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EAR 400 - Machine Learning in Earth and Environmental Sciences

Syracuse University
Earth and Environmental Sciences
Data Sciences
R

a 3-credit course introducing machine learning in earth and environmental sciences

Notebook Open in Colab

NoteArchived teaching materials — Spring 2025

These materials remain publicly available for reference. Enrolled students should use the syllabus and instructions for their current term.

Course description

At this end of the course, students will be able to describe various concepts of programming, apply geospatial analysis and machine learning techniques to address real-world earth and environmental science questions, and interpret model outputs. The specific learning outcomes of the course are:

  1. Read and write data using standard formats
  2. Utilize modern coding environment R to perform scientific programing
  3. Wrangle and explore data effectively
  4. Perform advanced data visualization
  5. Write efficient code to perform earth and environmental science data manipulation and geospatial analysis
  6. Apply machine learning techniques to address real-world earth and environmental science questions
  7. Design and complete a project using real-world data

This image was created with the assistance of DALL·E

Course format

The Spring 2025 offering of EAR 400 was taught in person.