Graphical Models and Variational Methods
author:Christopher Bishop,
Microsoft Research
published: Feb. 25, 2007, recorded: September 2004, views: 2919
published: Feb. 25, 2007, recorded: September 2004, views: 2919
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Description
In this course I will discuss how exponential families, a standard tool in statistics, can be used with great success in machine learning to unify many existing algorithms and to invent novel ones quite effortlessly. In particular, I will show how they can be used in feature space to recover Gaussian Process classification for multiclass discrimination, sequence annotation (via Conditional Random Fields), and how they can lead to Gaussian Process Regression with heteroscedastic noise assumptions.
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Reviews and comments:
a huge part of lecture 4 is not there. could some one please help fix it? thanks.
There is a huge chunk of the video missing. After the third lecture, there should be video lecture for Junction trees but instead the fourth lecture jumps to latent variable view of EM
I look through the video lecture and Part 4 and 5 are in reverse order. Also why is lecture 6 and 7 exactly the same? And why is lecture 8 not present?
Indeed, lecture 8 is missing, just when the lecture would get into variational methods. I wish someone could fix this. Given that the other commnents are from one year ago, I think these commnents are not getting they feedback they should.
early potion of part5 has no sound..
lectures 6 and 7 are exactly the same and the most important part of the course (variational inference) is missing! Please add it.
Camera is poorly managed. Professor is writting on the screen and the camera is focusing on his face (many occasions!). Please use professional camera man for recordings.
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