Fundamentals of Pattern Recognition and Machine Learning
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Beschreibung
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.
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.
Strikes a balance between theory and practice, with extensive use of Python scripts and real bioinformatics data sets Second Edition includes a new chapter on physics-informed machine learning, as well as additional exercises and figures Includes supplementary material online, as well as instructor resources
Autor*in
Ulisses Braga-Neto
Themen in »Fundamentals of Pattern Recognition and Machine Learning«
Pattern Recognition Regression Clustering Feature Selection Error Estimation Materials Informatics Vapnik-Chervonenkis Theory Dimensionality Reduction Neural Networks Support Vector Machines Multidimensional Scaling Decision Trees Principal Component Analysis Gaussian Process Cross-Validation
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“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)
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Praise for the First Edition:
“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)
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