Machine Learning: a Concise Introduction (Hardcover)

AN INTRODUCTION TO MACHINE LEARNING THAT INCLUDES THE FUNDAMENTAL TECHNIQUES, METHODS, AND APPLICATIONS

Machine Learning: a Concise Introduction offers a comprehensive introduction to the core concepts, approaches, and applications of machine learning. The author—an expert in the field—presents fundamental ideas, terminology, and techniques for solving applied problems in classification, regression, clustering, density estimation, and dimension reduction. The design principles behind the techniques are emphasized, including the bias-variance trade-off and its influence on the design of ensemble methods. Understanding these principles leads to more flexible and successful applications. Machine Learning: a Concise Introduction also includes methods for optimization, risk estimation, and model selection— essential elements of most applied projects. This important resource:

• Illustrates many classification methods with a single, running example, highlighting similarities and differences between methods
• Presents R source code which shows how to apply and interpret many of the techniques covered
• Includes many thoughtful exercises as an integral part of the text, with an appendix of selected solutions
• Contains useful information for effectively communicating with clients

A volume in the popular Wiley Series in Probability and Statistics, Machine Learning: a Concise Introduction offers the practical information needed for an understanding of the methods and application of machine learning.

STEVEN W. KNOX holds a Ph.D. in Mathematics from the University of Illinois and an M.S. in Statistics from Carnegie Mellon University. He has over twenty years’ experience in using Machine Learning, Statistics, and Mathematics to solve real-world problems. He currently serves as Technical Director of Mathematics Research and Senior Advocate for Data Science at the National Security Agency.

《機器學習簡明介紹》是一本包含基本技巧、方法和應用的機器學習入門書籍。作者是該領域的專家，提供了解分類、回歸、聚類、密度估計和降維等應用問題的基本概念、術語和技巧。強調了這些技巧的設計原則，包括偏差-方差平衡及其對集成方法設計的影響。理解這些原則有助於更靈活和成功地應用機器學習。《機器學習簡明介紹》還包括優化、風險估計和模型選擇等方法，這些是大多數應用項目中必不可少的元素。這本重要的資源還具有以下特點：

- 通過一個連續的示例來說明許多分類方法，突出了方法之間的相似性和差異性
- 提供了R源代碼，展示了如何應用和解釋所涵蓋的許多技術
- 包含許多思考性的練習作為文本的一部分，附有選定解答的附錄
- 包含有效與客戶溝通的有用信息

《機器學習簡明介紹》是廣受歡迎的Wiley概率與統計系列中的一本書，提供了理解機器學習方法和應用所需的實用信息。

作者史蒂文·W·諾克斯擁有伊利諾伊大學的數學博士學位和卡內基梅隆大學的統計學碩士學位。他在使用機器學習、統計和數學解決實際問題方面擁有超過二十年的經驗。他目前擔任國家安全局數學研究技術總監和數據科學高級顧問。