Learning with Gaussian Processes
author: Carl Edward Rasmussen,
Max Planck Institute for Biological Cybernetics, Max Planck Institute
published: Feb. 5, 2008, recorded: January 2008, views: 4434
published: Feb. 5, 2008, recorded: January 2008, views: 4434
You might be experiencing some problems with Your Video player.
Slides
Related content
Report a problem or upload files
If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Description
This presentation describes the basic foundations and advanced theory of Gaussian processes.
See Also:
Launch in a standalone WM Player
Switch to Windows Media Player
Download slides:
epsrcws08_rasmussen_lgp_01.pdf (1.1 MB)
Link this page
Would you like to put a link to this lecture on your homepage?Go ahead! Copy the HTML snippet !









Reviews and comments:
This is an awesome lecture. I love it. I learned a lot about probability distributions over functions.
Excellent lecture indeed! Highly recommended for those who want to learn about gaussian process. Well, I think I will create an account here to put yet another star for this video lecture. Thanks, Prof. Rasmussen!
very good lecture. helped me a lot. gaussian processes and bayesian inference are presented in a very clear way. this is the best introductions to these tricky subjects that i have ever come across.
however, the videographer should be shot.
Great! I really enjoy this lecture. Thanks.
great lecture. thanks!
Who is the idiot behind the camera. It seems it is the same fool who captured the tutorial on deep learning.
I just shouted at my monitor because I couldn't see where He was pointing, a bad sighn for my mental sanity.
Hi
I need the video lecturer series of Pattern Recognition and Pattern Classification How to get this if so please help me
thanking
aravinda
Write your own review or comment: