Topics include component reliability and lifetime distributions, hazard rates, system reliability, covariate reliability models, state-dependent models, nonparametric models, statistical estimation and inference, maintenance management and repair processes.

An introduction to basic stochastic processes such as Poisson and Markov processes and their applications in areas such as inventory, reliability, and queueing.

Undergraduate research conducted under the guidance of a faculty mentor. This course is a graded academic credit research course.

This course integrates decision and data analytics to solve real-world problems. It includes hands-on system modeling, data collection and analysis, and reporting writing projects.

Independent doctoral research under faculty supervision leading to the development and completion of the dissertation

Topics include neural networks, support vector machines, classification trees, boosting and discriminant analyses. Intended for Ph.D. students and those seeking the M.S. in Statistics.

Theoretical/computational foundations of analyzing large/complex modern datasets, including the fundamental concepts of machine learning and data mining needed for both resesarch and practice. Crosslisted with CSE 6740. Credit not awarded for both ISYE 6740 and CS 4641/7641/CSE 6740.