High Dimensional Data Science

Erscheinungsjahr 2026
ISBN: 978-981-9207-58-9
Verlag: Springer

Buch, Englisch, 390 Seiten, Format (B × H): 155 mm x 235 mm

Reihe: IISA Series on Statistics and Data Science

This edited volume is a Data Science text, where multiple aspects of Statistics, Machine Learning, Artificial Intelligence, Big Data methodology, High-dimensional techniques and algorithms, and applications and case studies are presented together. Owing to its very broad scope,  the chapters of this book will be collected under thematically coherent groups. The planned groups of chapters are on (i) Regularization and high-dimensional machine learning, (ii) Bayesian high-dimensional modeling and computation, (iii) Spatio-temporal and dependent data models, and (iv) Deep learning and artificial intelligence. Case studies and applications, as well as high-dimensional probability theory may be two other groups of chapters, if a number of authors write with primary focus on these topics. This book will be useful for graduate students who want to specialize eventually on some aspect of Data Science, to beginners as well as advanced researchers in the field of Data Science, and mayas well serve as an encyclopedic text on Data Science.

Zielgruppe

Research

Autoren/Hrsg.

Chatterjee, Snigdhansu

Fachgebiete

• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
• Mathematik | Informatik EDV | Informatik Business Application Mathematische & Statistische Software
• Mathematik | Informatik Mathematik Stochastik Bayesianische Inferenz
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Professionelle Anwendung

Inhaltsverzeichnis

Chapter 1. Introduction.- PART I: Regularization and high-dimensional machine learning.- PART II: Bayesian high-dimensional modeling and computation.- PART III: Spatio-temporal and dependent data models.- PART IV: Deep learning and artificial intelligence.

Über Autor(innen)

Snigdhansu (Ansu) Chatterjee is the Sinha E-nnovate Endowed Chair Professor at the University of Maryland at Baltimore County, USA. Till recently, he was a Professor in the School of Statistics at the University of Minnesota, and the Director of the Institute for Research in Statistics and its Applications, an interdisciplinary data science institute at the University of Minnesota, and a Data Scholar with the National Institutes of Health (NIH), USA. His research interests include various topics in artificial intelligence, machine learning, statistics and data science with emphasis on foundations, statistical inferences and uncertainty quantifications, and applications of data-driven techniques to various real-world problems including neurosciences, personalized healthcare and medicine, climate and environmental sciences, and to food, water and energy security issues. He has published in the Annals of Statistics, Annals of Applied Statistics, Annals of the Institute of Statistical Mathematics, Bioinformatics and other top Statistics journals, and in peer-reviewed Computer Science conferences on artificial intelligence, machine learning and data mining.