EE-614 Theory and Methods for Linear Inverse Problems

Instructor

Prof. Volkan Cevher

Description

This course is about inference from incomplete data in high-dimensional linear systems. The core topics will revolve around the following concepts:

  • Foundations of low dimensional models, such as sparsity and low-rank models
  • Convex geometry in high dimensions
  • Randomness in high dimensions
  • Convex and combinatorial optimization
  • Analysis and design of algorithms

Prerequisites

Linear algebra, probability theory, basic notions of optimization

Outline

Course outline