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Master in Environmental Geoscience (MSc GSE) Environmental Data Mining Contact persons: Mikhail Kanevski ([email protected]) and Michael Leuenberger ([email protected]) Context Machine Learning (ML) is a rapidly developing interdisciplinary approach to complex data analysis, modelling and visualization. It plays an extremely important 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 main framework of Environmental Data Mining belongs to the interface between the raw data of environmental phenomenon and the elaboration of new knowledge for intelligent decision-‐making process. Objectives and Methods This includes, but is not limited to: -‐Development and application of a methodology -‐Construction and analysis of high dimensional feature space -‐Modelling of spatial pattern with simulated and real data -‐Uncertainties quantification and assessment -‐Visual analytics of results and residuals Case studies: natural hazards and risks, renewable resources assessment, pollution of the environment, etc. Literature -‐Kanevski, M., Pozdnoukhov, A., Timonin, V., 2009. Machine Learning for Spatial Environmental Data. EPFL Press. -‐Hastie, T., Tibshirani, R., Friedman, J., 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd edition). Springer. WEBSITES http://www.unil.ch/idyst/home/menuinst/research-‐ poles/geoinformatics-‐and-‐spatial-‐m/geomatics-‐and-‐ geostatistics.html https://sites.google.com/site/lorisforesti/projects/geokernels 08.01.16
