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

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.

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

This course provides academic credit for independent thesis research conducted under the supervision of a Georgia Tech faculty advisor.

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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.

Small groups of students undertake an industry-sponsored project under faculty guidance.