Machine learning is a major sub-field in computational intelligence (also called artificial intelligence). Its main objective is to use computational methods to extract information from data. Machine learning has a wide spectrum of applications including handwriting and speech recognition, object recognition in computer vision, robotics and computer games, natural language processing, brain–machine interfaces, medical diagnosis, DNA classification, search engines, spam and fraud detection, and stock market analysis. Neural network methods, generally regarded as forming the first wave of breakthrough in machine learning, became popular in the late 1980s, while kernel methods arrived in a second wave in the second half of the 1990s.

In the 1990s, machine learning methods began to infiltrate the environmental sciences. Today, they are no longer an exotic fringe species, since their presence is ubiquitous in the environmental sciences, as illustrated by the lengthy References section of this book. They are heavily used in satellite data processing, in general circulation models (GCM) for emulating physics, in post-processing of GCM model outputs, in weather and climate prediction, air quality forecasting, analysis and modelling of environmental data, oceanographic and hydrological forecasting, ecological modelling, and in monitoring of snow, ice and forests, etc.

This book presents machine learning methods (mainly neural network and kernel methods) and their applications in the environmental sciences, written at a level suitable for beginning graduate students and advanced undergraduates.