Civil and Environmental Engineering
Assistant Professor
Member, Ken Kennedy Institute
Average rating
4.0
4 temporary mock ratings
Difficulty
2.4
course-linked average
Courses
2
in seeded sections
• Stochastic and deterministic surrogate modeling • Statistical machine learning • Multi-hazards resilience assessment • Regional risk evaluation • Random vibrations • Multi-fidelity analysis and dimension reduction methods
CEVE 348
Machine learning (ML) technologies are transforming science and engineering. This course is intended to provide CEE students with their first exposure to scientific programming and ML. In the class, students will learn different statistical and machine learning methods and practice implementing them using Python and real-world datasets. The examples are drawn from data and applications within civil and environmental engineering, including structural, transportation, hydrology, smart materials, air and water quality, among others. The methods cover introductory supervised and unsupervised learning methods. This class will provide students with a solid foundation in data analytics and insights into how ML is driving change across the CEE landscape.
CEVE 800
Repeatable for Credit.
