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Machine Learning Methods in the Environmental Sciences - Neural Networks and Kernels (Paperback) Loot Price: R1,138
Discovery Miles 11 380
Machine Learning Methods in the Environmental Sciences - Neural Networks and Kernels (Paperback): William W. Hsieh

Machine Learning Methods in the Environmental Sciences - Neural Networks and Kernels (Paperback)

William W. Hsieh

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Loot Price R1,138 Discovery Miles 11 380 | Repayment Terms: R107 pm x 12*

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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.

General

Imprint: Cambridge UniversityPress
Country of origin: United Kingdom
Release date: March 2018
Authors: William W. Hsieh
Dimensions: 245 x 170 x 20mm (L x W x T)
Format: Paperback - Trade
Pages: 363
ISBN-13: 978-1-108-45690-6
Categories: Books > Professional & Technical > Environmental engineering & technology > General
Books > Computing & IT > General theory of computing > General
Books > Computing & IT > Applications of computing > General
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LSN: 1-108-45690-1
Barcode: 9781108456906

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