Understanding Machine Learning: From Theory to Algorithms

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.

$19.99

SKU: dfd7468ac613 Category: Tags: ,

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

 

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