bernhard Schölkopf and Alexander J Smola. 2002. Learning with kernels: Support vector machines, regularization, optimization, and beyond. MIT press. [perceptron, book.ml, ebook] pdf google scholar books
Richard O. Duda, Peter E. Hart and David G. Stork. 2000. Pattern Classification. Wiley Interscience. [book.ml, ebook] djvu google scholar books
Judea Pearl. 2000. Causality. Cambridge. [book.ml] google scholar books
A. Webb. 1999. Statistical Pattern Recognition. A Hodder Arnold Publication. [book.ml, ebook] pdf google scholar books
Vladimir N. Vapnik. 1999. The Nature of Statistical Learning Theory. Springer. [book.ml] google scholar books
N. Cristianini and J. Shawe-Taylor. 1999. An introduction to support Vector Machines: and other kernel-based learning methods. Cambridge University Press New York, NY, USA. [book.ml, ebook] url epub google scholar books
Richard S. Sutton and Andrew G. Barto. 1998. Introduction to reinforcement learning. MIT Press. [bridge, rl, book.ml] url url google scholar books
G. Orr and K.R. Müller. 1998. Neural Networks: Tricks of the Trade. Springer-Verlag. [book.ml, ebook] url pdf djvu google scholar books
Tom Mitchell. 1997. Machine Learning. McGraw Hill. [ML, book.ml, ebook] pdf google scholar books
Brian Ripley. 1996. Pattern recognition and neural networks. Cambridge. mtsezgin-2011-11-18. [book.ml, missing] google scholar books
D. S. Sivia. 1996. Data Analysis, A Bayesian Tutorial. Oxford. [book.ml] google scholar books
Teuvo Kohonen. 1995. Self organizing maps. Springer. read. [book.ml] google scholar books
B. Muller, J. Reinhardt and M.T. Strickland. 1995. Neural networks: an introduction. Springer. [book.ml] google scholar books
Christopher M. Bishop. 1995. Neural networks for pattern recognition. Oxford. [book.ml] google scholar books
Mohamad H. Hassoun. 1995. Fundamentals of artificial neural networks. MIT Press. [book.ml] google scholar books
Michael Kearns and Umesh V. Vazirani. 1994. An introduction to computational learning theory. MIT Press. [book.ml] pdf google scholar books
D.H. Wolpert. 1994. The mathematics of generalization. Addison-Wesley. [book.ml] google scholar books
Simon Haykin. 1994. Neural networks: a comprehensive foundation. Prentice Hall. [book.ml] google scholar books
S.J. Hanson, et al. 1994. Computational learning theory and natural learning systems (3 vols). MIT Press. [book.ml] google scholar books
John Hertz, Anders Krogh and Richard G. Palmer. 1991. Introduction to the theory of neural computation. Addison Wesley. [book.ml] google scholar books
Jude W. Shavlik and Thomas G. Dietterich, editors. 1990. Readings in machine learning. Morgan Kaufmann. [book.ml] google scholar books
Judea Pearl. 1988. Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann. [book.ml] google scholar books
Daniel N. Osherson, Michael Strob and Scott Weinstein. 1986. Systems that learn. MIT Press. [book.ml] google scholar books
James O. Berger. 1985. Statistical Decision Theory and Bayesian Analysis. Springer. [book.ml] google scholar books
Ryszard Michalski, et al, editors. 1983. Machine Learning (4 vols). Morgan Kaufmann. [book.ml] google scholar books

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