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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