#### Contact:

Instructor: Rina Foygel Barber (rina@uchicago.edu, office: Eckhart 113)

Class:

Office hours:

- Tue/Thu 10:30-11:50am, Eckhart 117

Office hours:

- Tue 12-2pm or by appointment

The material covered in this course will depend on student interest, & will include:

- Sparse signals & applications
- Conditions on the measurement matrix for compressed sensing; random measurement matrix
- Greedy selection & orthogonal matching pursuit
- Combinatorial group testing
- L1 minimization for sparse signal recovery
- Algorithms for L1 minimization
- Other types of sparsity: block-wise sparsity, fused Lasso, etc.
- Low-rank matrices & matrix completion
- Demixing structured signals: Robust PCA & other examples
- Applications for each topic will be presented & included in HW.

- Prerequisites: familar with linear algebra & probability theory
- Homework will be assigned every 1-2 weeks, including both theory and programming. HW assignments will include small projects using publicly available data sets. There will be no exams.
- We will use Matlab in this course (contact instructor if you don't currently have access to Matlab).

Links to many theory / algorithm / application papers: http://dsp.rice.edu/cs

Boyd & Vandenberghe, Convex Optimization. See Appendix A for a reference on norms, gradients, linear algebra, etc.

What does compressive sensing mean for X-ray CT and comparisons with its MRI application (lecture by Emil Sidky).

Boyd & Vandenberghe, Convex Optimization. See Appendix A for a reference on norms, gradients, linear algebra, etc.

What does compressive sensing mean for X-ray CT and comparisons with its MRI application (lecture by Emil Sidky).