EAS 5730

Machine Learning in Earth and Atmospheric Sciences

Course Description

Course information provided by the 2026-2027 Catalog.

Machine Learning in Earth and Atmospheric Sciences.

Prerequisites MATH 1710 (or equivalent), CS1110/1112 or EAS 2900 plus one additional course with a significant programming component (or equivalent experience).

Enrollment Priority Primarily for: Graduate Students. Recommended prerequisite: Linear Algebra (MATH 2310/2940 or equivalent).

Last 4 Terms Offered (None)

Learning Outcomes

• Identify the machine learning tools and techniques that are best suited for a given geoscience problem and justify their choice.
• Understand the most significant challenges in preparing geoscience datasets for machine learning and apply that knowledge to create machine-learning ready datasets.
• Train and evaluate basic supervised and unsupervised machine learning algorithms in Python using best practices for geoscience problems.
• Interpret outputs from machine learning algorithms and critically evaluate the limitations and biases of those outputs in their scientific context.
• Discuss and critically evaluate the ethical implications of machine learning solutions.

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