published: Feb. 25, 2007, recorded: May 2005, views: 7804
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.
Bayes Rule provides a simple and powerful framework for machine learning. This tutorial will be organised as follows:
1. I will give motivation for the Bayesian framework from the point of view of rational coherent inference, and highlight the important role of the marginal likelihood in Bayesian Occam's Razor.
2. I will discuss the question of how one should choose a sensible prior. When Bayesian methods fail it is often because no thought has gone into choosing a reasonable prior.
3. Bayesian inference usually involves solving high dimensional integrals and sums. I will give an overview of numerical approximation techniques (e.g. Laplace, BIC, variational bounds, MCMC, EP...).
4. I will talk about more recent work in non-parametric Bayesian inference such as Gaussian processes (i.e. Bayesian kernel "machines"), Dirichlet process mixtures, etc.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !