Selection, evaluation, and design of environmental and water resources engineering systems.

Graduate level course on reading and analyzing current literature in the field of hydrology and water resources.

Theory and applications of core concepts in data mining and machine learning from an engineering perspective. Key topics: fundamentals of data analysis, regression, unsupervised learning (clustering, dimensionality reduction, etc), classification (support vector machines, decision trees). Model assessment & inference, additive models and neural networks will also be covered, with a big data focus. Applications to various subdisciplines will be highlighted, especially in transportation, environmental, structural and industrial engineering.

Fundamentals of traffic flow including its characheristics and their relationships; Mathematical models that describe traffic flow dynamics at multiple levels of detail; Solutions and applications of these models that capture traffic flow phenomena such as congestion and queue dissipation. Prerequisites: CE-ENGIN 310 or 411 or 511 or equivalent.

Visiting lecturers. Research presentations by graduate students and faculty. Discussion of current transportation topics.

Research presentations by graduate students and faculty. Visiting lecturers.

Research presentations by graduate students and faculty. Visiting lecturers

Presentations by the graduate student of selected current literature and research. Visiting lecturers.

Research carried out and reported under supervision of student's adviser as partial fulfillment of requirements for Master's degree in civil engineering or Master's degree in environmental engineering. May not be taken by those taking CE-ENGIN 679 Engineering Report or CE-ENGIN 699 Master's Thesis.