Edité par Cambridge University Press, 2023
ISBN 10 : 1107065550 ISBN 13 : 9781107065550
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Ajouter au panierhardcover. Etat : Very Good. Cover and edges may have some wear.
Edité par Cambridge University Press, 2023
ISBN 10 : 1107065550 ISBN 13 : 9781107065550
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Edité par Cambridge University Press, 2023
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Edité par Cambridge University Press, 2023
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Edité par Cambridge University Press, 2023
ISBN 10 : 1107065550 ISBN 13 : 9781107065550
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Edité par Cambridge University Press, 2023
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Edité par Cambridge University Press, 2023
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Edité par Cambridge University Press, Cambridge, 2023
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Ajouter au panierHardcover. Etat : new. Hardcover. 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 is 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. Endofchapter exercises allow readers to develop their problem-solving skills, and online datasets allow readers to practise analysis of real data. This book provides a comprehensive guide to machine learning and statistics for students and researchers of environmental data science. A broad range of methods are covered together with the relevant background mathematics. End-of-chapter exercises and online data sets are included. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Edité par Cambridge University Press, 2023
ISBN 10 : 1107065550 ISBN 13 : 9781107065550
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Edité par Cambridge University Press, 2023
ISBN 10 : 1107065550 ISBN 13 : 9781107065550
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Edité par Cambridge University Press, 2023
ISBN 10 : 1107065550 ISBN 13 : 9781107065550
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Ajouter au panierHardcover. Etat : Brand New. 500 pages. 9.69x6.69x1.34 inches. In Stock.
Edité par Cambridge University Press, 2023
ISBN 10 : 1107065550 ISBN 13 : 9781107065550
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Edité par Cambridge University Press, 2023
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Edité par Cambridge University Press, 2023
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Edité par Cambridge University Press, 2023
ISBN 10 : 1107065550 ISBN 13 : 9781107065550
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Edité par Cambridge University Press, 2023
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Edité par Cambridge University Press Mär 2023, 2023
ISBN 10 : 1107065550 ISBN 13 : 9781107065550
Langue: anglais
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Ajouter au panierBuch. Etat : Neu. Neuware - This book provides a comprehensive guide to machine learning and statistics for students and researchers of environmental data science. A broad range of methods are covered together with the relevant background mathematics. End-of-chapter exercises and online data sets are included.
Edité par Cambridge University Press, Cambridge, 2023
ISBN 10 : 1107065550 ISBN 13 : 9781107065550
Langue: anglais
Vendeur : CitiRetail, Stevenage, Royaume-Uni
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Ajouter au panierHardcover. Etat : new. Hardcover. 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 is 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. Endofchapter exercises allow readers to develop their problem-solving skills, and online datasets allow readers to practise analysis of real data. This book provides a comprehensive guide to machine learning and statistics for students and researchers of environmental data science. A broad range of methods are covered together with the relevant background mathematics. End-of-chapter exercises and online data sets are included. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Edité par Cambridge University Press, 2023
ISBN 10 : 1107065550 ISBN 13 : 9781107065550
Langue: anglais
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EUR 119,39
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Ajouter au panierHardcover. Etat : Brand New. 500 pages. 9.69x6.69x1.34 inches. In Stock.
Edité par Cambridge University Press, 2023
ISBN 10 : 1107065550 ISBN 13 : 9781107065550
Langue: anglais
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Ajouter au panierEtat : New. Über den AutorWilliam W. Hsieh is a professor emeritus in the Department of Earth, Ocean and Atmospheric Sciences at the University of British Columbia. Known as a pioneer in introducing machine learning to environmental science, he.
Edité par Cambridge University Press, Cambridge, 2023
ISBN 10 : 1107065550 ISBN 13 : 9781107065550
Langue: anglais
Vendeur : AussieBookSeller, Truganina, VIC, Australie
EUR 143,64
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Ajouter au panierHardcover. Etat : new. Hardcover. 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 is 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. Endofchapter exercises allow readers to develop their problem-solving skills, and online datasets allow readers to practise analysis of real data. This book provides a comprehensive guide to machine learning and statistics for students and researchers of environmental data science. A broad range of methods are covered together with the relevant background mathematics. End-of-chapter exercises and online data sets are included. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.