The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World

by Pedro Domingos

these, the closest in content to this book is, not coincidentally, the one I teach (www.coursera.org/course/machlearning). Two other options are Andrew Ng’s course (www.coursera.org/course/ml) and Yaser Abu-Mostafa’s (http://work.caltech.edu/telecourse.html). The next step is to read a textbook. The closest to this book, and one of the most accessible, is Tom Mitchell’s Machine Learning* (McGraw-Hill, 1997). More up-to-date, but also more mathematical, are Kevin Murphy’s Machine Learning: A Probabilistic Perspective* (MIT Press, 2012), Chris Bishop’s Pattern Recognition and Machine Learning* (Springer, 2006), and An Introduction to Statistical Learning with Applications in R,* by Gareth James, Daniela Witten, Trevor Hastie, and Rob Tibshirani (Springer, 2013). My article “A few useful things to know about machine learning” (Communications of the ACM, 2012) summarizes some of the “folk knowledge” of machine learning that textbooks often leave implicit and was one of the starting points for this book. If you know how to program and are itching to give machine learning a try, you can start from a number of open-source packages, such as Weka (www.cs.waikato.ac.nz/ml/weka). The two main machine-learning journals are Machine Learning and the Journal of Machine Learning Research. Leading machine-learning conferences, with yearly proceedings, include the International Conference on Machine Learning, the Conference on Neural Information Processing Systems, and the International Con...

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