Machine Learning Foundations in the Earth Systems Sciences
This self-paced module is designed to guide you through the very basics of supervised machine learning in the Earth Systems Sciences. You will discover how machine learning is used by scientists, the process for supervised machine learning model development, how data plays a crucial role in making good predictions, and how to be an effective and ethical user of machine learning tools. You will also learn that machine learning is not a catch-all solution to every problem!
You won't be expected to have any programming skills to complete this module. Through simple schematics and graphs, you will be guided through the conceptual process for developing and using supervised machine learning for science. Expect to leave this module with the skills to assess the suitability of supervised machine learning for new problems and question supervised machine learning analyses you encounter.
This notebook is part of a series: Machine Learning in the Earth Systems Sciences
Details
Learning Series
Machine Learning in the Earth Systems Sciences
Intended Audience
- Earth Systems scientists
- New and aspiring machine learning users
- Graduate and upper-level undergraduate students
Learning Objectives
- Define machine learning in terms of its goals or purpose
- Explain the steps of a general supervised machine learning analysis
- Distinguish scenarios that are and are not appropriate for supervised machine learning analysis
Suggested Prerequisite Skills
Author
NSF Unidata
In partnership with Metropolitan State University of Denver
This work was supported by NSF Unidata under award #2319979 from the US National Science Foundation.
Format
Self-Paced eLearning
Duration
1 hour
Technical Requirements
Last Update
March 2025