Machine Learning for Earth System Sciences

Por: Swayam . en: , ,

Week 1: Recap of probability, spatio-temporal statistics (autoregression, geostatistical equation, Gaussian Processes, Extreme value statistics) Week 2:Recap of relevant Machine Learning and Deep Learning techniques (Bayesian Networks, CNN, RNN/LSTM, VaE, Interpretability, Causality) Week 3:Earth System Process Understanding: case studies (predictors of monsoon, extreme weather forecasting, climate change visualization) Week 4:Earth System Process Understanding: case studies(Extreme event analysis, networks and teleconnections, causal analysis) Week 5:Earth System Process Understanding: case studies(Extreme event analysis, networks and teleconnections, causal analysis) Week 6:Earth System Process Understanding: case studies(Extreme event analysis, networks and teleconnections, causal analysis) Week 7:Earth System Modeling: relevant concepts (Model structures, modeling challenges, model validation, data assimilation) Week 8:Earth System Modeling: applications in different domains (ML-based surrogate models, deep and shallow generators, long-term forecasting)

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