Location: Conferences » The ACM SIGKDD Conference Series - International Conference on Knowledge Discovery & Data Mining » 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), San Jose 2007 » Tutorials

Learning Bayesian Networks

author: Richard E. Neapolitan, Northeastern Illinois University
published: Aug. 12, 2007,   recorded: August 2007,   views: 67151
Categories

Slides

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.
  Bibliography

 Watch videos:   (click on thumbnail to launch)

Watch Part 1
Part 1 1:03:43
!NOW PLAYING
Watch Part 2
Part 2 1:02:32
!NOW PLAYING
Watch Part 3
Part 3 04:55
!NOW PLAYING

Description

Bayesian networks are graphical structures for representing the probabilistic relationships among a large number of variables and doing probabilistic inference with those variables. The 1990's saw the emergence of excellent algorithms for learning Bayesian networks from passive data.

I will discuss the constraint-based learning method using an intuitive approach that concentrates on causal learning. Then I will discuss the Bayesian approach with some simple examples. I will show how, using the Bayesian approach, we can even learning something about causal influences from passive data on two variables. Finally, I will show some applications to finance and marketing.

See Also:

Download slides icon Download slides: neapolitan_richard_00.ppt (1.1 MB)

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 !

Reviews and comments:

Comment1 Eugen Hotwagner, December 13, 2007 at 3:49 p.m.:

This is a very good introduction to causality and learning bayesian networks. there are many examples which are explained in detail. the lecture is also a very good introduction and supplement to neapolitans book "learning bayesian networks" which can be found here: http://www.amazon.com/Learning-Bayesi... the book is very good but also very expensive. i bought it and kind of regretted it later because of the price. combined with this lecture it might be worth it :)

Comment2 S.Gayathri, June 26, 2012 at 12:20 p.m.:

Thanks for the presenattion sir its really helpful

Comment3 John W. Brooks, October 4, 2012 at 4:55 a.m.:

Outstanding Lecture! Thanks very much!

JWB

Comment4 G Costantini, December 13, 2012 at 5:21 p.m.:

I found this lecture very interesting and very helpful, thank you

Comment5 Jack Johnson, February 2, 2021 at 9:37 a.m.:

This lecture will be useful for those who want to get involved with the proper activities. https://amongus-games.com

Write your own review or comment:

make sure you have javascript enabled or clear this field: