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

ISBN-13: 9780128015223

ISBN-10: 0128015225

Author: Sergios Theodoridis

Edition: 1

Publication date:
2015
Publisher:
Academic Press
Format:
Hardcover 1062 pages
Category:
Statistics, Computers
Rating:
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Summary

Acknowledged author Sergios Theodoridis wrote Machine Learning: A Bayesian and Optimization Perspective (Net Developers) comprising 1062 pages back in 2015. Textbook and etextbook are published under ISBN 0128015225 and 9780128015223. Since then Machine Learning: A Bayesian and Optimization Perspective (Net Developers) textbook received total rating of 3.5 stars and was available to sell back to BooksRun online for the top buyback price of $16.28 or rent at the marketplace.


Description

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.