Machine learning methods environmental von hsieh william (13 Ergebnisse)

Zustand: New. Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be printed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide.…

Hardcover. Zustand: Like New. First Edition. Hardback book in nearly new condition: firm and square with strong joints. Just a few hardly noticeable rubs or very mild bumps. Hence a non-text page shows a small 'damaged' stamp. Despite such this book looks and feels unread. Thus the contents are crisp, fresh and tight. And so a very nice book in great condition, now offered for sale at a reasonable price.…

Zustand: New. In English.

Zustand: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

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

Zustand: New. 2018. Reprint. Paperback. . . . . . Books ship from the US and Ireland.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.…

Zustand: Used. pp. xiii + 349 Illus.

Zustand: Used. pp. xiii + 349.

Zustand: New. In English.

Zustand: New. A graduate textbook that provides a unified treatment of machine learning methods and their applications in the environmental sciences. Num Pages: 364 pages, 116 b/w illus. 48 exercises. BIC Classification: RN; UYQM; UYQN. Category: (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 256 x 182 x 25. Weight in Grams: 866. . 2009. 1st Edition. hardcover. . . . . Books ship from the US and Ireland. …

Hardcover. Zustand: Brand New. 1st edition. 368 pages. 9.84x6.77x0.87 inches. In Stock.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.…