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.
A comprehensive guide to machine learning and statistics for students and researchers of environmental data science.
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.
'As a new wave of machine learning becomes part of our toolbox for
environmental science, this book is both a guide to the latest
developments and a comprehensive textbook on statistics and data
science. Almost everything is covered, from hypothesis testing
to convolutional neural networks. The book is enjoyable to
read, well explained and economically written, so it will probably
become the first place I'll go to read up on any of these topics.'
Alan Geer, European Centre for Medium-Range Weather Forecasts
(ECMWF)
'William Hsieh has been one of the 'founding fathers' of an
exciting new field of using machine learning (ML) in the
environmental sciences. His new book provides readers with a
solid introduction to the statistical foundation of ML and various
ML techniques, as well as with the fundamentals of data science.
The unique combination of solid mathematical and statistical
backgrounds with modern applications of ML tools in the
environmental sciences … is an important distinguishing feature of
this book. The broad range of topics covered in this book makes it
an invaluable reference and guide for researchers and graduate
students working in this and related fields.' Vladimir
Krasnopolsky, Center for Weather and Climate Prediction, NOAA
'Dr. Hsieh is one of the pioneers of the development of machine
learning for the environmental sciences including the development
of methods such as nonlinear principal component analysis to
provide insights into the ENSO dynamic. Dr. Hsieh has a deep
understanding of the foundations of statistics, machine learning,
and environmental processes that he is sharing in this timely and
comprehensive work with many recent references. It will no doubt
become an indispensable reference for our field. I plan to use the
book for my graduate environmental forecasting class and recommend
the book for a self-guided progression or as a comprehensive
reference.' Philippe Tissot, Texas A&M University-Corpus
Christi
'There is a need for a forward-looking text on environmental data
science and William Hsieh's text succeeds in filling the gap. This
comprehensive text covers basic to advanced material ranging
from timeless statistical techniques to some of the latest machine
learning approaches. His refreshingly engaging style is written to
be understood and complemented by a plethora of expressive visuals.
Hsieh's treatment of nonlinearity is cutting-edge and the final
chapter examines ways to combine machine learning with physics.
This text is destined to become a modern classic.' Sue Ellen Haupt,
National Center for Atmospheric Research
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