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Machine Learning: A Bayesian and Optimization Perspective

Machine Learning: A Bayesian and Optimization Perspective

Machine Learning: A Bayesian and Optimization Perspective
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Machine Learning: A Bayesian and Optimization Perspective Hardback - 2015

by Theodoridis, Sergios

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Academic Press, 2015-04-10. 1. hardcover. New. 7.75x2.00x9.50. Buy with confidence. Excellent Customer Service & Return policy.
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Details

  • Title Machine Learning: A Bayesian and Optimization Perspective
  • Author Theodoridis, Sergios
  • Binding Hardback
  • Edition 1
  • Condition New
  • Pages 1062
  • Volumes 1
  • Language ENG
  • Publisher Academic Press
  • Publication date 2015-04-10
  • Illustrated Yes
  • Bookseller's Inventory # DADAX0128015225
  • ISBN 9780128015223 / 0128015225
  • Weight 5.05 lbs (2.29 kg)
  • Dimensions 9.25 x 7.5 x 2 in (23.50 x 19.05 x 5.08 cm)
  • Size 7.75x2.00x9.50
  • Category Technology & Industrial Arts
  • Library of Congress subjects Bayesian statistical decision theory, Mathematical optimization
  • Dewey Decimal Code 006.31
  • Quantity available 6

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From the publisher

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.

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