Fundamentals of Pattern Recognition and Machine Learning

Authors: Braga-Neto, Ulisses

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  • Strikes a balance between theory and practice, with extensive use of python scripts and real bioinformatics and materials informatics data sets to illustrate key points of the theory.
  • User friendly: the theory is amply illustrated with examples and figures; sections containing advanced or supplementary topics are marked with a star or identified as “additional topics” sections; all plots in the text were generated using python scripts, which the user can experiment with and use them in the coding assignments.
  • A thorough but brief review of probability and statistics, optimization, and matrix algebra concepts needed in the book is provided in the Appendices.
  • Numerous end-of-chapter exercises and python-based computer projects provide hands-on experience that helps the student understand the subject.
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eBook $59.99
price for USA in USD
  • ISBN 978-3-030-27656-0
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $79.99
price for USA in USD
About this Textbook

Fundamentals of Pattern Recognition and Machine Learning is designed for a one or two-semester introductory course in Pattern Recognition or Machine Learning at the graduate or advanced undergraduate level. The book combines theory and practice and is suitable to the classroom and self-study. It has grown out of lecture notes and assignments that the author has developed while teaching classes on this topic for the past 13 years at Texas A&M University.

The book is intended to be concise but thorough. It does not attempt an encyclopedic approach, but covers in significant detail 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 Gaussian process regression and convolutional neural networks. In addition, the selection of topics has a few features that are unique among comparable texts: it contains an extensive chapter on classifier error estimation, as well as sections on Bayesian classification, Bayesian error estimation, separate sampling, and rank-based classification.

The book is mathematically rigorous and covers the classical theorems in the area. Nevertheless, an effort is made in the book to strike a balance between theory and practice. In particular, examples with datasets from applications in bioinformatics and materials informatics are 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 scikit-learn. All plots in the text were generated using python scripts, which are also available on the book website. 

About the authors

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.

Reviews

“The coverage is very unique and I like the way that the theory is interspersed with applications and python scripts. I don't know any other book that covers ML in such an integrated manner.” (Alfred Hero, Professor, University of Michigan, USA)

“I think the selection of topics is really nice. Also, the math is very clearly written; I'm sure it will be greatly appreciated.” (Gábor Lugosi, Research Professor, Pompeu-Fabra University, Spain)

Table of contents (11 chapters)

Table of contents (11 chapters)

Buy this book

eBook $59.99
price for USA in USD
  • ISBN 978-3-030-27656-0
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $79.99
price for USA in USD
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Bibliographic Information

Bibliographic Information
Book Title
Fundamentals of Pattern Recognition and Machine Learning
Authors
Copyright
2020
Publisher
Springer International Publishing
Copyright Holder
Springer Nature Switzerland AG
eBook ISBN
978-3-030-27656-0
DOI
10.1007/978-3-030-27656-0
Hardcover ISBN
978-3-030-27655-3
Edition Number
1
Number of Pages
XVIII, 357
Number of Illustrations
11 b/w illustrations, 73 illustrations in colour
Topics