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