PASCAL Bootcamp in Machine Learning, Marseille 2010

Graphical Models

author: Cedric Archambeau, University College London
published: Aug. 5, 2010, recorded: July 2010, views: 8968

Slides

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.

Lecture popularity: You need to login to cast your vote.

Watch videos: (click on thumbnail to launch)

Part 1 1:40:45

!NOW PLAYING

Part 2 1:20:39

!NOW PLAYING

Description

We will discuss probabilistic graphical models associated to directed and undirected graphs. We will introduce exact inference algorithms, such as the sum-product algorithm, and apply it to hidden Markov models. We will also discuss elements of learning in graphical models including maximum likelihood, maximum a posteriori and the expectation-maximisation algorithm.

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

Comment:
Name:
Email address:
URL:

make sure you have javascript enabled or clear this field: