Introduction to Compressed Sensing
Instructor: Prof. HeungNo Lee
Email: heungno@gist.ac.kr
This course was offered in the Spring semester 2011 at GIST.
Here is the lecture note Book_CS.pdf for this course.
Here is the presentation of professor HeungNo Lee presented at PSIVT 2011 Overview of Compressed Sensing 
Course Syllabus
1  Introduction to Compressive Sensing, Shannon Nyquist Sampling Theorem


2  Comparison of L0, L1, L2 solutions, application of sparse representation theory in filter array based spectrometers  HW#1 
3  Compressive Sensing Theory: L0 and L1 equivalence, The Spark = Dmin of parity check matrix, The Singleton bound, Givens Rotation based Matrix Design  
4  Compressive Sensing Mathematics: Generalized Uncertainty Principle, Sparse Representation, conditions for the unique ell0 solution, and the unique ell1 solution, the Donoho approach

HW#2 
5  Compressive Sensing Mathematics: conditions for the ell0 solution, and the unique ell1 solution, the CandesTao approach.


6  Compressive Sensing Mathematics: Sensing matrices and oversampling factors  HW#3 
7  Stable Recovery  
8  Midterm Exam  
9  Recovery Algorithm I : Homotopy, LASSO, LARs , OMP.  HW#4 
10  Recovery Algorithm II : ell1 minimization , SOCP, Message Passing Algorithm s  
11  Class Presentations #1/#2/#3  HW#5 
12  Connection to the Shannon TheoryClass Presentations #4/#5/#6  
13  The Rate Distortion TheoryClass Presentations #7/8/9  HW#6 