Machine Learning:A Bayesian and Optimization Perspective 2nd Edition

By 作者:Sergios Theodoridis

Pages:1160 pages

Publisher Finelybook 出版社:Academic Press; 2 edition (April 3, 2020)

Language:English

ISBN-10:0128188030

ISBN-13:9780128188033

**The 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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