Machine Learning A Bayesian and Optimization Perspective 2nd Edition


Machine Learning:A Bayesian and Optimization Perspective 2nd Edition
by:Sergios Theodoridis
pages 页数:1160 pages
Publisher Finelybook 出版社:Academic Press; 2 edition (April 3, 2020)
Language 语言:English
ISBN-10 书号:0128188030
ISBN-13 书号:9780128188033

Book Description
About the book
Machine Learning:A Bayesian and Optimization Perspective, Second Edition, gives a unifying perspective on machine learning by covering both probabilistic and deterministic approaches based on optimization techniques combined with the Bayesian inference approach. The book builds from the basic classical methods to recent trends, making it suitable for different courses, including pattern recognition, statistical/adaptive signal processing, and statistical/Bayesian learning, as well as short courses on sparse modeling, deep learning, and probabilistic graphical models. In addition, sections cover major machine learning methods 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 and supported by examples and problems, giving an invaluable resource to both the student and researcher for understanding and applying machine learning concepts.

This updated edition includes many more simple examples on basic theory, complete rewrites of the chapter on Neural Networks and Deep Learning, and expanded treatment of Bayesian learning, including Nonparametric Bayesian Learning.

Key Features
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, Bayesian inference, graphical and hidden Markov models, particle filtering, deep learning, dictionary learning and latent variables modeling
Provides case studies on a variety of topics, including protein folding prediction, optical character recognition, text authorship identification, fMRI data analysis, change point detection, hyperspectral image unmixing, target localization, and more
Contents
About the Author
Preface
Acknowledgments
Notation
Chapter 1-Introduction
Chapter 2-Probability and Stochastic Processes
Chapter 3-Learning in Parametric Modeling Basic Concepts and Directions
Chapter 4-Mean-Square Error Linear Estimation
Chapter 5-Online Learning the Stochastic Gradient Descent Family of Algorithms
Chapter 6-The Least-Squares Family
Chapter 7-Classification a Tour of the Classics
Chapter 8-Parameter Learning a Convex Analytic Path
Chapter 9-Sparsity-Aware Learning Concepts and Theoretical Foundations
Chapter 10-Sparsity-Aware Learning Algorithms and Applications
Chapter 11-Learning in Reproducing Kernel Hilbert Spaces
Chapter 12-Bayesian Learning Inference and the EM Algorithm
Chapter 13-Bayesian Learning Approximate Inference and Nonparametric Models
Chapter 14-Monte Carlo Methods
Chapter 15-Probabilistic Graphical Models PartI
Chapter 16-Probabilistic Graphical Models Part ll
Chapter 17-Particle Filtering
Chapter 18-Neural Networks and Deep Learning
Chapter 19-Dimensionality Reduction and Latent Variable Modeling
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