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Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz

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 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 book 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 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 advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics and engineering.

Price: 92.4 AUD

Location: Hillsdale, NSW

End Time: 2024-11-11T02:10:45.000Z

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Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-ShwartzUnderstanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz

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EAN: 9781107057135

UPC: 9781107057135

ISBN: 9781107057135

MPN: N/A

Item Length: 25.7 cm

Number of Pages: 410 Pages

Language: English

Publication Name: Understanding Machine Learning: from Theory to Algorithms

Publisher: Cambridge University Press

Publication Year: 2014

Subject: Computer Science

Item Height: 260 mm

Item Weight: 910 g

Type: Textbook

Author: Shai Ben-David, Shai Shalev-Shwartz

Item Width: 183 mm

Format: Hardcover

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