## Practical Statistical Relational Learning

author: Pedro Domingos, University of Washington
published: June 21, 2007,   recorded: June 2007,   views: 3984
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# Slides

0:00 Slides Practical Statistical Relational Learning Overview Motivation Examples Costs and Benefits of SRL Goal and Progress Plan Disclaimers Overview Markov Networks - part 1 Markov Networks - part 2 Hammersley-Clifford Theorem Markov Networks - part 2 Hammersley-Clifford Theorem Markov Nets vs. Bayes Nets Inference in Markov Networks MCMC: Gibbs Sampling Other Inference Methods MAP/MPE Inference MAP Inference Algorithms Learning Markov Networks Generative Weight Learning Pseudo-Likelihood Discriminative Weight Learning Other Weight Learning Approaches Discriminative Weight Learning Other Weight Learning Approaches Discriminative Weight Learning Other Weight Learning Approaches Structure Learning Overview First-Order Logic Inference in First-Order Logic Satisfiability Backtracking The DPLL Algorithm Stochastic Local Search The WalkSAT Algorithm Overview Rule Induction Learning a Single Rule Learning a Set of Rules First-Order Rule Induction Overview Plethora of Approaches Key Dimensions Knowledge-Based Model Construction Stochastic Logic Programs Probabilistic Relational Models Relational Markov Networks Bayesian Logic Markov Logic - part 1 Markov Logic - part 2 Markov Logic: Intuition Markov Logic: Definition Example: Friends & Smokers - part 1 Example: Friends & Smokers - part 2 Example: Friends & Smokers - part 3 Example: Friends & Smokers - part 4 Example: Friends & Smokers - part 5 Example: Friends & Smokers - part 6 Example: Friends & Smokers - part 7 Example: Friends & Smokers - part 8 Markov Logic Networks Summary

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Part 1 1:24:24
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Part 2 2:11:18

# Description

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.