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.

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

This course is about enabling students to contribute professionally and/or scientifically through the next decades to the systemic design, engineering, analysis, planning, testing, implementation, operation, adaptation, and optimization of supply chain facilities and networks of such facilities. The course covers supply chain facilities such as logistic, deployment (e.g. warehousing), realization, and customer interface centers. Learning is aimed to be applicable in a wide variety of supply chain types, notably spanning e-commerce, healthcare, manufacturing, and retailing, concurrently subject to performance goals such as agility, efficiency, responsiveness, resiliency, scalability, and sustainability.

Courses in special topics of timely interest to the profession conducted by resident or visiting faculty.

This course provides academic credit for independent thesis research conducted under the supervision of a Georgia Tech faculty advisor. The course does not involve regular class meetings, assignments, or examinations. The scope and direction of research are determined by the student in consultation with the thesis advisor, consistent with the requirements of the degree program.

This course provides academic credit for independent thesis research conducted under the supervision of a Georgia Tech faculty advisor. The course does not involve regular class meetings, assignments, or examinations. The scope and direction of research are determined by the student in consultation with the thesis advisor, consistent with the requirements of the degree program.

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.

Simple and multiple linear regression, inferences and diagnostics, stepwise regression and model selection, advanced regression methods, basic design and analysis of experiments, factorial analysis.