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Machine Learning: A Bayesian and Optimization Perspective (Net Developers), by Sergios Theodoridis
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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.
- All major classical techniques: Mean/Least-Squares regression and filtering, Kalman filtering, stochastic approximation and online learning, Bayesian classification, decision trees, logistic regression and boosting methods.
- The 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 variables modeling.
- Case 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 applied.
- MATLAB code for all the main algorithms are available on an accompanying website, enabling the reader to experiment with the code.
- Sales Rank: #308809 in Books
- Published on: 2015-04-10
- Original language: English
- Number of items: 1
- Dimensions: 2.00" h x 7.60" w x 9.30" l, 5.16 pounds
- Binding: Hardcover
- 1062 pages
Review
"Overall, this text is well organized and full of details suitable for advanced graduate and postgraduate courses, as well as scholars…" --Computing Reviews
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 tools
- All major classical techniques: Mean/Least-Squares regression and filtering, Kalman filtering, stochastic approximation and online learning, Bayesian classification, decision trees, logistic regression and boosting methods
- A presentation of the physical reasoning, mathematical modeling and algorithmic implementation of each method
- The 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 modeling
- Case 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 applied
- MATLAB code for all the main algorithms are available on an accompanying website, enabling the reader to experiment with the code
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.
Most helpful customer reviews
12 of 13 people found the following review helpful.
The companion MATLAB source codes are also very useful for better understanding of the methods
By S. P. Chatzis
A much needed and long awaited handbook on modern (Statistical) Machine Learning, including the most current trends of Bayesian Non-parametrics and Deep Learning. Very well-written, it helps the students grasp the main concepts as well as the technical details of the method. The companion MATLAB source codes are also very useful for better understanding of the methods. Being an instructor myself, I would rate it as exceptional.
19 of 23 people found the following review helpful.
Good but may be hard to follow
By Andrei
I'm still looking for a "perfect machine learning theory book": the one which is a pleasure to read and that covers most of concepts you see here and there all the time but always wanted to know how exactly they work: log-linear, maximum likelihood, MAP, least squares and MLS, expectation maximization, stochastic gradient descent, CRFs, mixtures of gaussian, and many others. I would like that the book explain to me why should I use this model or algorithm, why previous one would not be good? And I would like that the author take the time to carefully guide the reader throughout the theory, without leaving him alone with a bunch of matrix equations or integrals like if they were evident.
I'm not a novice in the AI: I have a PhD (not in the theoretical Machine Learning though) and several years of practical experience with the algorithms. But most of the time I use the algorithms and models like blackboxes. My goal, however, is not only be able to use the algorithms and know where and how each algorithm can be used, but really understand the math that drives each them.
Unfortunately, this is not the book that can help me with my goal. In the beginning of each chapter the author really tries to move slowly with a care to details, but very fast the math becomes the only language used on the page. If, in the middle of a section you didn't understand how equation 12 follows from equation 11, your only option is to skip the remainder of the section and this is very frustrating.
As an example, when presenting the "central limit theorem", the author writes "Consider N mutually independent random variables, each following its own distribution with mean values ... and variances ... Define a new random variable **as their sum**: ... Then the mean and variance of the new variable are given by...". Here, or before, no definition of a **sum of two random variables** was presented. But this is very important to understand, because later, for example, in the "Linear Regression" section of Chapter 3, the author writes "If we model the system as a linear combiner, the dependence relationship is written as: " (a linear combination of several random variables follows). What does this mean: a linear combination of ***random variables***? How is this related to the central limit theorem which says that by adding up several random variables, the resulting variable tends to have a gaussian distribution? Author, please don't hurry up, it's a book, not a NIPS paper!
Furthermore, the whole section "3.10.1 LINEAR REGRESSION: THE NONWHITE GAUSSIAN NOISE CASE" on page 84 cannot be directly understood from the text because the author does not explain how the joint log-likelihood function L(theta) for the model of y dependent on theta, x and nu can be constructed. The equation 3.57 gives the final expression for L(theta) but no clues on how to build it if we only have a linear model for y. I spent the whole evening just to understand that to build the joint log-likelihood function one has to transform the y = theta*x + nu into the expression p(y=yn | x=xn, theta, nu) and in order to obtain one such expression for each yn, one has to write p(y=yn | x=xn, theta, nu) = sum_k p(xn*theta=k)p(nu=yn - xn*theta). Then, the joint log likelihood L(theta) can be obtained as ln p(y=y1 | x=x1, theta, nu) + ln p(y=y2 | x=x2, theta, nu) + ... + ln p(y=yn | x=xn, theta, nu).
The internet is full of information on the subject of machine learning. Almost every subject is already explained by multiple sources. The problem with the information of the Web is that it is dispersed and often incomplete. If one decides to write a book on this subject, it has to be complete and self-contained. With this book, unfortunately, one still has to google, decrypt and guess things just too often to call the reading process a pleasure.
11 of 12 people found the following review helpful.
It is a great book!
By Paulo S. R. Diniz
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.
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