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 background equivalent to: MATH 1710; CS1110 or CS 1112 or EAS 2900.

Enrollment Information Primarily for: graduate students. Recommended prerequisites: background equivalent to: MATH 2310 or MATH 2940.

Last 4 Terms Offered 2026FA

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.

View Enrollment Information