John Tuhao Chen, Clement Lee, Lincy Y. Chen

Statistical Prediction and Machine Learning (eBook, PDF)

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 314
• Erscheinungstermin: 6. August 2024
• Englisch
• ISBN-13: 9781040096307
• Artikelnr.: 72272470

Autorenporträt

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 Master's 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.

Inhaltsangabe

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

Rezensionen

"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