Understanding Machine Learning: From Theory to Algorithms (Hardcover)

Shai Shalev-Shwartz, Shai Ben-David

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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.

商品描述(中文翻譯)

機器學習是計算機科學中增長最快的領域之一,具有廣泛的應用。本教科書的目的是以原則性的方式介紹機器學習及其提供的算法範式。該書提供了機器學習基本思想的廣泛理論解釋,以及將這些原則轉化為實際算法的數學推導。在介紹該領域基礎知識後,本書涵蓋了許多以前教科書未涉及的核心主題。這些主題包括學習的計算複雜性和凸性、穩定性的討論;重要的算法範式,包括隨機梯度下降、神經網絡和結構化輸出學習;以及新興的理論概念,如PAC-Bayes方法和基於壓縮的界限。本書設計為高年級本科生或初級研究生課程,使統計學、計算機科學、數學和工程等專業的學生和非專業讀者能夠理解機器學習的基礎知識和算法。