Proposal Description

This work looks at AI, Machine Learning and Statistics in the Environment. As computational methods become more and more present in our daily lives and research agendas, how can we use these methods to complement each other. AI is very good with complex high dimensional data however does not give insight in to the process that generates the data, Machine Learning is very good at fitting a set of data but often does not address uncertainty. Statistics is based on a solid foundation of probability which makes it the natural candidate to quantify uncertainty. As Environmental data is often complex high dimensional, requires good predictions and uncertainty needs to be assessed, learning how we can combine these methods to create models that can help us make critical decisions is important.