Supervised Machine Learning Readiness

This learning series uses a combination of step-by-step frameworks, exploratory widgets, and low-code exercises in Jupyter Notebooks to explore the full cycle of supervised machine learning model development.

This work was supported by the US National Science Foundation under CyberTraining award #2319979

  • Leading the program as Principal Investigator (PI)

    Learning strategy

    Instructional design and development

    Graphic design

    Video creation

    GIS map creation

    Assessment design

    Program evaluation

  • Articulate Rise + Mighty!

    Jupyter Notebooks

    Javascript

    Camtasia

    Adobe Illustrator

Machine Learning Foundations

Module 01

No-code conceptual introduction to supervised machine learning in the Earth Systems Sciences. Learners explore problem framing, data handling, and model development concepts with support from no-code widgets.

Machine Learning Applications

Module 02

Low-code exploration of concepts in Module 01. Learners create and test hypotheses using Jupyter widgets and pre-populated code. Learners work with real-world data and test the effect of various preprocessing and training strategies.

Machine Learning Analysis

Module 03

Team-based lab module. Groups of students select a scenario to create and execute a machine learning strategy for. Groups must justify the choice of data, model, and other decisions made for their scenario.