CE 548 — Machine Learning and Artificial Intelligence Applications in Transportation Systems

Course Description

A three-credit elective introducing machine learning and AI fundamentals and their applications to transportation, including road safety, micromobility, and sustainability. The course combines exploratory data analysis, regression, classification, and clustering with Python implementation and a team project using real transportation data.

Learning Outcomes

• Recognize machine learning and AI fundamentals and transportation applications.
• Compare algorithms and explain their strengths and limitations.
• Evaluate datasets and select appropriate algorithms.
• Implement algorithms in Python using real data with autonomy and responsibility.
• Collaborate professionally and uphold scientific integrity.

Main Topics

Python fundamentals, linear and logistic regression, decision trees, boosting and random forests, support vector machines, neural networks, clustering, PCA dimensionality reduction, and algorithm diagnostics.

The original course syllabus is attached below.

In-person office hours and schedule | Courses and supervision