Analyze the counterintuitive geometry and concentration properties of high-dimensional spaces and unit balls
Fit spherical Gaussian models to data and generate random points in high dimensions
Apply the Singular Value Decomposition and best rank-k approximations to matrix and data problems
Use the power method and relate singular vectors to eigenvectors for computation
Model processes with random walks and Markov chains, including MCMC sampling and stationary distributions
Connect random walks to electrical networks and compute hitting, commute, and cover times; apply PageRank
