The Future of Machine Intelligence
by David Beyer
Publisher: O'Reilly Media 2016
Number of pages: 78
The series of interviews in this exclusive report unpack concepts and innovations that represent the frontiers of ever-smarter machines. You’ll get a rare glimpse into this exciting field through the eyes of some of its leading minds.
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by Richard S. Sutton, Andrew G. Barto - The MIT Press
The book provides a clear and simple account of the key ideas and algorithms of reinforcement learning. It covers the history and the most recent developments and applications. The only necessary mathematical background are concepts of probability.
by Robert E. Schapire, Yoav Freund - The MIT Press
Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate 'rules of thumb'. A remarkably rich theory has evolved around boosting, with connections to a range of topics.
by G. James, D. Witten, T. Hastie, R. Tibshirani - Springer
This book provides an introduction to statistical learning methods. It contains a number of R labs with detailed explanations on how to implement the various methods in real life settings and it is a valuable resource for a practicing data scientist.
by Carl E. Rasmussen, Christopher K. I. Williams - The MIT Press
Gaussian processes provide a principled, practical, probabilistic approach to learning in kernel machines. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics.