Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (Hardcover)

John D. Kelleher, Brian Mac Namee, Aoife D'Arcy

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Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context.

After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning. Each of these approaches is introduced by a nontechnical explanation of the underlying concept, followed by mathematical models and algorithms illustrated by detailed worked examples. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book, informed by the authors' many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals.

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機器學習常被用於從大型數據集中提取模式以建立預測模型。這些模型用於預測數據分析應用,包括價格預測、風險評估、預測客戶行為和文件分類。這本入門教材詳細介紹了預測數據分析中使用的最重要的機器學習方法,涵蓋了理論概念和實際應用。技術和數學材料配有解釋性的實例,案例研究則展示了這些模型在更廣泛的商業背景下的應用。

在討論從數據到洞察到決策的軌跡後,本書描述了四種機器學習方法:基於信息的學習、基於相似性的學習、基於概率的學習和基於錯誤的學習。每種方法都通過非技術性的解釋介紹其基本概念,然後通過詳細的實例展示數學模型和算法。最後,本書探討了評估預測模型的技術,並提供了兩個案例研究,描述了從制定業務問題到實施分析解決方案的具體數據分析項目的每個階段。本書基於作者多年的機器學習教學和預測數據分析項目經驗,適用於計算機科學、工程學、數學或統計學的本科生,以及具有預測數據分析應用的研究生和專業人士的參考書。