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Machine Learning
A Bayesian
and Optimization
Perspective
Machine Learning
A Bayesian
and Optimization
Perspective
Sergios Theodoridis
AMSTERDAM • BOSTON • HEIDELBERG • LONDON
NEW YORK • OXFORD • PARIS • SAN DIEGO
SAN FRANCISCO • SINGAPORE • SYDNEY • TOKYO
Academic Press is an imprint of Elsevier
Academic Press is an imprint of Elsevier
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Copyright © 2015 Elsevier Ltd. All rights reserved.
No part of this publication may be reproduced or transmitted in any form or by any means, electronic or
mechanical, including photocopying, recording, or any information storage and retrieval system, without
permission in writing from the publisher. Details on how to seek permission, further information about the
Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center
and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions.
This book and the individual contributions contained in it are protected under copyright by the Publisher
(other than as may be noted herein).
Notices
Knowledge and best practice in this eld are constantly changing. As new research and experience broaden our
understanding, changes in research methods, professional practices, or medical treatment may become necessary.
Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using
any information, methods, compounds, or experiments described herein. In using such information or methods
they should be mindful of their own safety and the safety of others, including parties for whom they have a
professional responsibility.
To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability
for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise,
or from any use or operation of any methods, products, instructions, or ideas contained in the material herein.
British Library Cataloguing-in-Publication Data
A catalogue record for this book is available from the British Library
Library of Congress Cataloging-in-Publication Data
A catalog record for this book is available from the Library of Congress
ISBN: 978-0-12-801522-3
For information on all Academic Press publications
visit our website at http://store.elsevier.com/
Publisher: Jonathan Simpson
Acquisition Editor: Tim Pitts
Editorial Project Manager: Charlie Kent
Production Project Manager: Susan Li
Designer: Greg Harris
Typeset by SPi Global, India
Printed and bound in The United States
1516171819 10987654321
Preface
Machine Learning is a name that is gaining popularity as an umbrella for methods that have been studied
and developed for many decades in different scientic communities and under different names, such as
Statistical Learning, Statistical Signal Processing, Pattern Recognition, Adaptive Signal Processing,
Image Processing and Analysis, System Identication and Control, Data Mining and Information
Retrieval, Computer Vision, and Computational Learning. The name “Machine Learning” indicates
what all these disciplines have in common, that is, to learn from data,andthenmake predictions.
What one tries to learn from data is their underlying structure and regularities, via the development of
a model, which can then be used to provide predictions.
To this end, a number of diverse approaches have been developed, ranging from optimization of cost
functions, whose goal is to optimize the deviation between what one observes from data and what the
model predicts, to probabilistic models that attempt to model the statistical properties of the observed
data.
The goal of this book is to approach the machine learning discipline in a unifying context,
by presenting the major paths and approaches that have been followed over the years, without giving
preference to a specic one. It is the author’s belief that all of them are valuable to the newcomer who
wants to learn the secrets of this topic, from the applications as well as from the pedagogic point of
view. As the title of the book indicates, the emphasis is on the processing and analysis front of machine
learning and not on topics concerning the theory of learning itself and related performance bounds.
In other words, the focus is on methods and algorithms closer to the application level.
The book is the outgrowth of more than three decades of the author’s experience on research and
teaching various related courses. The book is written in such a way that individual (or pairs of) chapters
are as self-contained as possible. So, one can select and combine chapters according to the focus he/she
wants to give to the course he/she teaches, or to the topics he/she wants to grasp in a rst reading. Some
guidelines on how one can use the book for different courses are provided in the introductory chapter.
Each chapter grows by starting from the basics and evolving to embrace the more recent advances.
Some of the topics had to be split into two chapters, such as sparsity-aware learning, Bayesian learning,
probabilistic graphical models, and Monte Carlo methods. The book addresses the needs of advanced
graduate, postgraduate, and research students as well as of practicing scientists and engineers whose
interests lie beyond black-box solutions. Also, the book can serve the needs of short courses on specic
topics, e.g., sparse modeling, Bayesian learning, probabilistic graphical models, neural networks and
deep learning.
Most of the chapters include Matlab exercises, and the related code is available from the book’s
website. The solutions manual as well as PowerPoint lectures are also available from the book’s website.
xvii
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