Matrix Computations in Machine Learning

author: Inderjit S. Dhillon, Department of Computer Science, University of Texas at Austin
published: Aug. 26, 2009,   recorded: June 2009,   views: 6502
Categories

See Also:

Download slides icon Download slides: icml09_dhillon_itmcml_01.pdf (491.6┬áKB)


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.
  Bibliography

Description

Matrix Computations are ubiquitous in all areas of science and engineering. In this talk, I will first survey some traditional problems in matrix computations and discuss issues that arise in solving them, such as, accuracy, algorithms and software. Then, I will discuss various matrix computation problems that arise in machine learning, especially specialized computations, such as non-negative matrix factorization, multilevel graph clustering and kernel learning. I will conclude with a pointer to resources and a discussion.

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: