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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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Understanding Machine Learning 2024 pdf epub mobi 電子書下載

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這本書第一部分詳細地介紹瞭 PAC學習理論（計算學習理論和統計學習理論）。與Foundations of Machine Learning 不同之處在於，其在第四章抽齣瞭 Uniform Convergence（依概率一緻收斂）這一特性，這使得對 Agnostic PAC learning 下的泛化界的導齣更加清晰。Uniform Converge...

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市麵上關於machine learning (ML)的書很多，但是個人認為用一本書將ML的方方麵麵全部講清楚是不可能的。粗略的來講，ML的書籍可以分為算法(algorithm)和理論(theorem)兩大類。前一類中，個人認為最近十年比較經典的教材包括Bishop的Pattern Recognition and Machine Learning，...

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出版者:Cambridge University Press

作者:Shai Shalev-Shwartz

出品人:

頁數:424

譯者:

出版時間:2014

價格:USD 48.51

裝幀:Hardcover

isbn號碼:9781107057135

叢書系列:

圖書標籤: 機器學習 MachineLearning 人工智能算法理論計算機科學 ML 計算機