Foundations of data science

Introduction

High-dimensional space

Best-fit subspaces and Singular Value Decomposition (SVD)

Random walks and Markov chains

Machine learning

Algorithms for massive data problems: streaming, sketching, and sampling

Clustering

Random graphs

Topic models, non-negative matrix factorization, hidden Markov models, and graphical models

Other topics

Wavelets

Background material.