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Machine Learning: A Bayesian and Optimization Perspective
This tutorial text gives a unifying perspective on machine learning by covering both probabilistic and deterministic approaches—which are based on optimization techniques—together with the Bayesian inference approach, whose essence lies in the use of a hierarchy of probabilistic models. The book presents the major machine learning methods as they have been developed in different disciplines, such as statistics, statistical and adaptive signal processing, and computer science. Focusing on the physical reasoning behind the mathematics, all the various methods and techniques are explained in depth, supported by examples and problems, giving an invaluable resource to the student and researcher for understanding and applying machine learning concepts.
The book builds carefully from the basic classical methods to the most recent trends, with chapters written to be as self-contained as possible, making the text suitable for different courses: pattern recognition, statistical/adaptive signal processing, statistical/Bayesian learning, as well as short courses on sparse modeling, deep learning, and probabilistic graphical models.
The book builds carefully from the basic classical methods to the most recent trends, with chapters written to be as self-contained as possible, making the text suitable for different courses: pattern recognition, statistical/adaptive signal processing, statistical/Bayesian learning, as well as short courses on sparse modeling, deep learning, and probabilistic graphical models.
- GenresArtificial Intelligence
1062 pages, Hardcover
First published April 2, 2015
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Displaying 1 of 1 review
July 11, 2015
Comprehensive and up to date. Gets to the mathematical foundations quickly but sometimes lingers too long there. Not a handbook of techniques for the practicing data scientist, but its interesting to iterate through familiar techniques using a Bayesian / Optimization lens.
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