Probabilistic Graphical Models
published: Feb. 25, 2007, recorded: January 2005, views: 20054
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.
Watch videos: (click on thumbnail to launch)
My lectures will cover the basics of graphical models, also known as Bayes(ian) (Belief) Net(work)s. We will cover the basic motivations for using probabilities to represent and reason about uncertain knowledge in machine learning, and introduce graphical models as a qualitative and quantitative specification of large joint probability distributions. We will see how many common classification, regression and clustering models can be cast in this framework. We will cover the basic algorithm (called belief propagation) for inference in graphical model structures. We will also cover the major approaches to learning models from data (parameter estimation). The course will focus on directed models and the basic algorithms, but time and student desire permitting, I will also try to give some preliminary explanations of undirected models, approximate inference and learning, structure discovery and current applications.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !