Supervised Learning - Mathematical Foundations and Real-World Applications

This book discusses the relevance of probabilistic supervised learning, to the pursuit of automated and reliable prediction of an unknown that is in a state of relationship with another variable. This book is valuable for students across disciplines, including students of computational sciences, statistics, and mathematics.

Sommario

Foreword Preface Acknowledgements 1. Inter-variable relationships 2. Bayesianism 3. Supervised learning & prediction, using Gaussian
Processes 4. Covariance kernels suitable for real-world data 5. Learning a high-dimensional function 6. A self-assembled prior on correlation matrices Bibliography Index

Info autore

Dr. Dalia Chakrabarty is a Reader in Statistical Data Science in the Department of Mathematics at the University of York. Her PhD is from St. Cross College in the University of Oxford, and she works on the development of methods to permit the probabilistic learning of random variables of various kinds, given real world data that is diversely challenging.

Riassunto

This book discusses the relevance of probabilistic supervised learning, to the pursuit of automated and reliable prediction of an unknown that is in a state of relationship with another variable. This book is valuable for students across disciplines, including students of computational sciences, statistics, and mathematics.

Dettagli sul prodotto

• Autori: Dalia Chakrabarty, Dalia (Department of Mathematics Chakrabarty
• Editore: Taylor & Francis Ltd.
• Lingue: Inglese
• Formato: Copertina rigida
• Pubblicazione: 29.04.2025
• EAN: 9781032283302
• ISBN: 978-1-0-3228330-2
• Pagine: 344
• Categorie: Scienze naturali, medicina, informatica, tecnica > Informatica, EDP > Informatica; Data Mining, BUSINESS & ECONOMICS / Statistics, MATHEMATICS / Probability & Statistics / General, COMPUTERS / Machine Theory, computer science, Probability & statistics, Automatic control engineering, Probability and statistics, COMPUTERS / Data Science / Machine Learning, COMPUTERS / Data Science / Data Analytics, Gaussian Processes;Machine Learning;Data Mining