Environmental Statistics and Learning

4.0 credits · Graduate

This course introduces statistical and machine learning techniques for analyzing complex datasets in the environmental sciences and engineering. It focuses on supervised learning methods, such as regression, decision trees, and neural networks, applied to real environmental data, with emphasis on both prediction and inference. Designed for students with basic statistics knowledge, the course provides a practical foundation in applied statistics and machine learning. It includes review of key mathematical and coding concepts needed to implement these tools. The goal is to build a toolbox of methods not covered in introductory courses and to help students understand when and why to use each method. Learning is hands-on, with in-class programming exercises that translate theory into application using real-world datasets.