3.0

credits

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Introduces advanced AI methods for analyzing large-scale geospatial data, with particular emphasis on geostatistics. Starts with an overview of geospatial data and exploratory data analysis and visualization techniques. Delves next into Gaussian Processes (GP) and kriging for spatial data modeling, covering both classical optimization and Bayesian MCMC methods for GP models. Covers spatial graphical models and Nearest Neighbor Gaussian Processes (NNGP) for handling massive datasets. Introduces machine learning techniques, including random forests and neural networks (multi-layer perceptrons, convolutional and graph neural networks), and explores hybrid methods that combine traditional statistical modeling with machine learning. Covers various state-of-the-art computational techniques, like stochastic approximations and variational Bayesian optimization, and offers a hands-on demonstration of analysis of big spatial data using R and Python.