Fundamentals of Pattern Recognition and Machine Learning
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Beschreibung
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.
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 classifiererror 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.
Strikes a balance between theory and practice, using Python scripts, bioinformatics and data sets to illustrate key points Includes supplementary material: sn.pub/extras Request lecturer material: sn.pub/lecturer-material
Autor*in
Ulisses Braga-Neto
Themen in »Fundamentals of Pattern Recognition and Machine Learning«
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
Stimmen zu »Fundamentals of Pattern Recognition and Machine Learning«
“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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