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1 - 3 of 3 results for: CME103

CME 251: Geometric and Topological Data Analysis (CS 233)

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.
Terms: Spr | Units: 3
Instructors: Guibas, L. (PI)

CS 233: Geometric and Topological Data Analysis (CME 251)

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.
Terms: Spr | Units: 3
Instructors: Guibas, L. (PI)

CS 348A: Computer Graphics: Geometric Modeling & Processing

The mathematical tools needed for the geometrical aspects of computer graphics and especially for modeling smooth shapes. The course covers classical computer-aided design, geometry processing, and data-driven approaches for shape generation. Fundamentals: homogeneous coordinates and transformation. Theory of parametric and implicit curve and surface models: polar forms, Bézier arcs and de Casteljau subdivision, continuity constraints, B-splines, tensor product, and triangular patch surfaces. Subdivision surfaces and multi-resolution representations of geometry. Surface reconstruction from scattered data points. Geometry processing on meshes, including simplification and parametrization. Deep neural generative models for 3D geometry: parametric and implicit approaches, VAEs and GANs. Prerequisite: linear algebra at the level of CME103. Recommended: CS248.
Terms: Win | Units: 3
Instructors: Guibas, L. (PI)
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