Table of Contents:

* High Dimensional Space

* Best Fit Subspace & SVD

* Random Walks & Markov Chains

* Machine Learning

* Massive Data: Streaming, Sketching, Sampling

* Clustering

* Topic Models, Hidden Markov Process, Graphical Models, and Belief Propagation