Civil & Environmental Engrg 590STA - MachineLearningFoundations&App

This course it introduces the theory and applications of core concepts in machine learning from an engineering perspective. Key topics include: fundamentals of data analysis and regression, classification (support vector machines, decision trees), linear model selection and assessment, flexible functional forms, decision trees and ensemble methods, support vector machines, unsupervised learning (dimensionality reduction, clustering) and neural networks for structured data, images and sequences. Applications to various engineering disciplines will be highlighted, especially in transportation, environmental, structural and industrial engineering. Hands-on programming in Python (R will also be supported) throughout the course will enable students to analyze and train models on real-world datasets. Through this course, students will learn to develop and train models on data to solve challenging engineering problems.

Open to undergraduates with a GPA of 3.0 or higher and graduate students. CEE 244, 260/MIE 273, MATH 233 Students who do not meet the minimum GPA may seek the consent of the instructor to enroll.