Introduction to the applications of machine learning techniques to extract information from large amounts of data available to model Earth and environmental systems. Topics include applications of neural network approaches, classification and regression of large datasets, and non-parametric spatial process modelling to improve the prediction of hydrological and hydroclimatic variables, understand water resources behaviours, represent global feedbacks between hydroclimate variables, hydrological response of a watershed after a storm, and to address hydrological scaling issues.

Prerequisite(s): AISE 3010A/B, Earth Sciences 3340A/B, Statistical Sciences 2141A/B.

Extra Information: 2 lecture hours per week, 2 lab/tutorial hours per week.