Skip to main content
  • Textbook
  • © 2020

Fundamentals of Pattern Recognition and Machine Learning

  • 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.

  • Includes supplementary material: sn.pub/extras

  • Request lecturer material: sn.pub/lecturer-material

Buying options

eBook USD 44.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-27656-0
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book USD 59.99
Price excludes VAT (USA)
Hardcover Book USD 84.99
Price excludes VAT (USA)

This is a preview of subscription content, access via your institution.

Table of contents (11 chapters)

  1. Front Matter

    Pages i-xviii
  2. Introduction

    • Ulisses Braga-Neto
    Pages 1-13
  3. Optimal Classification

    • Ulisses Braga-Neto
    Pages 15-49
  4. Sample-Based Classification

    • Ulisses Braga-Neto
    Pages 51-65
  5. Parametric Classification

    • Ulisses Braga-Neto
    Pages 67-88
  6. Nonparametric Classification

    • Ulisses Braga-Neto
    Pages 89-108
  7. Function-Approximation Classification

    • Ulisses Braga-Neto
    Pages 109-150
  8. Error Estimation for Classification

    • Ulisses Braga-Neto
    Pages 151-183
  9. Model Selection for Classification

    • Ulisses Braga-Neto
    Pages 185-204
  10. Dimensionality Reduction

    • Ulisses Braga-Neto
    Pages 205-229
  11. Clustering

    • Ulisses Braga-Neto
    Pages 231-252
  12. Regression

    • Ulisses Braga-Neto
    Pages 253-286
  13. Back Matter

    Pages 287-357

About this book

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. 

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.

Keywords

  • Pattern Recognition
  • Machine Learning
  • Classification
  • Regression
  • Clustering
  • Feature Selection
  • Error Estimation
  • Linear Discriminant Analysis
  • Vapnik-Chervonenkis Theory
  • Perceptron
  • Neural Networks
  • Support Vector Machines
  • Multidimensional Scaling
  • Decision Trees
  • Principal Component Analysis
  • Gaussian Process
  • Cross-Validation
  • Bootstrap
  • K-means Clustering
  • Gaussian Mixture Modeling

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)

Authors and Affiliations

  • Department of Electrical & Computer Engineering, Texas A&M University, College Station, USA

    Ulisses Braga-Neto

About the author

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.

Bibliographic Information

  • Book Title: Fundamentals of Pattern Recognition and Machine Learning

  • Authors: Ulisses Braga-Neto

  • DOI: https://doi.org/10.1007/978-3-030-27656-0

  • Publisher: Springer Cham

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: Springer Nature Switzerland AG 2020

  • Hardcover ISBN: 978-3-030-27655-3

  • Softcover ISBN: 978-3-030-27658-4

  • eBook ISBN: 978-3-030-27656-0

  • Edition Number: 1

  • Number of Pages: XVIII, 357

  • Number of Illustrations: 11 b/w illustrations, 73 illustrations in colour

  • Topics: Automated Pattern Recognition, Computer Vision, Probability Theory

Buying options

eBook USD 44.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-27656-0
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book USD 59.99
Price excludes VAT (USA)
Hardcover Book USD 84.99
Price excludes VAT (USA)