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SC51

Machine Learning of Environmental Data. Foundations and Case Studies
Convener: Mikhail Kanevski 
Thu, 21 Apr, 08:30–12:00  / Room -2.61
Machine learning (ML) is a rapidly developing interdisciplinary approach to complex data analysis and modelling. It plays a key role in data mining in different scientific fields and in many practical applications. At present ML is widely used as an efficient tool in GI Sciences, remote sensing images processing, environmental monitoring and space-time forecasting. The short course gives an overview of ML algorithms applied for the analysis, modelling, prediction and visualization of high dimensional and multivariate environmental data. The main topics, presented within the framework of a generic methodology, include: detection of patterns and predictability, feature selection, supervised and unsupervised learning, visual analytics. Real case studies consider environmental pollution, natural hazards, renewable energy resources, topo-climatic modelling.