Machine Learning Bayesian Optimization by Theodoridis Sergios (22 results)

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Hardcover. Condition: Brand New. 2nd edition. 1071 pages. 9.25x7.50x2.25 inches. In Stock.

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Condition: New. 2020. 2nd Edition. Hardcover. . . . . .

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Condition: New. Presents the physical reasoning, mathematical modeling and algorithmic implementation of each method Updates on the latest trends, including sparsity, convex analysis and optimization, online distributed algorithms, learning in RKH spaces..

- Hardcover
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Language: English
Published by Elsevier Science Publishing Co Inc Mär 2020 2020
- Hardcover
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Buch. Condition: Neu. Neuware - Machine Learning: A Bayesian and Optimization Perspective, 2nd edition, gives a unified perspective on machine learning by covering both pillars of supervised learning, namely regression and classification. The book starts with the basics, including mean square, least squares and maximum likelihoo…d methods, ridge regression, Bayesian decision theory classification, logistic regression, and decision trees. It then progresses to more recent techniques, covering sparse modelling methods, learning in reproducing kernel Hilbert spaces and support vector machines, Bayesian inference with a focus on the EM algorithm and its approximate inference variational versions, Monte Carlo methods, probabilistic graphical models focusing on Bayesian networks, hidden Markov models and particle filtering. Dimensionality reduction and latent variables modelling are also considered in depth.

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Condition: New. 2020. 2nd Edition. Hardcover. . . . . . Books ship from the US and Ireland.

- Hardcover
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Buch. Condition: Neu. Machine Learning | A Bayesian and Optimization Perspective | Sergios Theodoridis | Buch | Einband - fest (Hardcover) | Englisch | 2020 | Elsevier Science Publishing Co Inc | EAN 9780128188033 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter:… preigu.

- Hardcover
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Hardcover. Condition: Brand New. 1st edition. 1062 pages. 9.50x7.75x2.00 inches. In Stock.

- Hardcover
- Print on Demand
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Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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 t…he 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.