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An Introduction to Pattern Recognition and Machine Learning

  • Textbook
  • © 2022

Overview

  • Provides case studies in each chapter to contextualize the study material with real-world examples
  • Offers a worked numerical lab in every chapter to ensure that abstract concepts are made concrete and accessible
  • Emphasizes fundamental topics and insights, keeping an eye to making the book engaging and meaningful to many

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About this book

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.

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Keywords

Table of contents (13 chapters)

Reviews

“The book is an introduction to pattern recognition and machine learning. ... The book brings a balance between the analytical and experimental approaches of teaching these important subjects. It offers a great deal of examples and case studies. ... The book contains very useful appendices for refreshing mathematical concepts. Overall, this is an excellent introduction to pattern recognition and machine learning.” (Smaranda Belciug, zbMATH 1516.68002, 2023)

Authors and Affiliations

  • Department of Systems Design Engineering, Faculty of Engineering, University of Waterloo, Waterloo, Canada

    Paul Fieguth

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.

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