Graduate seminar, University of Arizona, Lunar & Planetary Laboratory, 2025

Course Overview

Graduate seminar on applying machine learning and deep learning techniques to solve real problems in planetary science and planetary exploration.

Topics Covered

  • Fundamentals of machine learning and deep learning
  • Convolutional neural networks (CNNs)
  • Supervised and unsupervised learning methods
  • Feature detection and classification in planetary imagery
  • Time series analysis
  • Practical applications in Mars geology, astrobiology, and planetary exploration
  • Best practices for implementing ML in scientific research
  • Ethical considerations in AI/ML applications

Course Format

A combination of lectures, code demonstrations, and hands-on projects. Students work with real planetary datasets and develop ML pipelines for specific scientific questions.

Learning Outcomes

Students will gain:

  • Understanding of core machine learning algorithms and their applications
  • Practical experience implementing ML models in Python
  • Ability to apply ML techniques to real planetary science problems
  • Critical evaluation skills for AI/ML methods in research