An Introduction to Pattern Recognition and Machine Learning

Paul Fieguth

ISBN 10: 3030959937 / ISBN 13: 9783030959937

Published by Springer International Publishing, Springer Nature Switzerland Nov 2022, 2022

About this Item

Neuware -The domains of Pattern Recognition and Machine Learning have experienced exceptional interest and growth, however the overwhelming number of methods and applications can make the fields seem bewildering. This text offers an accessible and conceptually rich introduction, a solid mathematical development emphasizing simplicity and intuition. Students beginning to explore pattern recognition do not need a suite of mathematically advanced methods or complicated computational libraries to understand and appreciate pattern recognition; rather the fundamental concepts and insights, eminently teachable at the undergraduate level, motivate this text. This book provides methods of analysis that the reader can realistically undertake on their own, supported by real-world examples, case-studies, and worked numerical / computational studies.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 496 pp. Englisch.

Seller Inventory # 9783030959937

Bibliographic Details

• Title: An Introduction to Pattern Recognition and Machine Learning
• Publisher: Springer International Publishing, Springer Nature Switzerland Nov 2022
• Publication Date: 2022
• Language: English
• ISBN 10: 3030959937
• ISBN 13: 9783030959937
• Binding: Buch
• Condition: Neu

About this title

Synopsis

The domains of Pattern Recognition and Machine Learning have experienced exceptional interest and growth, however the overwhelming number of methods and applications can make the fields seem bewildering. This text offers an accessible and conceptually rich introduction, a solid mathematical development emphasizing simplicity and intuition. Students beginning to explore pattern recognition do not need a suite of mathematically advanced methods or complicated computational libraries to understand and appreciate pattern recognition; rather the fundamental concepts and insights, eminently teachable at the undergraduate level, motivate this text. This book provides methods of analysis that the reader can realistically undertake on their own, supported by real-world examples, case-studies, and worked numerical / computational studies.

About the Author

Paul Fieguth received the B.A.Sc. degree from the University of Waterloo, Canada, in 1991 and the Ph.D. degree from the Massachusetts Institute of Technology (MIT), United States, in 1995, both degrees in electrical engineering. He joined the faculty at the University of Waterloo in 1996, where he is currently Professor in Systems Design Engineering. He is a co-director of the Vision and Image Processing research group, where his research interests broadly involve machine learning for computer vision and statistical image processing. Specific interests include hierarchical algorithms for large problems, particularly in simplifying modelling and interpretation. In addition to this text, he is also the author on textbooks on Statistical Image Processing and Complex Systems.