Breaking News: All assignments are due by Wednesday, June 6. The class final is Friday, June 8, 3:30  5:00 pm in 380380C. See Piazza for details.  The goal of this course is to cover the rudiments of geometric and topological methods that have proven useful in the analysis of geometric data, using classical as well as deep learning approaches. Even nongeometric data, e.g. social networks or genomic microarrays, are often best analyzed by embedding them in a multidimensional geometric feature space. While great strides have been made in applying machine learning to image and natural language data, extant techniques rely heavily of the data being presented in regular array formats. Geometric and topological data, on the other hand is typically in less regular forms, such as for example point clouds or simplicial complexes, requiring novel ideas and methods in both the supervised and unsupervised settings.
Extant annotated visual data repositories, such as Imagenet and Shapenet, will be covered and used. The course also aims to show that there can be multiple perspectives or views on the same data, and that a particular piece of data is often best understood not alone but within a "social network" of related data sets that provide a useful context for its analysis.
These pages are maintained by Leonidas Guibas guibas@cs.stanford.edu.
