Description: Understanding Machine Learning : From Theory to Algorithms, Hardcover by Shalev-shwartz, Shai; Ben-David, Shai, ISBN 1107057132, ISBN-13 9781107057135, Used Good Condition, Free shipping in the US "Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. Th provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics of the field, th covers a wide array of central topics that have not been addressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering"--
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Book Title: Understanding Machine Learning : From Theory to Algorithms
Number of Pages: 410 Pages
Publication Name: Understanding Machine Learning : from Theory to Algorithms
Language: English
Publisher: Cambridge University Press
Publication Year: 2014
Item Height: 1.1 in
Subject: Algebra / General, Computer Vision & Pattern Recognition
Type: Textbook
Item Weight: 32.2 Oz
Author: Shai Ben-David, Shai Shalev-Shwartz
Subject Area: Mathematics, Computers
Item Length: 10.2 in
Item Width: 7.2 in
Format: Hardcover
