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Data Representations

CS 6344

Erik Jonsson School of Engineering and Computer Science

Useful representations of data for efficient manipulation and visualization. These include, among others Dimensionality Reduction, Clustering, Euclidean Embedding, Graph Embedding, and Discriminant Functions. Techniques covered include principal component analysis (PCA), singular value decomposition (SVD), clustering, and various randomized techniques. Special emphasis is given to the performance of these techniques on big data. 3 credit hours.

Prerequisite: CS 5343.

Offering Frequency: Each year

Grades: 309

Median GPA: A-

Mean GPA: 3.478

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