Topic 1: Introduction to Machine Learning and Geosciences. Explore an overview of commonly used machine learning algorithms for Geosciences applications. Geological Sciences 396D (Topic 1) and 398D (Topic 1) may not both be counted. Additional prerequisite: Knowledge of basic calculus, linear algebra and statistics, and python programming; or consent of instructor.

Topic 2: Applications of Data Analysis, Visualization, and Machine Learning. Examine computational approaches to take advantage of complex data. Explore addressing real-world geoscience challenges using the Python programming language and Jupyter computational notebooks. Discuss techniques in data analysis, modeling, and machine learning for subsurface characterization applicable to resource assessment and geomorphology and stratigraphy applicable to hazard assessment. Geological Sciences 396D (Topic 2) and 398D (Topic 2) may not both be counted.

Topic 3: Data Analytics and Geostatistics. Explore data analytics and geostatistics for geosciences applications. Geological Sciences 396D (Topic 3) and 398D (Topic 3) may not both be counted. Additional prerequisite: Knowledge of basic calculus, linear algebra and statistics, and python programming; or consent of instructor.

Topic 4: Subsurface Machine Learning. Explore problem formulation, feature engineering, and inferential and predictive machine learning algorithms for geosciences applications. Geological Sciences 396D (Topic 4) and 398D (Topic 4) may not both be counted. Additional prerequisite: Knowledge of basic calculus, linear algebra and statistics, and python programming; or consent of instructor.

Topic 5: Machine Learning Research. Examine machine learning and its application to the geosciences. Study and discuss current literature and complete a capstone project applying machine learning to a research area of choice. Geological Sciences 396D (Topic 5) and 398D (Topic 5) may not both be counted. Additional prerequisite: Geosciences 396D (Topic 1) and 392P.