Introduction to High Dimensional Data Analysis


Geometry of high dimensional data sets. Linear dimension reduction, principal component analysis, kernel methods. Nonlinear dimension reduction, manifold models. Graphs. Random walks on graphs, diffusions, page rank. Clustering, classification and regression in high- dimensions. Sparsity. Computational aspects, randomized algorithms. Prerequisite: MATH 218 or 221. Instructor: Staff

Additional Notes

Usually offered Fall semesters

  • COMPSCI445
Curriculum Codes