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CS 233

Geometric and Topological Data Analysis (CME 251)

  • Not Offered

3 units

Letter or Credit/No Credit

Mathematical and computational tools for the analysis of data with geometric content, such images, videos, 3D scans, GPS traces -- as well as for other data embedded into geometric spaces. Global and local geometry descriptors allowing for various kinds of invariances. The rudiments of computational topology and persistent homology on sampled spaces. Clustering and other unsupervised techniques. Spectral methods for graph data. Linear and non-linear dimensionality reduction techniques. Alignment, matching, and map computation between geometric data sets. Function spaces and functional maps. Networks of data sets and joint analysis for segmentation and labeling. Deep learning on irregular geometric data. Prerequisites: discrete algorithms at the level of CS161; linear algebra at the level of Math51 or CME103.

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