Probabilistic Modeling and Machine Learning for Civil and Environmental Engineering

Catalog description

This course introduces Civil and Environmental Engineering (CEE) applications and solutions that can be addressed by probabilistic machine learning techniques. Students will learn CEE related foundational concepts in probability theory, data science, and statistics, explore Monte Carlo simulation solutions, and get familiar with optimization methods. Predictive modeling based on analytical/computational models and acquired data will be covered, as well as condition identification and classification techniques based on available information, and decision-making under uncertainty concepts. Several CEE applications will be analyzed and demonstrated throughout the course. Through coding exercises students will also gain practical experience, preparing them to integrate probabilistic modeling and machine learning methodologies into their professional engineering practice.