Graphical Models

author: Cedric Archambeau, University College London
published: Aug. 5, 2010,   recorded: July 2010,   views: 8967


Related Open Educational Resources

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

 Watch videos:   (click on thumbnail to launch)

Watch Part 1
Part 1 1:40:45
Watch Part 2
Part 2 1:20:39


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.

See Also:

Download slides icon Download slides: bootcamp2010_archambeau_gm_01.pdf (1.1┬áMB)

Help icon Streaming Video Help

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:

make sure you have javascript enabled or clear this field: