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Sergios Theodoridis
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Machine Learning: A Bayesian and Optimization Perspective Hardcover – 19 May 2015

by Sergios Theodoridis (Author)
4.6 out of 5 stars 30 ratings
Edition: 1st
"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"--Publisher's website.
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  1. ISBN-10
    0128015225
  2. ISBN-13
    978-0128015223
  3. Edition
    1st
  4. Publisher
    Academic Press
  5. Publication date
    19 May 2015
  6. Language
    English
  7. Dimensions
    19.69 x 5.08 x 24.13 cm
  8. Print length
    1062 pages
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Machine Learning: A Bayesian and Optimization Perspective
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Review

"Overall, this text is well organized and full of details suitable for advanced graduate and postgraduate courses, as well as scholars…" --Computing Reviews "Machine Learning: A Bayesian and Optimization Perspective, Academic Press, 2105, by Sergios Theodoridis is a wonderful book, up to date and rich in detail. It covers a broad selection of topics ranging from classical regression and classification techniques to more recent ones including sparse modeling, convex optimization, Bayesian learning, graphical models and neural networks, giving it a very modern feel and making it highly relevant in the deep learning era. While other widely used machine learning textbooks tend to sacrifice clarity for elegance, Professor Theodoridis provides you with enough detail and insights to understand the "fine print". This makes the book indispensable for the active machine learner." --Prof. Lars Kai Hansen, DTU Compute - Dept. Applied Mathematics and Computer Science Technical University of Denmark "Before the publication of Machine Learning: A Bayesian and Optimization Perspective, I had the opportunity to review one of the chapters in the book (on Monte Carlo methods). I have published actively in this area, and so I was curious how S. Theodoridis would write about it. I was utterly impressed. The chapter presented the material with an optimal mix of theoretical and practical contents in very clear manner and with information for a wide range of readers, from newcomers to more advanced readers. This raised my curiosity to read the rest of the book once it was published. I did it and my original impressions were further reinforced. S. Theodoridis has a great capability to disentangle the important from the unimportant and to make the most of the used space for writing. His text is rich with insights about the addressed topics that are not only helpful for novices but also for seasoned researchers. It goes without saying that my department adopted his book as a textbook in the course on machine learning." --Petar M. Djurić, Ph.D. SUNY Distinguished Professor Department of Electrical and Computer Engineering Stony Brook University, Stony Brook, USA "As someone who has taught graduate courses in pattern recognition for over 35 years, I have always looked for a rigorous book that is current and appealing to students with widely varying backgrounds. The book on Machine Learning by Sergios Theodoridis has struck the perfect balance in explaining the key (traditional and new) concepts in machine learning in a way that can be appreciated by undergraduate and graduate students as well as practicing engineers and scientists. The chapters have been written in a self-consistent way, which will help instructors to assemble different sections of the book to suit the background of students" --Rama Cellappa, Distinguished University Professor, Minta Martin Professor of Engineering, Chair, Department of Electrical and Computer Engineering, University of Maryland, USA

About the Author

Sergios Theodoridis is professor emeritus of machine learning and data processing with the National and Kapodistrian University of Athens, Athens, Greece. He has also served as distinguished professor with the Aalborg University Denmark and as professor with the Chinese University of Hong Kong, Shenzhen, China. In 2023, he received an honorary doctorate degree (D.Sc) from the University of Edinburgh, U.K. He has also received a number of prestigious 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 2017 European Association for Signal Processing (EURASIP) Athanasios Papoulis Award, the 2014 IEEE Signal Processing Society Carl Friedrich Gauss Education Award, and the 2014 EURASIP Meritorious Service Award. He has served as president of EURASIP and vice president for the IEEE Signal Processing Society. He is a Fellow of EURASIP and a Life Fellow of IEEE. He is the coauthor of the book Pattern Recognition, 4th edition, Academic Press, 2009 and of the book Introduction to Pattern Recognition: A MATLAB Approach, Academic Press, 2010.

Product details

  • Publisher ‏ : ‎ Academic Press; 1st edition (19 May 2015)
  • Language ‏ : ‎ English
  • Hardcover ‏ : ‎ 1062 pages
  • ISBN-10 ‏ : ‎ 0128015225
  • ISBN-13 ‏ : ‎ 978-0128015223
  • Dimensions ‏ : ‎ 19.69 x 5.08 x 24.13 cm
  • Customer Reviews:
    4.6 out of 5 stars 30 ratings

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Sergios Theodoridis

Sergios Theodoridis

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Contact

e: stheodor [at] di.uoa.gr

ph: +30 2107275328

Department of Informatics and Telecommunications

National and Kapodistrian University of Athens,

Panepistimiopolis Ilissia,

157 84 Athens, GREECE

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About Sergios Theodoridis

Sergios Theodoridis is currently Professor of Signal Processing and Machine Learning in the Department of Informatics and Telecommunications of the National and Kapodistrian University of Athens, Greece.

His research interests lie in the areas of Adaptive Algorithms, Distributed and Sparsity-Aware Learning, Machine Learning and Pattern Recognition, Signal Processing for Audio Processing and Retrieval.

He is the author of the book "Machine Learning: A Bayesian and Optimization Perspective", Academic Press, 2015, the co-author of the best selling book "Pattern Recognition", Academic Press, 4th ed. 2009, the co-author of the book "Introduction to Pattern Recognition: A MATLAB Approach", Academic Press, 2010, and the co-editor of the book "Efficient Algorithms for Signal Processing and System Identification", Prentice Hall 1993. He has also co-authored three books in Greek, two of them for the Greek Open University.

He is the recipient of the 2014 IEEE Signal Processing Society Education Award and the 2014 EURASIP Meritorious Service Award.

He is the co-author of seven papers that have received Best Paper Awards including the 2014 IEEE Signal Processing Magazine best paper award and the 2009 IEEE Computational Intelligence Society Transactions on Neural Networks Outstanding Paper Award.

He serves as Editor-in-Chief for the IEEE Transactions on Signal Processing (2015-2017). He is Editor-in-Chief for the Signal Processing Book Series, Academic Press and co-Editor in Chief (with Rama Chellapa) for the E-Reference Signal Processing, Elsevier.

He has served as a Distinguished Lecturer for the IEEE Signal Processing and IEEE Circuits and Systems Societies. He was Otto Monstead Guest Professor, Technical University of Denmark, 2012, and holder of the Excellence Chair, Dept. of Signal Processing and Communications, University Carlos III, Madrid, Spain, 2011.

He was the general chairman of EUSIPCO-98, the Technical Program co-chair for ISCAS-2006 and ISCAS-2013, co-chairman and co-founder of CIP-2008, co-chairman of CIP-2010 and Technical Program co-chair of ISCCSP-2014. He has served as President of the European Association for Signal Processing (EURASIP), as a member of the Board of Governors for the IEEE CAS Society, as a member of the Board of Governors (Member-at-Large) of the IEEE SP Society and as a Chair of the Signal Processing Theory and Methods (SPTM) technical committee of IEEE SPS.

He has served as a member of the Greek National Council for Research and Technology and he was Chairman of the SP advisory committee for the Edinburgh Research Partnership (ERP). He has served as vice chairman of the Greek Pedagogical Institute and he was for four years member of the Board of Directors of COSMOTE (the Greek mobile phone operating company). He is Fellow of IET, a Corresponding Fellow of the Royal Society of Edinburgh (RSE), a Fellow of EURASIP and a Fellow of IEEE.

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  • Daniel
    5.0 out of 5 stars A very complete, and high level book
    Reviewed in Mexico on 6 February 2020
    Verified Purchase
    It's an excellent book, for those who wants to go deeper in the Bayesian Machine Learning area... Right now, there are more articles related to Bayesian Deep Learning ( Variational Dropout, Local Reparametrization... Etc ). So I believe that the next edition that is coming up will be more complete compared to this one, and of course I'll buy it.
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  • John Wick
    5.0 out of 5 stars Five Stars
    Reviewed in the United Kingdom on 10 March 2016
    Verified Purchase
    Nice book.
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  • Amazon Customer
    5.0 out of 5 stars Great book for professionals
    Reviewed in the United States on 23 February 2016
    Verified Purchase
    The author put the machine learning and parameter estimation in systemic and unifying framework. This is a great book for professional engineers who want to know the whole picture of the machine learning, the classic and new advanced ones. It answers a lot of my questions that I cannot get from other books. I really enjoy reading it.

    This book is focused more on the application level, not verbose on the theory. It is exact what professional engineer needs.
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  • Andres Mendez
    5.0 out of 5 stars Great Book!!! A Machine Learning must....
    Reviewed in the United States on 25 November 2015
    Verified Purchase
    As a practitioner of Machine Learning, I am so amassed about Theodoridis' abilities to deliver fresh and precise content about the so fast evolving field of Machine Learning. This book is a must on the shelves of anybody calling herself or himself a data scientist. Sections like the ones about sparse data, Learning Kernels, Bayesian Non-Parametric Models, Probabilistic Graphical Models and Deep Learning make of this book a forefront reference on a field that is transforming the world.
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  • Stergios Papadimitriou
    5.0 out of 5 stars The Machine Learning Bible!
    Reviewed in the United States on 18 April 2018
    Verified Purchase
    An excellent book: Each chapter is explained very well and it is readable and understandable.
    It covers a lot of modern advances, e.g. deep learning.
    It is the best machine learning book that I currently own.
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