Introduction to environmental data science A comprehensive guide to machine learning and statistics for students and researchers of environmental data science. Statistical and machine learning methods have many applications in the environmental sciences including prediction and data analysis in meteorology hydrology and oceanography; pattern recognition for satellite images from remote sensing; management ofagriculture and forests; assessment of climate change; and much more. With rapid advances in machine learning in the last decade this book provides an urgently needed comprehensive guide to machine learning and statistics for students and researchers interested in environmental data science. It includes intuitive explanations covering the relevant background mathematics with examples drawn from the environmental sciences. A broad range of topics are covered including correlation regression classification clustering neural networks random forests boosting kernel methods evolutionary algorithms and deep learning as well as the recent merging of machine learning and physics. End of chapter exercises allow readers to develop their problem solving skills and online datasets allow readers to practise analysis of real data. 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 (2009 Cambridge University Press) the first single authored textbook on machine learning for environmental scientists. Currently retired in Victoria British Columbia he enjoys growing organic vegetables