Machine Learning Analysis in the Earth Systems Sciences
In this module, you are tasked with planning, implementing, and evaluating a machine learning solution for a real-world scenario. Given pre-configured code blocks and prepared data, you will create a problem statement, explore the data, experiment with model development, and ultimately make a recommendation on the utility of machine learning for your scenario.
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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
- Develop a clear and comprehensive problem statement for a given machine learning scenario, considering the goals, constraints, and context of the analysis
- Summarize the characteristics of a provided dataset
- Summarize and justify the decisions made during the processes of model development, including feature selection and intermediate evaluation metrics
- Evaluate the model's performance in solving the initial problem, and recommend potential refinements or improvements for future iterations
Suggested Prerequisite Skills
- Essential Digital Competencies
- Completion of Machine Learning Foundations in the Earth Systems Sciences
- Completion of Machine Learning Applications in the Earth Systems Sciences
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
Jupyter Notebook
Duration
1 hour
Technical Requirements
For classroom support, you may request a JupyterHub from NSF Unidata.
Last Update
March 2025