BEE 6310

Environmental Statistics and Learning

Course Description

Course information provided by the Courses of Study 2024-2025.

This course introduces relatively simple but powerful data analysis techniques needed to analyze and model complex datasets frequently encountered in the environmental sciences. The course covers both supervised and unsupervised learning techniques, including linear regression, penalized regression, generalized linear models, local regression, and principal component analysis. These topics are introduced through applications to data from various environmental fields. The course serves as a first course in applied statistics and machine learning for students with only a basic knowledge of probability and statistics, and will provide a review of the mathematical concepts needed to understand the techniques presented. Students will learn by doing, with ample time in class to practice translating theory to application through programming exercises on real environmental datasets.

When Offered Fall.

Prerequisites/Corequisites Prerequisite: CEE 3040 or ENGRD 2700, or permission of instructor.

Course Attribute (CU-SBY)

• Apply supervised and unsupervised learning techniques in modern programming languages.
• Interpret and communicate statistical analyses of data to support scientific discovery and advance engineering solutions in environmental fields.

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