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El. knyga: Statistical Prediction and Machine Learning

(Bowling Green State University, Ohio, USA), (Cornell University, U.S.A),
  • Formatas: 314 pages
  • Išleidimo metai: 06-Aug-2024
  • Leidėjas: Chapman & Hall/CRC
  • Kalba: eng
  • ISBN-13: 9781040096338
Kitos knygos pagal šią temą:
Statistical Prediction and Machine LearningDidesnis paveikslėlis
  • Formatas: 314 pages
  • Išleidimo metai: 06-Aug-2024
  • Leidėjas: Chapman & Hall/CRC
  • Kalba: eng
  • ISBN-13: 9781040096338
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Written by an experienced statistics educator and two data scientists, this book unifies conventional statistical thinking and contemporary machine learning framework into a single overarching umbrella over data science. The book is designed to bridge the knowledge gap between conventional statistics and machine learning. It provides an accessible approach for readers with a basic statistics background to develop a mastery of machine learning. The book starts with elucidating examples in Chapter 1 and fundamentals on refined optimization in Chapter 2, which are followed by common supervised learning methods such as regressions, classification, support vector machines, tree algorithms, and range regressions. After a discussion on unsupervised learning methods, it includes a chapter on unsupervised learning and a chapter on statistical learning with data sequentially or simultaneously from multiple resources.

One of the distinct features of this book is the comprehensive coverage of the topics in statistical learning and medical applications. It summarizes the authors’ teaching, research, and consulting experience in which they use data analytics. The illustrating examples and accompanying materials heavily emphasize understanding on data analysis, producing accurate interpretations, and discovering hidden assumptions associated with various methods.

Key Features:

  • Unifies conventional model-based framework and contemporary data-driven methods into a single overarching umbrella over data science.
  • Includes real-life medical applications in hypertension, stroke, diabetes, thrombolysis, aspirin efficacy.
  • Integrates statistical theory with machine learning algorithms.
  • Includes potential methodological developments in data science.


This book unifies conventional statistical thinking and contemporary machine learning framework into a single overarching umbrella over data science. The book is designed to bridge the knowledge gap between conventional statistics and machine learning.

Recenzijos

"This book is a valuable resource for a diverse audience, including students, researchers, and professionals in the fields of statistics, data science, and machine learning. Its ability to synthesize theoretical principles with practical applications sets it apart from many other texts in the domain. The authors strike a fine balance between addressing foundational concepts, such as bias-variance tradeoffs and cross-validation, and delving into advanced topics like support vector machines and simultaneous learning. By integrating these elements, the book equips readers with a robust toolkit for tackling real-world challenges. Particularly commendable is its emphasis on practical examples, such as medical case studies, which not only contextualize the theoretical discussions but also demonstrate their relevance across disciplines. For professionals, the book offers insights into optimizing models and applying techniques to complex datasets, bridging the often-intimidating gap between theory and practice." - Fransiskus Serfian Jogo, Techonometrics, February 2025

"In a nutshell, this is an essential textbook that brings together statistical learning and machine learning in a cohesive and practical framework. The book is meticulously designed for students, data analysts, and professionals who seek a clear and comprehensive understanding of the essential tools and techniques used in data analysis today. One of its main strengths lies in its ability to integrate theoretical concepts from statistics with the computational practices of machine learning, bridging the gap between abstract theory and practical application.

The authors of this book are highly experienced in the field of data science, with an academic background in both statistical theory and computational methods. This combination makes their writing both accessible and authoritative, striking a balance that is often hard to achieve in texts that deal with complex topics. The book covers a broad spectrum of material, beginning with foundational topics like regression analysis and progressing to more sophisticated methods such as support vector machines, decision trees, unsupervised learning, and neural networks. This thorough coverage makes the book a one-stop resource for anyone seeking to master the fundamentals of statistical prediction and machine learning." - Michal Pesta, Charles University, Journal of the American Statistical Association, April 21, 2025

Preface
1. Two Cultures in Data Science
2. Fundamental Instruments
3. Sensitivity and Specificity Trade-off
4. Bias and Variation Trade-off
5. Linear Prediction
6. Nonlinear Prediction
7. Minimum Risk Classification
8. Support Vectors and Duality Theorem
9. Decision Trees and Range Regressions
10. Unsupervised Learning and Optimization
11. Simultaneous Learning and Multiplicity Bibliography Index

John T. Chen is a professor of Statistics at Bowling Green State University. He completed his postdoctoral training at McMaster University (Canada) after earning a PhD degree in statistics at the University of Sydney (Australia). John has published research papers in statistics journals such as Biometrika as well as in medicine journals such as the Annals of Neurology.

Clement Lee is a data scientist in a private firm in New York. He earned a Masters degree in applied mathematics from New York University, after graduating from Princeton University in computer science. Clement enjoys spending time with his beloved wife Belinda and their son Pascal.

Lincy Y. Chen is a data scientist at JP Morgan Chase & Co. She graduated from Cornell University, winning the Edward M. Snyder Prize in Statistics. Lincy has published papers regarding refinements of machine learning methods.
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