This graduate course builds a fundamental understanding of data-science concepts and hands-on skills in Python for analyzing datasets across civil, mechanical, electrical, computer, and manufacturing engineering as well as computer science. By the end of the term, students can frame a data-driven problem, select appropriate methods, assemble and analyze a dataset, and communicate evidence-based conclusions. Topics covered are:
Applied mathematics; probability & information theory
Machine learning basics; introduction to Python programming
Supervised learning: regression, k-nearest neighbors, decision trees & ensemble methods, support vector machines
Unsupervised learning: clustering analysis, principal component analysis (PCA)
Neural networks & an introduction to deep learning
Network science (applications to engineering datasets)
Assessment (project-based): A term project with staged milestones (topic & dataset pitch, interim presentation, final presentation, and written report), preferably using data from the student’s own research.
This course aims to introduce graduate students to conceptual underpinnings of methods applied in transportation decision-making, planning, and modeling as well as implications of the methods on the real-world transportation-related problems. In particular, this course provides an introduction to advanced concepts of transportation mathematical modeling (including statistics and optimization), trip-based models for transportation demand analysis and forecast (including trip generation; trip distribution; modal split; and trip assignment), and tour- and activity- based models.
With the aim of introducing students to transportation engineering within the broader field of civil engineering, this course is redesigned to prepare students for gaining fundamental knowledge on principles of transportation engineering and traffic analysis. Through learning both theory and practice, students learn to apply the principles and the approaches to solve real-world transportation-related problems. The topics coverev are:
Introduction to transportation and traffic engineering
The role of transportation and traffic engineering in economy and society
Highway traffic theories
Highway capacity analysis
Traffic control systems
Introduction to transportation planning and travel demand analysis and forecast
This course introduces senior students to both theory and practice in highway engineering and pavement design. In particular, the topics covered in this course are:
Transportation and highway engineering within the broader field of civil engineering
Fundamentals of highway system components: Vehicles, roads, and road users
Highway geometric design
Highway earthwork volumes
Highway pavement design
Pavement management for maintenance and rehabilitation of highway pavement systems