Gaussian Process Basics

author: David MacKay, University of Cambridge
published: Feb. 25, 2007,   recorded: June 2006,   views: 36354
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Description

How on earth can a plain old Gaussian distribution be useful for sophisticated regression and machine learning tasks?

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Download slides icon Download slides: gpip06_mackay_gpb_01.pdf (3.6 MB)


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Reviews and comments:

Comment1 Rohan Anil, December 31, 2008 at 1:15 a.m.:

Amazing, must watch
crystal clear!


Comment2 swy, June 11, 2009 at noon:

Video is very good, but can we not use rtmp? I would like to save the video so that I can watch it even when I am offline.


Comment3 zslevi, July 28, 2009 at 11:27 a.m.:

So, is the relation between a covariance and inverse covariance like between posterior and prior distributions?


Comment4 Lee, November 5, 2009 at 12:26 a.m.:

video is good, audio is poor though, hard to understand the speaker. My kingdom for a mic!


Comment5 Flávio Coelho, December 2, 2009 at 3:51 p.m.:

What software is he using to generate the graphics in real-time?


Comment6 gordon anderson, February 1, 2010 at 3:38 a.m.:

Very engaging and intuitive intro to gaussian processes.

Highly recommended.


Comment7 Tim, July 9, 2011 at 4:43 p.m.:

Excellent lecture, spoilt by shoddy camera work!


Comment8 human mathematics, January 9, 2012 at 9:57 p.m.:

A little hard to follow because he goes along without explaining why or how.

If the lecturer is reading this--I would use a ggplot geom_smooth / geom_path sort of deal rather than the video. The video is not Tufte-compliant because it shows the method more than the data.

For those having trouble viewing the videos, like me, stream the mms:// url with mplayer, that worked better for me than the site. I used mplayer -vfm ffmpeg -lavdopts lowres=1:fast:skiploopfilter=all mms://velblod2.ijs.si:80/v000/3c/hsnlqwdrqmxwtuvwnxaxx3hxibqyhld.wmv.

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