Introduction Environmental Data Science by Hsieh William (24 results)

hardcover. Condition: Very Good. Cover and edges may have some wear.

Hardcover. Condition: Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less.

Condition: New.

Condition: As New. Unread book in perfect condition.

Condition: New. In.

Condition: New.

Hardcover. Condition: New.

Condition: New. 2023. New. hardcover. . . . . .

Condition: As New. Unread book in perfect condition.

Hardback. Condition: New. 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. End-of-chapter exercises allow readers to develop their problem-solving skills, and online datasets allow readers to practise analysis of real data.

Condition: New. 2023. New. hardcover. . . . . . Books ship from the US and Ireland.

Condition: New.

Condition: New. &Uumlber 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.

Hardcover. Condition: Brand New. 500 pages. 9.69x6.69x1.34 inches. In Stock.

Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Endżofżchapter exercises allow readers to develop their problem-solving skills, and online datasets allow readers to practise analysis of real data.

Hardback. Condition: New. 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. End-of-chapter exercises allow readers to develop their problem-solving skills, and online datasets allow readers to practise analysis of real data.

Hardcover. Condition: 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

HRD. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Hardcover. Condition: Brand New. 500 pages. 9.69x6.69x1.34 inches. In Stock. This item is printed on demand.

Hardcover. Condition: 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Condition: New. Print on Demand.

Condition: New. PRINT ON DEMAND.

Hardcover. Condition: 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. 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.

Buch. Condition: Neu. Introduction to Environmental Data Science | William W. Hsieh | Buch | Gebunden | Englisch | 2023 | Cambridge University Press | EAN 9781107065550 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.