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Machine Learning: A Bayesian and Optimization Perspective (Net Developers)
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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.
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From the Back Cover
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. Key Features Include: An introductory chapter on related mathematical toolsAll major classical techniques: Mean/Least-Squares regression and filtering, Kalman filtering, stochastic approximation and online learning, Bayesian classification, decision trees, logistic regression and boosting methodsA presentation of the physical reasoning, mathematical modeling and algorithmic implementation of each methodThe latest trends: 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 modelingCase studies - protein folding prediction, optical character recognition, text authorship identification, fMRI data analysis, change point detection, hyperspectral image unmixing, target localization, channel equalization and echo cancellation, show how the theory can be appliedMATLAB code for all the main algorithms are available on an accompanying website, enabling the reader to experiment with the code
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Product details
Series: Net Developers
Hardcover: 1062 pages
Publisher: Academic Press; 1 edition (April 10, 2015)
Language: English
ISBN-10: 0128015225
ISBN-13: 978-0128015223
Product Dimensions:
7.8 x 2 x 9.5 inches
Shipping Weight: 5.2 pounds (View shipping rates and policies)
Average Customer Review:
4.7 out of 5 stars
16 customer reviews
Amazon Best Sellers Rank:
#331,529 in Books (See Top 100 in Books)
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.
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.
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.
Easily the best book I have ever bought. It is extremely complete. The prose is so well written that very advanced ideas are explained in a few lines.
It is a great book!!! It covers a wide range of subjects related to machine leaning not found in other books. It is well written and includes detailed reference list in each subject matter. The book should be useful for practitioners, graduate students and academics. I am glad I bought it.
I'm personally not a big fan of the hype around "machine learning" but this book is a good start if you haven't taken any statistics courses.
It's a big book -- and dense. But it covers the ground. Stick with it.
Awesome book! Very detailed and well written!
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