Fundamentals of Pattern Recognition and Machine Learning

by Ulisses Braga-Neto

Overview

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 learning and additional material on deep neural networks.

Combining theory and practice, this book is suitable for the graduate or advanced undergraduate level classroom and self-study. It fills the need of a mathematically-rigorous text that is relevant to the practitioner as well, with datasets from applications in bioinformatics and materials informatics 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 Keras/Tensorflow. All plots in the text were generated using python scripts and jupyter notebooks, which can be downloaded from the book website.

Product Details

ISBN-13:	9783031609497
Publisher:	Springer International Publishing
Publication date:	08/07/2024
Edition description:	Second Edition 2024
Pages:	400
Product dimensions:	7.01(w) x 10.00(h) x (d)

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.

Table of Contents

Introduction.- Optimal Classification.- Sample-Based Classification.- Parametric Classification.- Nonparametric Classification.- Function-Approximation Classification.- Error Estimation for Classification.- Model Selection for Classification.- Dimensionality Reduction.- Clustering.- Regression.- Bayesian Machine Learning.- Scientific.- Machine Learning.- Appendices.

What People are Saying About This

From the Publisher

“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)

Editorial Reviews

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)

From the Publisher