Fundamentals of Robust Machine Learning: Handling Outliers and Anomalies in Data Science
暫譯: 穩健機器學習基礎：處理資料科學中的異常值與異常情況
Saleh, Resve A., Majzoub, Sohaib, Saleh, A. K. MD Ehsanes
- 出版商: Wiley
- 出版日期: 2025-05-13
- 售價: $3,690
- 貴賓價: 9.5 折 $3,506
- 語言: 英文
- 頁數: 416
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 1394294379
- ISBN-13: 9781394294374
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相關分類:
Machine Learning、Data Science
尚未上市，無法訂購
相關主題
商品描述
An essential guide for tackling outliers and anomalies in machine learning and data science.
In recent years, machine learning (ML) has transformed virtually every area of research and technology, becoming one of the key tools for data scientists. Robust machine learning is a new approach to handling outliers in datasets, which is an often-overlooked aspect of data science. Ignoring outliers can lead to bad business decisions, wrong medical diagnoses, reaching the wrong conclusions or incorrectly assessing feature importance, just to name a few.
Fundamentals of Robust Machine Learning offers a thorough but accessible overview of this subject by focusing on the how to properly handle outliers and anomalies in datasets. There are two main approaches described in the book: using outlier-tolerant ML tools, or removing outliers before using standard tools. Balancing theoretical foundations with practical Python code, it provides all the necessary skills to enhance the accuracy, stability and reliability of ML models.
Fundamentals of Robust Machine Learning readers will also find:
- A blend of robust statistics and machine learning principles
- Detailed discussion of a wide range of robust machine learning methodologies, from robust clustering, regression and classification, to neural networks and anomaly detection
- Python code with immediate application to data science problems
Fundamentals of Robust Machine Learning is ideal for undergraduate or graduate students in data science, machine learning, and related fields, as well as for professionals in the field looking to enhance their understanding of building models in the presence of outliers.
商品描述(中文翻譯)
處理機器學習和數據科學中的異常值和異常現象的必備指南。
近年來，機器學習（ML）幾乎改變了每一個研究和技術領域，成為數據科學家的關鍵工具之一。穩健的機器學習是一種處理數據集中的異常值的新方法，這是數據科學中常被忽視的方面。忽略異常值可能導致糟糕的商業決策、錯誤的醫療診斷、得出錯誤的結論或錯誤評估特徵重要性，這些只是其中幾個例子。
穩健機器學習基礎 提供了這一主題的全面但易於理解的概述，重點在於如何正確處理數據集中的異常值和異常現象。書中描述了兩種主要方法：使用對異常值具有容忍度的機器學習工具，或在使用標準工具之前移除異常值。它在理論基礎與實用的 Python 代碼之間取得平衡，提供了增強機器學習模型準確性、穩定性和可靠性所需的所有技能。
穩健機器學習基礎 的讀者還將發現：
- 穩健統計學和機器學習原則的結合
- 對各種穩健機器學習方法的詳細討論，從穩健聚類、回歸和分類，到神經網絡和異常檢測
- 可立即應用於數據科學問題的 Python 代碼
穩健機器學習基礎 非常適合數據科學、機器學習及相關領域的本科生或研究生，以及希望增強在異常值存在下構建模型理解的專業人士。
作者簡介
Resve Saleh, (PhD, UC Berkeley) is a Professor Emeritus at the University of British Columbia. He worked for a decade as a professor at the University of Illinois and as a visiting professor at Stanford University. He was Founder and Chairman of Simplex Solutions, Inc., which went public in 2001. He is an IEEE Fellow and Fellow of the Canadian Academy of Engineering.
Sohaib Majzoub, (PhD, University of British Columbia) is an Associate Professor at the University of Sharjah, UAE. He also taught at the American University in Dubai, UAE and at King Saud University, KSA, and a visiting professor at Delft Technical University in The Netherlands. He is a Senior Member of the IEEE.
A. K. MD. Ehsanes Saleh, (PhD, University of Western Ontario) is a Professor Emeritus and Distinguished Professor in the School of Mathematics and Statistics, Carleton University, Ottawa, Canada. He also taught as Simon Fraser University, the University of Toronto, and Stanford University. He is a Fellow of IMS, ASA and an Honorary Member of SSC, Canada.
作者簡介(中文翻譯)
Resve Saleh（博士，加州大學伯克利分校）是英屬哥倫比亞大學的名譽教授。他在伊利諾伊大學擔任教授十年，並曾擔任史丹佛大學的訪問教授。他是Simplex Solutions, Inc.的創辦人及董事長，該公司於2001年上市。他是IEEE Fellow及加拿大工程學院的院士。
Sohaib Majzoub（博士，英屬哥倫比亞大學）是阿聯酋沙迦大學的副教授。他曾在阿聯酋迪拜美國大學及沙烏地阿拉伯國王沙烏德大學任教，並在荷蘭代爾夫特科技大學擔任訪問教授。他是IEEE的高級會員。
A. K. MD. Ehsanes Saleh（博士，西安大略大學）是加拿大渥太華卡爾頓大學數學與統計學院的名譽教授及傑出教授。他曾在西門菲莎大學、多倫多大學及史丹佛大學任教。他是IMS、ASA的院士，並且是加拿大統計學會的榮譽會員。
