Article Source
Machine Learning on Geometrical Data
Schedule
Unit 1: Theories of Geometry
1/8

Introduction  overview of the course, logistics, introduction to deep learning 
1/10

Numerical Methods  linear system, optimization. slides credit: Prof. Justin Solomon from MIT 
1/15

Curves  curve theory, Frenet frame. slides credit: Prof. Justin Solomon from MIT. Reference: Intro to DG, Ch2 
1/17

Surfaces, First Fundamental Form  surface theory, first fundamental form. slides credit: Prof. Keenan Crane from CMU. References: Intro to DG, Ch3, 4, 5 
1/22

Second Fundamental Form  second fundamental form, gaussian curvature. slides credit: Prof. Keenan Crane from CMU. References: Intro to DG, Ch3, 4, 5 
1/24

Theorema Egregium and GaussBonnet  intrinsic geometry, theorema egregium, euler characteristic, angle excess theorem, gaussbonnet theorem. References: Intro to DG, Ch6 
1/29

Geodesics  theories of geodesic, fast marching algorithm 
1/31

Laplacian Operator  divergence on manifolds, heat equation, laplacianbertrami operator, harmonic functions. reference: Analysis on Manifolds via the Laplacian 
2/5

Laplacian and Applications  laplacian graph theory and practice 
2/7

Data Embedding  classical embedding theorems and typical algorithms. reference: Note 
2/12

Highdimensional Geometry  some odd behaviors in highdimensional geometry. reference: Ch 2 