9781108456906 - Machine Learning Methods in the Environmental Sciences: Neural Networks and Kernels di Hsieh, William (17 risultati)

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Condizione: New. pp. 364 Reprint edition NO-PA16APR2015-KAP.

Paperback. Condizione: Brand New. reprint edition. 349 pages. 9.50x6.75x1.00 inches. In Stock.

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Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - A graduate textbook that provides a unified treatment of machine learning methods and their applications in the environmental sciences.

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Paperback. Condizione: new. Paperback. Machine learning methods originated from artificial intelligence and are now used in various fields in environmental sciences today. This is the first single-authored textbook providing a unified treatment of machine learning methods and their applications in the environmental sciences. Due to their powerful nonlinear modelling capability, machine learning methods today are used in satellite data processing, general circulation models(GCM), weather and climate prediction, air quality forecasting, analysis and modelling of environmental data, oceanographic and hydrological forecasting, ecological modelling, and monitoring of snow, ice and forests. The book includes end-of-chapter review questions and an appendix listing websites for downloading computer code and data sources. A resources website contains datasets for exercises, and password-protected solutions are available. The book is suitable for first-year graduate students and advanced undergraduates. It is also valuable for researchers and practitioners in environmental sciences interested in applying these new methods to their own work. Machine learning methods are used in various fields in environmental sciences today. This is the first single-authored textbook providing a unified treatment of these methods and their applications, and is a valuable resource for advanced undergraduates, graduates, and researchers and practitioners interested in applying such methods to their own work. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Paperback. Condizione: Brand New. reprint edition. 349 pages. 9.50x6.75x1.00 inches. In Stock. This item is printed on demand.

Paperback / softback. Condizione: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.

Condizione: New. Print on Demand pp. 364.

Condizione: New. PRINT ON DEMAND pp. 364.

Paperback. Condizione: new. Paperback. Machine learning methods originated from artificial intelligence and are now used in various fields in environmental sciences today. This is the first single-authored textbook providing a unified treatment of machine learning methods and their applications in the environmental sciences. Due to their powerful nonlinear modelling capability, machine learning methods today are used in satellite data processing, general circulation models(GCM), weather and climate prediction, air quality forecasting, analysis and modelling of environmental data, oceanographic and hydrological forecasting, ecological modelling, and monitoring of snow, ice and forests. The book includes end-of-chapter review questions and an appendix listing websites for downloading computer code and data sources. A resources website contains datasets for exercises, and password-protected solutions are available. The book is suitable for first-year graduate students and advanced undergraduates. It is also valuable for researchers and practitioners in environmental sciences interested in applying these new methods to their own work. Machine learning methods are used in various fields in environmental sciences today. This is the first single-authored textbook providing a unified treatment of these methods and their applications, and is a valuable resource for advanced undergraduates, graduates, and researchers and practitioners interested in applying such methods to their own work. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Kartoniert / Broschiert. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. &Uumlber den AutorWilliam W. Hsieh is a Professor in the Department of Earth and Ocean Sciences and in the Department of Physics and Astronomy, as well as Chair of the Atmospheric Science Programme, at the University of British Columbia.

Paperback. Condizione: new. Paperback. Machine learning methods originated from artificial intelligence and are now used in various fields in environmental sciences today. This is the first single-authored textbook providing a unified treatment of machine learning methods and their applications in the environmental sciences. Due to their powerful nonlinear modelling capability, machine learning methods today are used in satellite data processing, general circulation models(GCM), weather and climate prediction, air quality forecasting, analysis and modelling of environmental data, oceanographic and hydrological forecasting, ecological modelling, and monitoring of snow, ice and forests. The book includes end-of-chapter review questions and an appendix listing websites for downloading computer code and data sources. A resources website contains datasets for exercises, and password-protected solutions are available. The book is suitable for first-year graduate students and advanced undergraduates. It is also valuable for researchers and practitioners in environmental sciences interested in applying these new methods to their own work. Machine learning methods are used in various fields in environmental sciences today. This is the first single-authored textbook providing a unified treatment of these methods and their applications, and is a valuable resource for advanced undergraduates, graduates, and researchers and practitioners interested in applying such methods to their own work. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.