Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning series)
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The MIT Press
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The deep learning revolution has transformed the field of machine learning over the last decade. It was inspired by attempts to mimic the way the brain learns but it is grounded in basic principles of statistics, information theory, decision theory, and optimization. This book does an excellent job of explaining these principles and describes many of the classical machine learning methods that make use of them. It also shows how the same principles can be applied in deep learning systems that contain many layers of features. This provides a coherent framework in which one can understand the relationships and tradeoffs between many different ML approaches, both old and new.
Geoffrey Hinton, Emeritus Professor of Computer Science, University of Toronto; Engineering Fellow, Google
Geoffrey Hinton, Emeritus Professor of Computer Science, University of Toronto; Engineering Fellow, Google
About the Author
Kevin P. Murphy is a Research Scientist at Google in Mountain View, California, where he works on AI, machine learning, computer vision, and natural language understanding.
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