Machine Learning for Engineering Applications

Catalog description

This project-based course offers students the critical machine learning modeling skillsets for application to graduate level research in engineering disciplines. The course covers a wide ranges of ML topics ranging from basic regression and tree-based models to advanced methods such as computer vision, deep learning, graph models and reinforcement learning. A key aspect of the course is its research-focused approach. Students will apply machine learning techniques to their own graduate-level research projects and datasets, allowing them to advance their research projects while gaining experience with state-of-the-art ML modeling techniques. Example projects include: Image-based structural anomaly detection; Water quality prediction; Computer vision with remote sensing data; Causal inference of contamination; Graph model of transportation connectivity; Optimization of management decision making. By the end of the course, students are expected to have gained the skills needed to apply cutting-edge machine learning techniques in their research.