Gaussian Process Basics
author: David MacKay,
University of Cambridge
published: Feb. 25, 2007, recorded: June 2006, views: 210531
published: Feb. 25, 2007, recorded: June 2006, views: 210531
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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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Reviews and comments:
Amazing, must watch
crystal clear!
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.
So, is the relation between a covariance and inverse covariance like between posterior and prior distributions?
video is good, audio is poor though, hard to understand the speaker. My kingdom for a mic!
What software is he using to generate the graphics in real-time?
Very engaging and intuitive intro to gaussian processes.
Highly recommended.
Excellent lecture, spoilt by shoddy camera work!
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.
The RTMP link is rtmp://oxy.videolectures.net/video/gpip06_mackay_gpb/flv:v000/a7/u7mevpiqbufsbd2g7yglhtyjymodckhh
If that stops working see https://github.com/lmmx/devnotes/wiki...
youtube-dl is capable (2017-10) of extracting this video. The RTMP links appear to be down.
The 'medium bandwidth' .flv stream appears to end up with the best quality (VP6 Flash codec), 320x240:
youtube-dl -v -xk -f http-415 "http://videolectures.net/gpip06_mackay_gpb/"
List available formats with:
youtube-dl -v -F "http://videolectures.net/gpip06_mackay_gpb/"
A Gaussian process is a probability distribution over possible functions that fit a set of points.
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