CEE 2350 - MACHING LEARNING IN INFRASTRUCTURE ENGINEERING

Minimum Credits: 3
Maximum Credits: 3
This course covers theory and practical algorithms in machine learning from a variety of perspectives, with applications to solve problems in civil infrastructure engineering. The topics include linear methods for regression and classification, regularization, kernel smoothing methods, bayesian inference, sampling, decision tree learning, support vector machines, statistical learning methods, unsupervised learning and deep learning, as well as their field applications. This course is designed to give graduate-level students a thorough grounding in methodologies, technologies and algorithms in machine learning and push field applications in, but not limited to, civil infrastructure engineering.
Academic Career: Graduate
Course Component: Lecture
Grade Component: Grad Letter Grade

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