Mathematical Foundations for Data Analysis
暫譯: 數據分析的數學基礎

Phillips, Jeff M.

Mathematical Foundations for Data Analysis
  • 出版商: Springer
  • 出版日期: 2021-03-30
  • 售價: $2,490
  • 貴賓價: 9.5 折 $2,366
  • 語言: 英文
  • 頁數: 287
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 3030623408
  • ISBN-13: 9783030623401
  • 相關分類: Data Science

    • 海外代購書籍(需單獨結帳)

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商品描述

This textbook, suitable for an early undergraduate up to a graduate course, provides an overview of many basic principles and techniques needed for modern data analysis. In particular, this book was designed and written as preparation for students planning to take rigorous Machine Learning and Data Mining courses. It introduces key conceptual tools necessary for data analysis, including concentration of measure and PAC bounds, cross validation, gradient descent, and principal component analysis. It also surveys basic techniques in supervised (regression and classification) and unsupervised learning (dimensionality reduction and clustering) through an accessible, simplified presentation. Students are recommended to have some background in calculus, probability, and linear algebra. Some familiarity with programming and algorithms is useful to understand advanced topics on computational techniques.

商品描述(中文翻譯)

這本教科書適合早期本科生到研究生課程，提供了現代數據分析所需的許多基本原則和技術的概述。特別是，這本書是為計劃參加嚴謹的機器學習（Machine Learning）和數據挖掘（Data Mining）課程的學生而設計和撰寫的。它介紹了數據分析所需的關鍵概念工具，包括測量集中（concentration of measure）和PAC界限（PAC bounds）、交叉驗證（cross validation）、梯度下降（gradient descent）和主成分分析（principal component analysis）。它還通過易於理解的簡化呈現，調查了監督式學習（supervised learning，包含回歸和分類）和非監督式學習（unsupervised learning，包含降維和聚類）的基本技術。建議學生具備一些微積分、概率和線性代數的背景知識。對編程和算法有一定的熟悉度將有助於理解計算技術的高級主題。

作者簡介

Jeff M. Phillips is an Associate Professor in the School of Computing within the University of Utah. He directs the Utah Center for Data Science as well as the Data Science curriculum within the School of Computing. His research is on algorithms for big data analytics, a domain with spans machine learning, computational geometry, data mining, algorithms, and databases, and his work regularly appears in top venues in each of these fields. He focuses on a geometric interpretation of problems, striving for simple, geometric, and intuitive techniques with provable guarantees and solve important challenges in data science. His research is supported by numerous NSF awards including an NSF Career Award.

 

作者簡介(中文翻譯)

Jeff M. Phillips 是猶他大學計算學院的副教授。他負責猶他數據科學中心以及計算學院的數據科學課程。他的研究專注於大數據分析的演算法，這個領域涵蓋了機器學習、計算幾何、數據挖掘、演算法和資料庫，他的研究成果經常出現在這些領域的頂尖期刊上。他專注於問題的幾何解釋，努力尋求簡單、幾何且直觀的技術，並提供可證明的保證，以解決數據科學中的重要挑戰。他的研究得到了多項國家科學基金會（NSF）獎項的支持，包括 NSF 職業獎。

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