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Machine Learning: A Bayesian and Optimization Perspective, 2/Ed > 인공지능

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Machine Learning: A Bayesian and Optimization Perspective, 2/Ed
출간예정도서
판매가격 110,000원
저자 Sergios Theodoridis
도서종류 외국도서
출판사 Academic Press
발행언어 영어
발행일 2020
페이지수 1160
ISBN 9780128188033
배송비결제 주문시 결제
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    Description

    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

    Readership

    Researchers and graduate students in electronic engineering, mechanical engineering, computer science, applied mathematics, statistics, medical imaging

     

    Table of Contents

    1. Introduction

    2. Probability and stochastic Processes

    3. Learning in parametric Modeling: Basic Concepts and Directions

    4. Mean-Square Error Linear Estimation

    5. Stochastic Gradient Descent: the LMS Algorithm and its Family

    6. The Least-Squares Family

    7. Classification: A Tour of the Classics

    8. Parameter Learning: A Convex Analytic Path

    9. Sparsity-Aware Learning: Concepts and Theoretical Foundations

    10. Sparsity-Aware Learning: Algorithms and Applications

    11. Learning in Reproducing Kernel Hilbert Spaces

    12. Bayesian Learning: Inference and the EM Algorithm

    13. Bayesian Learning: Approximate Inference and nonparametric Models

    14. Montel Carlo Methods

    15. Probabilistic Graphical Models: Part 1

    16. Probabilistic Graphical Models: Part 2

    17. Particle Filtering

    18. Neural Networks and Deep Learning

    19. Dimensionality Reduction and Latent Variables Modeling

     

    About the Author

     

    Sergios Theodoridis is Professor of Signal Processing and Machine Learning in the Department of Informatics and Telecommunications of the University of Athens.

     

    He is the co-author of the bestselling book, Pattern Recognition, and the co-author of Introduction to Pattern Recognition: A MATLAB Approach.

     

    He serves as Editor-in-Chief for the IEEE Transactions on Signal Processing, and he is the co-Editor in Chief with Rama Chellapa for the Academic

     

    Press Library in Signal Processing.

     

    He has received a number of awards including the 2014 IEEE Signal Processing Magazine Best Paper Award, the 2009 IEEE Computational Intelligence Society Transactions on Neural Networks Outstanding Paper Award, the 2014 IEEE Signal Processing Society Education Award, the EURASIP 2014 Meritorious Service Award, and he has served as a Distinguished Lecturer for the IEEE Signal Processing Society and the IEEE Circuits and Systems Society. He is a Fellow of EURASIP and a Fellow of IEEE.

     

    Affiliations and Expertise

    Department of Informatics and Telecommunications, University of Athens, Greece

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