BEE 6311

Environmental Statistics and Learning II

Course Description

Course information provided by the 2026-2027 Catalog.

This course builds on concepts introduced in BEE 4310/6310 Environmental Statistics and Learning but does not require prior enrollment. It examines advanced statistical and machine learning methods for complex environmental datasets. Topics include unsupervised and supervised learning, such as clustering (hierarchical, k-means, Gaussian mixture models, DBSCAN), principal component analysis, k-nearest neighbors, Gaussian process regression, support vector machines, and neural networks. Students are introduced to explainable AI and deep learning concepts, including autoencoders, recurrent and long short-term memory networks, and convolutional neural networks. Applications span environmental science and engineering, emphasizing pattern recognition, prediction, and system understanding. The course prioritizes practical implementation, critical evaluation, and interpretation of results over black-box use, with hands-on programming and literature-based analysis.

Prerequisites background equivalent to: CEE 3040 or ENGRD 2700 or BTRY 3010/STSCI 2200.

Last 4 Terms Offered (None)

Learning Outcomes

• Apply advanced statistical and machine learning techniques in modern programming languages.
• Interpret and communicate analyses of data to support discovery, critically evaluate scientific literature, and advance solutions in environmental fields.

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