Machine Learning Laboratory
published: May 7, 2008, recorded: March 2008, views: 636
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.
The first laboratory has not been recorded but has featured some hands on experiments with Elefant (http://elefant.developer.nicta.com.au) mainly concentrating on installing, using, and developing machine learning algorithms within the Elefant framework. We will walk through examples of implementing a simple stochastic gradient descent algorithm as a part of this tutorial. This is the second part of the second session which is split with Christfried Webers's "Machine Learning Laboratory" and will feature hands on experiments with BNRM (Bundle Methods for Regularized Risk Minimization) (http://users.rsise.anu.edu.au/~chteo/BMRM.html). The emphasis here will be on developing various loss function modules which can then be plugged into the BMRM solver.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !