Download Machine Learning: A Bayesian and Optimization Perspective (Net Developers)
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Machine Learning: A Bayesian and Optimization Perspective (Net Developers)
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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.
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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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