Foundations of Machine Learning (Hardcover) (機器學習基礎)
Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar
- 出版商: MIT
- 出版日期: 2012-08-17
- 售價: $3,030
- 貴賓價: 9.5 折 $2,879
- 語言: 英文
- 頁數: 432
- 裝訂: Hardcover
- ISBN: 026201825X
- ISBN-13: 9780262018258
-
相關分類:
Machine Learning
-
相關翻譯:
機器學習基礎 (簡中版)
-
其他版本:
Foundations of Machine Learning, 2/e (Hardcover)
買這商品的人也買了...
-
$1,007Machine Learning and Data Mining: Methods and Applications (Hardcover)
-
$1,078Machine Learning (IE-Paperback)
-
$880$695 -
$620$490 -
$2,630$2,499 -
$1,780$1,744 -
$4,910$4,665 -
$650$553 -
$550$435 -
$560$476 -
$4,550$4,323 -
$880$748 -
$1,200$1,140 -
$301程序員度量-改善軟件團隊的分析學 (Codermetrics: Analytics for Improving Software Teams)
-
$454領域特定語言 (Domain-Specific Languages)
-
$1,020$969 -
$580$522 -
$352Hadoop 技術內幕-深入解析 MapReduce 架構設計與實現原理
-
$2,600$2,470 -
$250鳳凰計畫:一個 IT計畫的傳奇故事 (The Phoenix Project : A Novel about IT, DevOps, and Helping your business win)(沙盤特別版)
-
$890$694 -
$590$460 -
$390$332 -
$450$356 -
$500$390
相關主題
商品描述
This graduate-level textbook introduces fundamental concepts and methods in machine learning. It describes several important modern algorithms, provides the theoretical underpinnings of these algorithms, and illustrates key aspects for their application. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning fills the need for a general textbook that also offers theoretical details and an emphasis on proofs. Certain topics that are often treated with insufficient attention are discussed in more detail here; for example, entire chapters are devoted to regression, multi-class classification, and ranking. The first three chapters lay the theoretical foundation for what follows, but each remaining chapter is mostly self-contained. The appendix offers a concise probability review, a short introduction to convex optimization, tools for concentration bounds, and several basic properties of matrices and norms used in the book.
The book is intended for graduate students and researchers in machine learning, statistics, and related areas; it can be used either as a textbook or as a reference text for a research seminar.
商品描述(中文翻譯)
這本研究生級的教科書介紹了機器學習的基本概念和方法。它描述了幾個重要的現代演算法,提供了這些演算法的理論基礎,並說明了其應用的關鍵方面。作者旨在呈現新穎的理論工具和概念,同時對於相對進階的主題也給予簡潔的證明。《Foundations of Machine Learning》滿足了對於一本通用教科書的需求,該書同時提供理論細節並強調證明。某些主題在其他地方常常受到不足的重視,在此進行了更詳細的討論;例如,整個章節專門討論回歸、多類別分類和排序。前三章為後續內容奠定了理論基礎,但其餘章節大多是自成一體的。附錄提供了簡潔的機率回顧、對凸優化的簡短介紹、集中界限的工具,以及書中使用的幾個基本矩陣和範數的性質。
本書適合機器學習、統計學及相關領域的研究生和研究人員使用;它可以作為教科書或研究研討會的參考書。