Differential geometry classically is a backbone of theoretical physics such as Einstein's theory of general relativity.
Nowadays, differential geometric methods become important techniques for developing novel algorithms in
big data analytics and computer vision. In this course, we will cover both classical
differential geometry and their applications in computer vision.
Key topics include curves, surfaces, manifolds, Riemannian metrics, differential forms, Statistical Manifold,
Gauss curvature; isometries, covariant differentiation, parallel transport, geodesics with application to physics and
computer vision. This course is intended for majors in physics or mathematics or Math-CS majors who aim for
becoming research scientists.
Additionally, another goal of this course is to become
comfortable using Amazon Web Services and GitHub as
these tools are extremely prevalent in industry
and academia when developing and deploying models. To
that end, all code for your midterm and final projects
will be hosted on GitHub.