Practical Statistical Relational Learning

Posted in Science on October 03, 2008

Practical Statistical Relational Learning

The tutorial will be composed of three parts:

Foundational areas. The first part will consist of a brief introduction to each of the four foundational areas of SRL: logical inference, inductive logic programming, probabilistic inference, and statistical learning. Obviously, in the short time available no attempt will be made to comprehensively survey these areas; rather, the focus will be on providing the key concepts and techniques required for the subsequent parts. For example, the logical inference part will focus on the basics of satisfiability testing, and the probabilistic/statistical parts on Markov networks. The duration of this part will be approximately two hours (half hour per subtopic).

Putting the pieces together. The second part will introduce the key ideas in SRL and survey major approaches, using Markov logic as the unifying framework. It will present state-of-the-art algorithms for statistical relational learning and inference, and give an overview of the Alchemy open-source software. This part will essentially consist of putting together the pieces introduced in the first part. Its duration will be approximately an hour.

Applications. The third and final part will describe how to efficiently develop state-of-the-art non-i.i.d. applications in various areas, including: hypertext classification, link-based information retrieval, information extraction and integration, natural language processing, social network modeling, computational biology, and ubiquitous computing. This part will also include practical tips on using SRL, Markov logic and Alchemy - the kind of information that is seldom found in research papers, but is key to developing successful applications. The duration of this part will be approximately an hour.

Author: Pedro Domingos, University Of Washington

Watch Video

Tags: Science, Lectures, Computer Science, Machine Learning, VideoLectures.Net, Statistical Learning