Nonparametric Bayesian Models in Machine Learning
published: Feb. 25, 2007, recorded: September 2004, views: 2660
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.
Bayesian methods make it possible to handle uncertainty in a principled manner, sidestep the problem of overfitting, and incorporate domain knowledge. However, most parametric models are too limited to adequately model complex real-world problems. Thus, interest has shifted to nonparametric models which can capture much richer and more complex probability distributions. This talk will review some of the core nonparametric tools for regression and classification (Gaussian processes; GPs) and density estimation (Dirichlet process mixtures). We will then focus on extensions of these basic tools (such as mixtures of GPs, warped GPs, and GPs for ordinal regression) and approximation methods which allow efficient inference in these models (such as expectation propagation; EP).
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !