Introduction to Environmental Data Science

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.

Sommario

1. Introduction; 2. Basics; 3. Probability distributions; 4. Statistical inference; 5. Linear regression; 6. Neural networks; 7. Nonlinear optimization; 8. Learning and generalization; 9. Principal components and canonical correlation; 10. Unsupervised learning; 11. Time series; 12. Classification; 13. Kernel methods; 14. Decision trees, random forests and boosting; 15. Deep learning; 16. Forecast verification and post-processing; 17. Merging of machine learning and physics; Appendices; References; Index.

Info autore

William 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 has written over 100 peer-reviewed journal papers on climate variability, machine learning, atmospheric science, oceanography, hydrology, and agricultural science. He is the author of the book Machine Learning Methods in the Environmental Sciences ( Cambridge University Press, 2009), the first single-authored textbook on machine learning for environmental scientists. Currently retired in Victoria, British Columbia, he enjoys growing organic vegetables.

Riassunto

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.

Dettagli sul prodotto

• Autori: William W. Hsieh, William W. (University of British Columbia Hsieh, Hsieh William W.
• Editore: Cambridge University Press ELT
• Lingue: Inglese
• Formato: Copertina rigida
• Pubblicazione: 31.12.2022
• EAN: 9781107065550
• ISBN: 978-1-107-06555-0
• Pagine: 647
• Categorie: Scienze naturali, medicina, informatica, tecnica > Biologia > Ecologia; Scienze sociali, diritto, economia > Media, comunicazione > Scienze della comunicazione; machine learning, Data Mining, Environmental Management, pattern recognition, Science / Environmental Science, Meteorology & climatology, Meteorology and climatology, Mathematical & statistical software, Environmental Monitoring, Geographical information systems (GIS) & remote sensing, Data analysis: general, Data science and analysis: general, Mathematical and statistical software, Geographical information systems, geodata and remote sensing