Understanding Machine Learning: From Theory to Algorithms
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Understanding Machine Learning: From Theory to Algorithms
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Amazon Business : For business-only pricing, quantity discounts and FREE Shipping. Register a free business account Hardcover: 410 pages Publisher: Cambridge University Press; 1 edition (May 19, 2014) Language: English ISBN-10: 1107057132 ISBN-13: 978-1107057135 Product Dimensions: 7.2 x 1.1 x 10.2 inches Shipping Weight: 2 pounds (View shipping rates and policies)
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Book Description
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. 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 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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