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Understanding Machine Learning: From Theory to Algorithms

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Download Understanding Machine Learning: From Theory to Algorithms written by Shai Shalev-Shwartz, Shai Ben David in PDF format. This book is under the category Computers and bearing the isbn/isbn13 number 1107057132; 1107512824/9781107057135/ 9781107512825. You may reffer the table below for additional details of the book. We do NOT provide access codes, we provide eBooks ONLY. Instant access will be granted as soon as you complete the payment.

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Specifications

book-author

Shai Shalev-Shwartz, Shai Ben David

publisher

Cambridge University Press; 1st edition

file-type

PDF

pages

415 pages

language

English

asin

B00J8LQU8I

isbn10

1107057132; 1107512824

isbn13

9781107057135/ 9781107512825


Book Description

Machine learning is one of the fastest growing areas of computer science; with far-reaching applications. The aim of this digital textbook Understanding Machine Learning: From Theory to Algorithms (PDF) is to introduce machine learning; and the algorithmic paradigms it offers; in a principled way. The ebook provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics; the ebook covers a wide array of central topics unaddressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of stability and convexity; important algorithmic paradigms including neural networks; stochastic gradient descent; and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for beginning graduates or advanced undergraduates; the textbook makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in computer science; mathematics; statistics; and engineering.

Reviews

This elegant ebook covers both rigorous theory and practical methods of machine learning. This makes it a rather unique resource; ideal for all those who want to understand how to find structure in data.” – Professor Bernhard Schölkopf; Max Planck Institute for Intelligent Systems

This textbook gives a clear and broadly accessible view of the most important ideas in the area of full information decision problems. Written by 2 key contributors to the theoretical foundations in this area; it covers the range from algorithms to theoretical foundations; at a level appropriate for an advanced undergraduate course.” – Dr. Peter L. Bartlett; University of California; Berkeley

This is a timely textbook on the mathematical foundations of machine learning; providing a treatment that is both broad and deep; not only rigorous but also with insight and intuition. It presents a wide range of classic; fundamental algorithmic and analysis techniques as well as cutting-edge research directions. This is a great ebook for anyone interested in the computational and mathematical underpinnings of this important and fascinating field.” – Avrim Blum; Carnegie Mellon University

 

book-author

Shai Shalev-Shwartz, Shai Ben David

publisher

Cambridge University Press; 1st edition

file-type

PDF

pages

415 pages

language

English

asin

B00J8LQU8I

isbn10

1107057132; 1107512824

isbn13

9781107057135/ 9781107512825

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