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Machine learning = a Bayesian and optimization perspective /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Machine learning/ Sergios Theodoridis.
Reminder of title:	a Bayesian and optimization perspective /
Author:	Theodoridis, Sergios,
Published:	London ;Elsevier : : 2020.,
Description:	1 online resource (xxvii, 1031 p.) ;illustrations :
Subject:	Machine learning - Mathematical models. -
Online resource:	https://www.sciencedirect.com/science/book/9780128188033
ISBN:	9780128188040 (electronic bk.)

Machine learning = a Bayesian and optimization perspective /
Theodoridis, Sergios,1951-

Machine learninga Bayesian and optimization perspective /[electronic resource] :Sergios Theodoridis. - 2nd edition. - London ;Elsevier :2020. - 1 online resource (xxvii, 1031 p.) ;illustrations

Includes bibliographical references and index.

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. All major classical techniques: Mean/Least-Squares regression and filtering, Kalman filtering, stochastic approximation and online learning, Bayesian classification, decision trees, logistic regression and boosting methods. The latest trends: Sparsity, convex analysis and optimization, online distributed algorithms, learning in RKH spaces, Bayesian inference, graphical and hidden Markov models, particle filtering, deep learning, dictionary learning and latent variables modeling. Case studies - protein folding prediction, optical character recognition, text authorship identification, fMRI data analysis, change point detection, hyperspectral image unmixing, target localization, channel equalization and echo cancellation, show how the theory can be applied. MATLAB code for all the main algorithms are available on an accompanying website, enabling the reader to experiment with the code.

ISBN: 9780128188040 (electronic bk.)Subjects--Topical Terms:

639640
Machine learning
--Mathematical models.Index Terms--Genre/Form:

554714
Electronic books.

LC Class. No.: Q325.5 / .T43 2020

Dewey Class. No.: 006.3/1

Machine learning = a Bayesian and optimization perspective /
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100 1 $a Theodoridis, Sergios, $d 1951- $3 636605
245 1 0 $a Machine learning $h [electronic resource] : $b a Bayesian and optimization perspective / $c Sergios Theodoridis.
250 $a 2nd edition.
260 $a London ; $a San Diego : $c 2020. $b Elsevier : $b Academic Press,
300 $a 1 online resource (xxvii, 1031 p.) ; $b illustrations
504 $a Includes bibliographical references and index.
520 $a 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. All major classical techniques: Mean/Least-Squares regression and filtering, Kalman filtering, stochastic approximation and online learning, Bayesian classification, decision trees, logistic regression and boosting methods. The latest trends: Sparsity, convex analysis and optimization, online distributed algorithms, learning in RKH spaces, Bayesian inference, graphical and hidden Markov models, particle filtering, deep learning, dictionary learning and latent variables modeling. Case studies - protein folding prediction, optical character recognition, text authorship identification, fMRI data analysis, change point detection, hyperspectral image unmixing, target localization, channel equalization and echo cancellation, show how the theory can be applied. MATLAB code for all the main algorithms are available on an accompanying website, enabling the reader to experiment with the code.
588 $a Description based on online resource; title from digital title page (viewed on February 28, 2020).
650 0 $a Machine learning $x Mathematical models. $3 639640
650 0 $a Bayesian statistical decision theory. $3 527671
650 0 $a Mathematical optimization. $3 527675
655 4 $a Electronic books. $2 local $3 554714
856 4 0 $u https://www.sciencedirect.com/science/book/9780128188033