Machine Learning: A Bayesian and Optimization Perspective (.Net Developers Series) - Hardcover

Sergios Theodoridis Dr.

Hardcover

ISBN 10: 0128015225 ISBN 13: 9780128015223

Publisher: Academic Press, 2015

Synopsis

[ Machine Learning: A Bayesian and Optimization Perspective By ( Author ) Apr-2015 Hardcover

About this title

Review

Gain an in-depth understanding of all the main machine learning methods, including sparse modeling, online and convex optimization, Bayesian inference, graphical models, deep networks, learning in RKH spaces, dimensionality reduction and dictionary learning

From the Back Cover

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.

Key Features Include:

• An introductory chapter on related mathematical tools
• 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
• A presentation of the physical reasoning, mathematical modeling and algorithmic implementation of each method
• 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 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

Edition details

• Publisher: Academic Press
• Publication date: 2015
• ISBN 10: 0128015225
• ISBN 13: 9780128015223
• Binding: Hardcover
• Language: English
• Number of pages: 1062