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Machine learning = a Bayesian and op...

Theodoridis, Sergios, (1951-)

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Machine learning = a Bayesian and optimization perspective /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine learning/ Sergios Theodoridis.
其他題名:	a Bayesian and optimization perspective /
作者:	Theodoridis, Sergios,
出版者:	London ;Elsevier : : 2020.,
面頁冊數:	1 online resource (xxvii, 1031 p.) ;illustrations
標題:	Machine learning - Mathematical models. -
電子資源:	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:

1005796
Machine learning
--Mathematical models.Index Terms--Genre/Form:

542853
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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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).
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650 0 $a Bayesian statistical decision theory. $3 551404
650 0 $a Mathematical optimization. $3 517763
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