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Book details
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Author : Sergios Theodoridis
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Pages : 1062 pages
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Publisher : Academic Press 2015-04-10
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Language : English
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ISBN-10 : 0128015225
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ISBN-13 : 9780128015223
Book Synopsis
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.