Love these lectures, thank you!

Dear Professor, We cannot thank you enough for sharing your knowledge with us, especially in a way that we, as non-experts, can get it. Your Python codes makes the lessons immediately usable for our daily problems. I wish you would one day, extend it to the numerical solutions for the Maxwell equations to simulate S-parameters for an interconnect structure consisting of power ports.

It's awesome what you can do with linear algebra. Thank you for the explanation

I wish i have such a lightboard with good acoustics. Simply the best way for teaching theory and math.

Awesome, brilliant 👏

Brilliant as always! Thanks!

these videos are so good

Thank for the lecture, one question: the algorthims that you implemented in python with the L1 norm is called basic pursuit rigth?

Very cool stuff from a TAMS graduate! ha - all the best to you Dr. B.

could you please do a lecture on curvelets. Thanks!

deal steve, love your book, but in your book page 22, formel 1.26 , you may means B=X- avg(X ) ?

Awesome videos. Thank you so much for your effort. But the python and Matlab links on the website direct me to this video again. How can I reach the codes?

Teacher, can you make the notebooks of this chapter available? This topic is very interesting

its all coming together..

and on page 23. Code 1.10 added the code für PCA coodinate would be helpful, otherwise we love you, greeting from germany :)

I'm not sure if this question make sense, but if s corresponds to the Fourier transform of x, would that mean that Psi technically corresponds to the inverse of the Fourier basis? If Psi corresponds to the Fourier basis, then applying Psi to a vector would technically correspond to taking its Fourier transform.

Why is s=(Theta+)y called L2 solution? I thought there should be an extra L2 norm (||s||_2) to penalize before it can be called L2 solution. Am I missing something?

How exactly does the L1 and the L2 norm make the difference in the values and nature of X? I would really like to know that. Exactly what happens and how- that is my question.

Hi Steve can you please do a series on time series analysis?

According to the histogram, there are many very small, but still not zero solution components for vector s after the L1 minimization. Are those tiny entries rounded to 0 to obtain a truly sparse solution vector (denote it by ss)? If done so, is it always guaranteed that the Theta*ss - y is small? I ask it because it reminds me of the process when a mixed integer linear programming is replaced by a mixed linear programming problem and the corresponding optimum can largely differ.

I think it would be more instructive if you actually generated y from some sparse vector

Very good lectures but I have one critisim. there are many repetition during the class so makes difficult to follow. You have already explained y, theta and x. So you can directly go on.

Why didn't you just use Lasso regression? Much faster