published: Oct. 12, 2011, recorded: September 2011, views: 35409
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.
Machine learning researchers often have to contend with issues of model selection and model fitting in the context of large complicated models and sparse data. The idea which I am pushing for in this project is that these can be nicely handled using Bayesian techniques.
Model selection is selecting, among a class of models each of which has finite capacity, the model of the right capacity. Nonparametric Bayesian modelling sidesteps model selection by simply using models of potentially unbounded (or infinite) capacity. Overfitting is avoided simply by the usual Bayesian approach of integrating out all parameters (perhaps using MCMC or variational methods).
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !