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  3. Machine learning

Introduction to Environmental Data Science

Af: William W. Hsieh Engelsk Hardback

Introduction to Environmental Data Science

Af: William W. Hsieh Engelsk Hardback
Tjek vores konkurrenters priser
Statistical and machine learning methods have many applications in the environmental sciences, including prediction and data analysis in meteorology, hydrology and oceanography, pattern recognition for satellite images from remote sensing, management of agriculture and forests, assessment of climate change, and much more. With rapid advances in machine learning in the last decade, this book provides an urgently needed, comprehensive guide to machine learning and statistics for students and researchers interested in environmental data science. It includes intuitive explanations covering the relevant background mathematics, with examples drawn from the environmental sciences. A broad range of topics are covered, including correlation, regression, classification, clustering, neural networks, random forests, boosting, kernel methods, evolutionary algorithms, and deep learning, as well as the recent merging of machine learning and physics. End-of-chapter exercises allow readers to develop their problem-solving skills and online data sets allow readers to practise analysis of real data.
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Statistical and machine learning methods have many applications in the environmental sciences, including prediction and data analysis in meteorology, hydrology and oceanography, pattern recognition for satellite images from remote sensing, management of agriculture and forests, assessment of climate change, and much more. With rapid advances in machine learning in the last decade, this book provides an urgently needed, comprehensive guide to machine learning and statistics for students and researchers interested in environmental data science. It includes intuitive explanations covering the relevant background mathematics, with examples drawn from the environmental sciences. A broad range of topics are covered, including correlation, regression, classification, clustering, neural networks, random forests, boosting, kernel methods, evolutionary algorithms, and deep learning, as well as the recent merging of machine learning and physics. End-of-chapter exercises allow readers to develop their problem-solving skills and online data sets allow readers to practise analysis of real data.
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Produktdetaljer
Sprog: Engelsk
Sider: 647
ISBN-13: 9781107065550
Indbinding: Hardback
Udgave:
ISBN-10: 1107065550
Kategori: Machine learning
Udg. Dato: 23 mar 2023
Længde: 37mm
Bredde: 251mm
Højde: 177mm
Forlag: Cambridge University Press
Oplagsdato: 23 mar 2023
Forfatter(e): William W. Hsieh
Forfatter(e) William W. Hsieh

Kategori Machine learning

ISBN-13 9781107065550

Sprog Engelsk

Indbinding Hardback

Sider 647

Udgave

Længde 37mm

Bredde 251mm

Højde 177mm

Udg. Dato 23 mar 2023

Oplagsdato 23 mar 2023

Forlag Cambridge University Press

Kategori sammenhænge