From Practice to Theory in Learning from Massive Data
author: Charles Elkan,
Department of Computer Science and Engineering, UC San Diego
published: Oct. 12, 2016, recorded: August 2016, views: 1168
published: Oct. 12, 2016, recorded: August 2016, views: 1168
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.
Description
This talk will discuss examples of how Amazon applies machine learning to large-scale data, and open research questions inspired by these applications. One important question is how to distinguish between users that can be influenced, versus those who are merely likely to respond. Another question is how to measure and maximize the long-term benefit of movie and other recommendations. A third question, is how to share data while provably protecting the privacy of users. Note: Information in the talk is already public, and opinions expressed will be strictly personal.
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: