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Fundamentals of Pattern Recognition and Machine Learning

Af: Ulisses Braga-Neto Engelsk Paperback

Fundamentals of Pattern Recognition and Machine Learning

Af: Ulisses Braga-Neto Engelsk Paperback
Tjek vores konkurrenters priser

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. 

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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. 

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Se mere i:
Produktdetaljer
Sprog: Engelsk
Sider: 357
ISBN-13: 9783030276584
Indbinding: Paperback
Udgave:
ISBN-10: 3030276589
Kategori: Stokastik
Udg. Dato: 11 sep 2021
Længde: 0mm
Bredde: 178mm
Højde: 254mm
Forlag: Springer Nature Switzerland AG
Oplagsdato: 11 sep 2021
Forfatter(e): Ulisses Braga-Neto
Forfatter(e) Ulisses Braga-Neto

Kategori Stokastik

ISBN-13 9783030276584

Sprog Engelsk

Indbinding Paperback

Sider 357

Udgave

Længde 0mm

Bredde 178mm

Højde 254mm

Udg. Dato 11 sep 2021

Oplagsdato 11 sep 2021

Forlag Springer Nature Switzerland AG