Topics include modeling with networks and graphs; linear, nonlinear, and integer programming, construction of models employing modern modeling languages; and general solution strategies.

Covers modeling of discrete-event dynamic systems and introduces methods for using these models to solve engineering design and analysis problems.

Topics include modeling with networks and graphs; linear, nonlinear, and integer programming, construction of models employing modern modeling languages; and general solution strategies.

Practical analytics project experience applying ideas from the classroom to a significant project of interest to a business, government agency, or other organization.

For graduate students holding graduate research assistantships.

Topics include modeling with networks and graphs; linear, nonlinear, and integer programming, construction of models employing modern modeling languages; and general solution strategies.

Defines, classifies, and examines technology used in stand-alone sensor systems. Centers on system-engineering trades useful in designing systems of sensors and systems of systems.

Introduction to engineering economic decision making, economic decision criteria, discounted cash flow, replacement and timing decisions, risk, depreciation, and income tax.

This course focuses on analysis of high-dimensional structured data including profiles, images, and other types of functional data using statistical machine learning. A variety of topics such as functional data analysis, image processing, multilinear algebra and tensor analysis, and regularization in high-dimensional regression and its applications including low rank and sparse learning is covered. Optimization methods commonly used in statistical modeling and machine learning and their computational aspects are also discussed.