Understanding Machine Learning: From Theory to Algorithms (Hardcover)
Shai Shalev-Shwartz, Shai Ben-David
- 出版商: Cambridge
- 出版日期: 2014-07-17
- 售價: $2,280
- 貴賓價: 9.5 折 $2,166
- 語言: 英文
- 頁數: 410
- 裝訂: Hardcover
- ISBN: 1107057132
- ISBN-13: 9781107057135
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相關分類:
Machine Learning 機器學習 、Algorithms-data-structures 資料結構與演算法
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相關翻譯:
深入理解機器學習:從原理到算法 (Understanding Machine Learning : From Theory to Algorithms) (簡中版)
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商品描述
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.