Course description:
In this course we will review the recent
advances in high-dimensional statistics. We will cover concepts in
empirical process theory, concentration of measure, and random matrix
theory in the context of understanding the statistical properties of
high-dimensional estimation methods. In this discussion we will also
overview the computational constraints that are involved with solving
high-dimensional problems and touch upon concepts in convex
optimization and online learning.