Introduction to Machine Learning
published: March 31, 2011, recorded: February 2011, views: 4774
Report a problem or upload filesIf 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.
This talk gives an overview of machine learning from a practical perspective. Starting with examples of problems we might want to solve (in vision, signal processing, and geospatial inference), and the assumptions we have to make in order to get anywhere, it then covers a number of different supervised and unsupervised learning techniques. The talk concludes with ideas on how to evaluate a system, and when we should believe that a model is "right".
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !