Explore the theory and practice of data analytics and machine learning for subsurface resource modeling. Examine fundamental probability and statistics; data preparation and feature engineering, inference (clustering, multidimensional scaling) and prediction (regression, naive Bayes, decision trees, random forest, support vector machines and artificial neural nets); and model selection, training, testing, tuning, and validation. Petroleum and Geosystems Engineering 383 (Topic: Subsurface Machine Learning) and 383 (Topic 63) may not both be counted. Three lecture hours a week for one semester.