Machine Learning Methods in the Environmental Sciences
Neural Networks and Kernels
- Author: William W. Hsieh, University of British Columbia, Vancouver
- Date Published: March 2018
- availability: Available
- format: Paperback
- isbn: 9781108456906
Paperback
Other available formats:
Hardback, eBook
Looking for an inspection copy?
This title is not currently available on inspection
-
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.
Read more- Offers broad coverage of the applications of modern non-linear machine learning methods in remote sensing, atmospheric science, oceanography, hydrology and ecology, providing the most extensive resource available
- An appendix listing websites for downloading computer codes and datasets allows the reader to apply the methods discussed in the book to real datasets
- End-of-chapter exercises and a course website with updates to the book material develop problem-solving skills and keep the student/reader at par with new developments
Reviews & endorsements
'… one of the first books describing machine learning techniques in the context of environmental applications … goes a long way in explaining these subjects in a very clear, concise, and understandable way. This is one of the few books where one will find diverse areas of machine learning all within the same cover … aimed at advanced undergraduates and Ph.D. students, as well as researchers and practitioners. No previous knowledge of machine learning concepts is assumed.' Vladimir Krasnopolsky, National Oceanic and Atmospheric Administration (NOAA) and National Weather Service
See more reviews'[This book] aims to, and succeeds in, bridging the gap between AI and what is often referred to as conventional statistics. Add to that the unique perspective that a physicist and an environmental scientist brings to the table, and one has a truly rare book. … a well-balanced mix of theoretical and practical exercises. … Hsieh's book [is] ideal as both a textbook on the topic, and a reference book for the researcher in the field.' Caren Marzban, University of Washington and University of Oklahoma
'This book is unique because it presents machine learning in the context of environmental science applications. I found it to be a valuable tool to bring myself up to date with the historical and recent developments in the subject of machine learning, and I believe the reader will too. The purchase price is modest. I highly recommend that any student or researcher interested in machine learning methods obtain a copy.' William Burrows, Canadian Meteorological and Oceanographic Society Bulletin
Customer reviews
Not yet reviewed
Be the first to review
Review was not posted due to profanity
×Product details
- Date Published: March 2018
- format: Paperback
- isbn: 9781108456906
- length: 363 pages
- dimensions: 245 x 170 x 20 mm
- weight: 0.64kg
- availability: Available
Table 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.-
Find resources associated with this title
Type Name Unlocked * Format Size Showing of
This title is supported by one or more locked resources. Access to locked resources is granted exclusively by Cambridge University Press to lecturers whose faculty status has been verified. To gain access to locked resources, lecturers should sign in to or register for a Cambridge user account.
Please use locked resources responsibly and exercise your professional discretion when choosing how you share these materials with your students. Other lecturers may wish to use locked resources for assessment purposes and their usefulness is undermined when the source files (for example, solution manuals or test banks) are shared online or via social networks.
Supplementary resources are subject to copyright. Lecturers are permitted to view, print or download these resources for use in their teaching, but may not change them or use them for commercial gain.
If you are having problems accessing these resources please contact [email protected].
Sorry, this resource is locked
Please register or sign in to request access. If you are having problems accessing these resources please email [email protected]
Register Sign in» Proceed
You are now leaving the Cambridge University Press website. Your eBook purchase and download will be completed by our partner www.ebooks.com. Please see the permission section of the www.ebooks.com catalogue page for details of the print & copy limits on our eBooks.
Continue ×Are you sure you want to delete your account?
This cannot be undone.
Thank you for your feedback which will help us improve our service.
If you requested a response, we will make sure to get back to you shortly.
×