Introduction to Boosting

author: Gunnar Rätsch, Max Planck Institute
published: Feb. 25, 2007,   recorded: August 2006,   views: 15504

See Also:

Download slides icon Download slides: mlss06tw_ratsch_ib.pdf (1.8 MB)

Help icon Streaming Video Help

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 1:10:35
Watch Part 2
Part 2 1:11:31
Watch Part 3
Part 3 1:22:09


This course provides an introduction to theoretical and practical aspects of Boosting and Ensemble Learning. I will begin with a short description of the learning theoretical foundations of weak learners and their linear combination. Then we point out the useful connection between Boosting and the Theory of Optimization, which facilitates the understanding of Boosting and later on enables us to move on to new Boosting algorithms, applicable to a broader spectrum of problems. In the course we will discuss "tricks of the trade", algorithmic issues, experimental results and a few applications.

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 Demo, December 12, 2018 at 1:41 p.m.:

Ensemble methods are meta-algorithms that combine several machine learning techniques into one predictive model in order to decrease variance, bias!!

Write your own review or comment:

make sure you have javascript enabled or clear this field: