Fundamentals of Pattern Recognition and Machine Learning, 2nd Edition
$185.22
- Hardcover
400 pages
- Release Date
7 August 2024
Summary
Mastering Pattern Recognition and Machine Learning: A Comprehensive Guide
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 is thoroughly revised, featuring a new chapter on the emerging topic of physics-informed machine l…
Book Details
| ISBN-13: | 9783031609497 |
|---|---|
| ISBN-10: | 3031609492 |
| Author: | Ulisses Braga-Neto |
| Publisher: | Springer International Publishing AG |
| Imprint: | Springer International Publishing AG |
| Format: | Hardcover |
| Number of Pages: | 400 |
| Edition: | 2nd |
| Release Date: | 7 August 2024 |
| Weight: | 960g |
| Dimensions: | 254mm x 178mm |
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Critics Review
“The style of the book, with its numerous examples, exercises and references, recommends it for a broad audience from students to researchers. The style is comprehensive, yet approachable, and the incremental increase in difficulty, strengthened by the ties with previous chapters, makes it easy to go back and forth between concepts and adapts the theoretical aspects to a wide variety of machine learning tasks.” (Irina Ioana Mohorianu, zbMATH 1555.68003, 2025)
About The Author
Ulisses Braga-Neto
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.
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