Fundamentals of Pattern Recognition and Machine Learning - Rilegato

Braga-Neto, Ulisses

Rilegato

ISBN 10: 3031609492 ISBN 13: 9783031609497

Casa editrice: Springer-Nature New York Inc, 2024

Riassunto

This book is a concise but thorough introduction to the tools commonly used in pattern recognition and machine learning, including classification, dimensionality reduction, regression, and clustering, as well as recent popular topics such as deep neural networks and Gaussian process regression. The Second Edition includes a new chapter on the emerging topic of physics-informed machine learning as well as new material on neural networks.

Combining theory and practice, this book is suitable for the graduate or advanced undergraduate level classroom and self-study. It fills the need of a mathematically-rigorous text that is relevant to the practitioner as well, with datasets from applications in bioinformatics and materials informatics used throughout to illustrate the theory. These datasets are available from the book website to be used in end-of-chapter coding assignments based on python and Keras/Tensorflow. All plots in the text were generated using python scripts and jupyter notebooks, which can be downloaded from the book website.

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Informazioni sull'autore

Ulisses Braga-Neto, Ph.D. is a Professor in the Department of Electrical and Computer Engineering at Texas A&M University. His main research areas are pattern recognition, machine learning, statistical signal processing, and applications in bioinformatics and materials informatics. He has worked extensively in the field of error estimation for pattern recognition and machine learning, having received an NSF CAREER award for research in this area, and co-authored a monograph with Edward R. Dougherty on the topic. He has also made contributions to the field of Mathematical morphology in signal and image processing.

Dalla quarta di copertina

This book is a concise but thorough introduction to the tools commonly used in pattern recognition and machine learning, including classification, dimensionality reduction, regression, and clustering, as well as recent popular topics such as deep neural networks and Gaussian process regression. The Second Edition includes a new chapter on the emerging topic of physics-informed machine learning and significant additions to the section on neural networks. In addition to the new chapter, Fundamentals of Pattern Recognition and Machine Learning contains other unique features such as an extensive chapter on classifier error estimation and sections on Bayesian error estimation, separate sampling designs, and rank-based classification.

Combining theory and practice, this book is suitable for the graduate or advanced undergraduate level classroom and self-study. It fills the need of a mathematically-rigorous text that is relevant to the practitioner as well, with datasets from applications in bioinformatics and materials informatics used throughout to illustrate the theory. These datasets are available from the book website to be used in end-of-chapter coding assignments based on python and Keras/Tensorflow. All plots in the text were generated using python scripts and jupyter notebooks.

 

Dettagli dell'edizione

• Editore: Springer-Nature New York Inc
• Data di pubblicazione: 2024
• Lingua: Inglese
• ISBN 10: 3031609492
• ISBN 13: 9783031609497
• Rilegatura: Copertina rigida
• Numero edizione: 2
• Numero di pagine: 400
• Contatto del produttore: ProductSafety@springernature.com; ProductSafety@springernature.com; ; Poststr. 9; Darmstadt; 64293; Germania