Welcome!
Welcome to the course materials for EDS 232 - Machine Learning for Environmental Science! In this website you will find all the materials for the Spring 2026 term. This course is part of the UCSB Masters in Environmental Data Science.
Course description
Machine learning can help process big/complex data and extract knowledge. It forms one of the foundations in data science. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning (decision tree, random forest, support vector machines, neural networks) and unsupervised learning (clustering, dimensionality reduction, deep learning). Problems and exercises are framed within environmental science applications. The course will be taught using Python.
Teaching team
Instructor
Carmen Galaz García (she/her/hers)
- E-mail: c_galazgarcia@ucsb.edu
- Student hours: Thursday 2 pm - 3 pm @ Bren Hall 4424
Co-instructor
Annie Adams (she/her/hers)
- E-mail: aradams@ucsb.edu
- Student hours: Tuesday 11 am - 12 pm @ Bren Hall 3418
Syllabus
Calendar
The following is our tentative calendar. The course content and calendar may be subject to change as the course progresses.
Contribute
📝 If you have suggestions on how to correct, improve, or expand these course materials, please feel free to email the course instructor at c_galazgarcia@ucsb.edu or file a GitHub issue.
🌟 If these materials have been useful to you, consider adding a star to the project’s repository!