Explore basic frequentist and Bayesian probability and statistics, confidence and significance models, multivariate and spatial correlated features, feature engineering, spatial debiasing and estimation, stochastic simulation, uncertainty modeling, basic machine learning, and optimum decision making in the presence of uncertainty. Examine applications to spatial problems such as geology and subsurface resource development.