Thank you for the lecture. In one of your lectures, you showed that Laplacian eigenmaps can be considered a special case of diffusion maps where t=0. I was told that DM supposedly is better for data that is nonuniformly sampled, but I am not sure what the implications of this statement are for common medical signals of interest (e.g., PPG, ECG, EEG). In what scenario (e.g., which kinds of signals, sampling rate, etc) would you prefer to use Laplacian eigenmaps over diffusion maps where $t eq 0$?

Great lecture, is there anywhere that we can have access to the slides?

Fortunate for awareness you have drawn to the crowd with this

Just watching... Trying to appear smart. But somehow I reckon, this is way beyond my capacity. And yet youtube keeps recommending me these things.

I am trying to learn as much as I can to apply machine learning to biological signals! Thank you for the knowledge!

The topic is cool bit please better sound

It's fantastic to see algorithms supported by theory, as it's often overlooked amidst all the current hype..

Excellent lecture. Thank you Dr.Wu

you should write a book

this channel is a hidden gem, thank you!

keep going bro

Thank you. That was very interesting. While I've been on a 15 year diversion into cryptography, I've played on-and-off with algorithms for extracting notes from music and the overlap here is clear.

I’m so glad I get recommended stuff like this by the algorithm

Hi, Dr. Wu, Thanks for your video. Could you please kindly share the code for synchrosqueezing s-transform

wow, I am currently doing nonstationary stuff reading of papers using wigner ville, such a gift!