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 Title Understanding Machine Learning: From Theory to Algorithms
 Author(s) Shai ShalevShwartz and Shai BenDavid
 Publisher: Cambridge University Press; 1 edition (May 19, 2014)
 Hardcover/Paperback 409 pages
 eBook PDF (449 pages, 2.5 MB)
 Language: English
 ISBN10: 1107057132
 ISBN13: 9781107057135
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Book Description
Machine learning makes use of computer programs to discover meaningful patters in complex data. It is one of the fastest growing areas of computer science, with farreaching applications. This book explains the principles behind the automated learning approach and the considerations underlying its usage. The authors explain the "hows" and "whys" of the most important machinelearning algorithms, as well as their inherent strengths and weaknesses, making the field accessible to students and practitioners in computer science, statistics, and engineering.
The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book 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, the book 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 PACBayes approach and compressionbased bounds. Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and nonexpert readers in statistics, computer science, mathematics, and engineering.
About the Authors N/A
 Machine Learning
 Neural Networks and Deep Learning
 Artificial Intelligence
 Data Analysis and Data Mining
 Statistics, R Language and SAS Programming
 Operations Research (OR), Linear Programming, Optimization, and Approximation
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