Webcasts

Webcast #5: A Tutorial on Learning Causal Influences
www.kdd.org/webcasts.php

By Richard E. Neapolitan, Professor and Chair of Computer Science at Northeastern Illinois University.

Abstract
Bayesian networks are graphical structures for representing the probabilistic relationships among a large number of variables and doing probabilistic inference with those variables. The 1990's saw the emergence of excellent algorithms for learning Bayesian networks from passive data. In 2004 I unified this research with my text Learning Bayesian Networks. This tutorial is based on that text and my paper. Neapolitan, R.E., and X. Jiang, "A Tutorial on Learning Causal Influences," in Holmes, D. and L. Jain (Eds.): Innovations in Machine Learning, Springer-Verlag, New York, 2005. I will discuss the constraint-based method for learning Bayesian networks using an intuitive approach that concentrates on causal learning. Then I will show a few real examples.

Biography
Richard E. Neapolitan is Professor and Chair of Computer Science at Northeastern Illinois University. He has previously written three books including the seminal 1990 Bayesian network text Probabilistic Reasoning in Expert Systems. More recently, he wrote the 2004 text Learning Bayesian networks, and Foundations of Algorithms, which has been translated to three languages and is one of the most widely-used algorithms texts world-wide. His books have the reputation of making difficult concepts easy to understand because of the logical flow of the material, the simplicity of the explanations, and the clear examples.

Webcast #4: Exploring the Power of Links in Data Mining (44 MB)
www.kdd.org/webcasts.php

By Jiawei Han, Professor, Department of Computer Science, University of Illinois at Urbana-Champaign.

Abstract
Algorithms like PageRank and HITS have been developed in late 1990s to explore links among Web pages to discover authoritative pages and hubs. Links have also been popularly used in citation analysis and social network analysis. We show that the power of links can be explored thoroughly at data mining in classification, clustering, information integration, and other interesting tasks. Some recent results of our research that explore the crucial information hidden in links will be introduced, including (1) multi-relational classification, (2) user-guided clustering, (3) link-based clustering, and (4) object distinction analysis. The power of links in other analysis tasks will also be discussed in the talk.