Fundamental theoretical tools for analyzing online methods, algorithmic techniques for developing computationally efficient methods, applications to real-world problems, and discussion around ethical and legal issues.

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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.

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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.

Theory, algorithms, and applications of linear programming. Topics include the simplex method and resolution of degeneracy, duality and sensitivity analysis, basis factorization, the dual and revised simplex methods, and geometry of polyhedra. Intended for Ph.D. students.

This course aims to develop a comprehensive understanding of nonparametric statistical methods and their applications in data analysis. Students will learn to apply distribution-free techniques, including rank-based methods, density estimation, and resampling approaches such as the bootstrap, to analyze real-world data.