Introduction to Kernel Methods

author: Bernhard Schölkopf, Max Planck Institute for Biological Cybernetics, Max Planck Institute
published: Feb. 25, 2007,   recorded: September 2004,   views: 23439


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 1:03:18
Watch Part 2
Part 2 51:06
Watch Part 3
Part 3 1:03:47
Watch Part 4
Part 4 45:32


The course will cover the basics of Support Vector Machines and related kerne methods: 1. Kernels and Feature Spaces
2. Large Margin Classification
3. Basic Ideas of Learning Theory
4. Support Vector Machines
5. Examples of Other Kernel Algorithms

See Also:

Download slides icon Download slides: mlss04_scholkopf_ikm_01.pdf (2.9 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 mlearnx, October 18, 2012 at 3:30 a.m.:

Thank you very much Dr. Bernhard Schölkopf, and to people who invented this source. In fact, there are people in remote areas of the world, who are studying machine learning individually without the help of any suprvisors and who has no other sources rather than a hardcopy book. It is indeed helpful. May God bless you all for every deeds of yours. Thank you!!!

Write your own review or comment:

make sure you have javascript enabled or clear this field: