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Machine Learning : A Bayesian and Optimization Perspective

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Description

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. The book presents the major machine learning methods as they have been 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, supported by examples and problems, giving an invaluable resource to the student and researcher for understanding and applying machine learning concepts.

The book builds carefully from the basic classical methods to the most recent trends, with chapters written to be as self-contained as possible, making the text suitable for different courses: pattern recognition, statistical/adaptive signal processing, statistical/Bayesian learning, as well as short courses on sparse modeling, deep learning, and probabilistic graphical models.
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Product details

  • Hardback | 1062 pages
  • 200.66 x 241.3 x 53.34mm | 2,199.91g
  • Academic Press Inc
  • San Diego, United States
  • English
  • 0128015225
  • 9780128015223
  • 437,128

Table of contents

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. Stochastic Gradient Descent: The LMS Algorithm and Its Family 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: Part 1 Chapter 16. Probabilistic Graphical Models: Part 2 Chapter 17. Particle Filtering Chapter 18. Neural Networks and Deep Learning Chapter 19. Dimensionality Reduction and Latent Variables Modeling
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Review Text

"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. Djuric, 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.
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Review quote

"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. Djuric, 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 recognitionã for over 35 years, I have alwaysã looked for a rigorousã book that is current and appealing to students with widely varying backgrounds.ã The book on Machine Learning by Sergios Theodoridis has struck the perfect balance in explaining the key (traditional and new)ã 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.
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About Dr. Sergios Theodoridis

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.
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Rating details

8 ratings
4 out of 5 stars
5 38% (3)
4 25% (2)
3 38% (3)
2 0% (0)
1 0% (0)
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