This resources website contains the data files needed for some of the exercises in the book, as well as websites for video lectures, data sources, computer codes and an erratum. [The solutions to exercises are only available to instructors via the Cambridge Univ. Press website (under "Resources").]

This book is the first single-authored textbook providing a unified treatment of machine learning methods (which originated from the field of artificial intelligence) 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. This book presents machine learning methods and their applications in the environmental sciences at a level 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.

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.

---

William W. Hsieh
2009