Introduces mathematical, algorithmic, and statistical tools needed to analyze geometric data and to apply geometric techniques to data analysis, with applications to computer graphics, machine learning, computer vision, medical imaging, architecture, and other fields. Potential topics include: applied introduction to differential geometry; discrete notions of curvature; metric embedding; geometric PDE via the finite element method (FEM) and discrete exterior calculus (DEC); computational spectral geometry and relationship to graph-based learning; correspondence and mapping; level set methods; descriptors; shape collections; optimal transport; and vector field design. (catalog listing)
Time: Tuesday/Thursday, 2:30pm-4pm
Location: 2-105
Instructor: Justin Solomon, office hours Wednesdays 10am-12pm (32-D460)
TA: Sebastian Claici, office hours Monday/Wednesday 3pm-4pm (32-D475A)
In this offering of 6.838, Justin is attempting to cover at least the first half of the class with written lecture notes. These notes are being written on the fly, and hence there is high likelihood that (1) they contain typos and (2) they will change frequently through the course of the semester. Please get in the habit of hitting "refresh" before reading them, and avoid printing these notes to save paper. Please post errata and suggestions on Piazza; Justin promises not to be offended by constructive criticism. Here is the link: Geometry course notes
The following is a highly tentative lecture schedule for 6.838. It will be updated dynamically as the course proceeds. The list of topics is ambitious and likely to be shortened; if there are topics you feel strongly should be included/emphasized/added, feel free to contact Justin with this information.
Links to slides, Youtube videos of lectures, and homeworks will be posted on this spreadsheet as the course proceeds.