Publication of the “Machine Learning with R” Series / Professor Kwahk Kee-Young (Business IT Major)
- 26.09.01 / 홍유민
Professor Kwahk Kee-Young of the Graduate School of Business and IT at Kookmin University has published the “Machine Learning with R” series, which systematically outlines the key concepts and analytical methods of machine learning using R. The series consists of three volumes: *Machine Learning with R: Modeling, Tidymodels, Caret*, *Machine Learning with R: Supervised Learning*, and *Machine Learning with R: Unsupervised Learning*. It comprehensively covers everything from core machine learning theory to a wide range of algorithms and analytical tools applicable to real-world data analysis.
*Machine Learning with R: Modeling, Tidymodels, Caret* introduces the key concepts, modeling procedures, data preprocessing, performance evaluation metrics, and evaluation methods required in the machine learning model development process. In particular, it explains 29 widely used machine learning algorithms and demonstrates how to use Tidymodels and Caret—leading integrated tools for R-based machine learning analysis—through various examples.
*Machine Learning with R: Supervised Learning* focuses on supervised learning—which predicts specific numerical values or categories based on data—and covers regression and classification problems. It introduces 18 major machine learning algorithms used in regression and classification analysis alongside real-world examples, enabling readers to understand the characteristics and applications of each algorithm. *Machine Learning with R: Unsupervised Learning* explains how to explore the structures and patterns inherent in data without a clear prediction goal. It covers a total of 11 unsupervised learning algorithms and their use cases, focusing on dimension reduction, clustering, and association rule mining.
A key feature of this series is that while each volume covers a different area of machine learning, it maintains a cohesive learning structure that progresses from the fundamental principles of modeling through supervised and unsupervised learning. Designed to help readers understand the complex and vast field of machine learning from a systematic yet practical perspective, this series is expected to be a valuable resource not only for beginners new to machine learning but also for learners seeking to systematically organize their existing knowledge and enhance their practical data analysis skills.
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This content is translated from Korean to English using the AI translation service DeepL and may contain translation errors such as jargon/pronouns. If you find any, please send your feedback to kookminpr@kookmin.ac.kr so we can correct them.
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Publication of the “Machine Learning with R” Series / Professor Kwahk Kee-Young (Business IT Major) |
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Professor Kwahk Kee-Young of the Graduate School of Business and IT at Kookmin University has published the “Machine Learning with R” series, which systematically outlines the key concepts and analytical methods of machine learning using R. The series consists of three volumes: *Machine Learning with R: Modeling, Tidymodels, Caret*, *Machine Learning with R: Supervised Learning*, and *Machine Learning with R: Unsupervised Learning*. It comprehensively covers everything from core machine learning theory to a wide range of algorithms and analytical tools applicable to real-world data analysis. *Machine Learning with R: Modeling, Tidymodels, Caret* introduces the key concepts, modeling procedures, data preprocessing, performance evaluation metrics, and evaluation methods required in the machine learning model development process. In particular, it explains 29 widely used machine learning algorithms and demonstrates how to use Tidymodels and Caret—leading integrated tools for R-based machine learning analysis—through various examples. *Machine Learning with R: Supervised Learning* focuses on supervised learning—which predicts specific numerical values or categories based on data—and covers regression and classification problems. It introduces 18 major machine learning algorithms used in regression and classification analysis alongside real-world examples, enabling readers to understand the characteristics and applications of each algorithm. *Machine Learning with R: Unsupervised Learning* explains how to explore the structures and patterns inherent in data without a clear prediction goal. It covers a total of 11 unsupervised learning algorithms and their use cases, focusing on dimension reduction, clustering, and association rule mining. A key feature of this series is that while each volume covers a different area of machine learning, it maintains a cohesive learning structure that progresses from the fundamental principles of modeling through supervised and unsupervised learning. Designed to help readers understand the complex and vast field of machine learning from a systematic yet practical perspective, this series is expected to be a valuable resource not only for beginners new to machine learning but also for learners seeking to systematically organize their existing knowledge and enhance their practical data analysis skills.
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