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. 

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 studies under the direction of Professor Benoit Montreuil in the Physical Internet and hyperconnected supply chain and logistics engineering domains. The scope and direction of the studies are determined by the student (or small group of students) in consultation with Professor Montreuil.

Topics include Real Analysis (countable sets, basic topological notions, sequence, series, continuity), Basic Linear Algebra (linear transformations, vector spaces, singular value decomposition), and writing mathematical proofs.

Overview of basic tools used in statistical analysis and modeling. Credit not allowed to students seeking a degree in ISYE.

An introduction to deterministic optimization methodologies including approaches from linear, discrete, and nonlinear optimization including algorithms and computations. Applications will be introduced as appropriate.

Independent research conducted under the guidance of a faculty member.