EE 378 A : Syllabus

Here is a rough syllabus (precise schedule will depend on the progress in class, and suggestions/feedback are welcome).

Mar 28, 30

Function spaces and their bases: Hilbert; Sobolev; Reproducing Kernel Hilbert Spaces. Linear denoising. Non-parametric regression.

Apr 4, 6

Pinsker's theorem and applications. Information theoretic lower bounds.

Apr 11, 13

Wavelet bases. Nonlinear approximation.

Apr 18, 20

Compression of signals and images.

Apr 25, 27

Nonlinear denoising.

May 2, 4

Inverse problems. Compressed sensing.

May 9, 11

Bayesian approaches to inverse problems.

May 16, 18

Survey of deep learning approaches to denoising, compression, inverse problems.

May 23, 25; Jun 30, 31

Complementary topics. Discussion on projects.

2-3 homeworks will be assigned on a subset of the week. (Assigned on Monday, Due the following Monday)