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

A graduate textbook that provides a unified treatment of machine learning methods and their applications in the environmental sciences.

List of contents

Preface; 1. Basic notions in classical data analysis; 2. Linear multivariate statistical analysis; 3. Basic time series analysis; 4. Feed-forward neural network models; 5. Nonlinear optimization; 6. Learning and generalization; 7. Kernel methods; 8. Nonlinear classification; 9. Nonlinear regression; 10. Nonlinear principal component analysis; 11. Nonlinear canonical correlation analysis; 12. Applications in environmental sciences; Appendix A. Sources for data and codes; Appendix B. Lagrange multipliers; Bibliography; Index.

About the author

William 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. He is internationally known for his pioneering work in developing and applying machine learning methods in environmental sciences. He has published over eighty peer-reviewed journal publications covering areas of climate variability, machine learning, oceanography, atmospheric science and hydrology.

Summary

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.

Product details

• Authors: William W. Hsieh, William W. (University of British Columbia Hsieh, William. W Hsieh, Hsieh William W.
• Publisher: Cambridge University Press ELT
• Languages: English
• Product format: Paperback / Softback
• Released: 31.03.2018
• EAN: 9781108456906
• ISBN: 978-1-108-45690-6
• No. of pages: 363
• Subjects: Natural sciences, medicine, IT, technology > Biology > Ecology; Non-fiction book > Nature, technology > Natural science; computer science, environmental science, engineering & technology, Science / Environmental Science, Mathematical physics, The Earth: Natural History General, Environmental science, engineering and technology, Neural networks and fuzzy systems