Machine Learning: A Bayesian and Optimization Perspective (Net Developers)

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9780128015223: Machine Learning: A Bayesian and Optimization Perspective (Net Developers)
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"Overall, this text is well organized and full of details suitable for advanced graduate and postgraduate courses, as well as scholars…" --Computing Reviews

"Machine Learning: A Bayesian and Optimization Perspective", Academic Press, 2105, by Sergios Theodoridis is a wonderful book, up to date and rich in detail. It covers a broad selection of topics ranging from classical regression and classification techniques to more recent ones including sparse modeling, convex optimization, Bayesian learning, graphical models and neural networks, giving it a very modern feel and making it highly relevant in the deep learning era. While other widely used machine learning textbooks tend to sacrifice clarity for elegance, Professor Theodoridis provides you with enough detail and insights to understand the "fine print". This makes the book indispensable for the active machine learner." --Prof. Lars Kai Hansen, DTU Compute - Dept. Applied Mathematics and Computer Science Technical University of Denmark

Reseña del editor:

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.

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Theodoridis, Sergios
Editorial: Academic Press (2015)
ISBN 10: 0128015225 ISBN 13: 9780128015223
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Descripción Elsevier Science Publishing Co Inc, United States, 2015. Hardback. Estado de conservación: New. 241 x 201 mm. Language: English . Brand New Book. 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. Nº de ref. de la librería AA59780128015223

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Dr. Sergios Theodoridis
Editorial: Elsevier Science Publishing Co Inc, United States (2015)
ISBN 10: 0128015225 ISBN 13: 9780128015223
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Descripción Elsevier Science Publishing Co Inc, United States, 2015. Hardback. Estado de conservación: New. 241 x 201 mm. Language: English . Brand New Book. 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. Nº de ref. de la librería AA59780128015223

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Dr. Sergios Theodoridis
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ISBN 10: 0128015225 ISBN 13: 9780128015223
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Descripción Elsevier Science Publishing Co Inc 2015-05-08, San Diego, 2015. hardback. Estado de conservación: New. Nº de ref. de la librería 9780128015223

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ISBN 10: 0128015225 ISBN 13: 9780128015223
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Descripción Academic Press Inc 2015-03-27, 2015. Estado de conservación: New. Brand new book, sourced directly from publisher. Dispatch time is 24-48 hours from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely. Nº de ref. de la librería NU-LBR-01596333

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Descripción Hardback. Estado de conservación: New. Not Signed; 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. book. Nº de ref. de la librería ria9780128015223_rkm

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Descripción U.S.A.: Academic Press, 2015. Hardcover. Estado de conservación: New. 1st Edition. ***Absolutely Brand New Textbook***. We sell only brand new textbook. ~Perfect US Edition Textbook~. All orders ship out same or next business day. We do not ship to APO/FPO address. We usually ship the textbook via DHL. FedEx or UPS express; your textbook will arrive within 3-5 Bus days to US and Canada via DHL express, 4-6 days to EU and Asia countries. Ship from Multiple Locations, including LA/US, EU, ASIA. We may ship the books from Multiple Locations for inventory purpose. Nº de ref. de la librería 20f610128015225

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