Optimization in Learning and Data Analysis

author: Stephen J. Wright, Computer Sciences Department, University of Wisconsin-Madison
published: Sept. 27, 2013,   recorded: August 2013,   views: 1324
Categories

Slides

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.
  Delicious Bibliography

Description

Optimization tools are vital to data analysis and learning. The optimization perspective has provided valuable insights, and optimization formulations have led to practical algorithms with good theoretical properties. In turn, the rich collection of problems in learning and data analysis is providing fresh perspectives on optimization algorithms and is driving new fundamental research in the area. We discuss research on several areas in this domain, including signal reconstruction, manifold learning, and regression / classification, describing in each case recent research in which optimization algorithms have been developed and applied successfully. A particular focus is asynchronous parallel algorithms for optimization and linear algebra, and their applications in data analysis and learning.

See Also:

Download slides icon Download slides: kdd2013_wright_data_analysis_01.pdf (1.2┬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: