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Video Title: CSE Colloquia - 2005: Learning, Logic, and Probability - A Unified View
Description: "Artificial intelligence systems must be able to learn, reason logically, and handle uncertainty. Research has focused on each of these goals individually, and only recently have attempts been made to achieve all three at once. In this colloquia, Pedro Domingos, UW Computer Science & Engineering, describes Markov logic: a representation that combines the full power of first-order logic and probabilistic graphical models, and algorithms for learning and inference in it. Experiments in a real-world university domain illustrate the promise of this approach."
Date of Video: November 2, 2004
Color. Sound. Length (min:sec): 56:22.
Copyright Info: University of Washington
Interesting Clips:
  • 2:27 The way things were
  • 3:53 The way things are
  • 7:04 The way things will be
  • 8:45 current state of the art
  • 12:14 two questions addressed by this talk
  • 13:27 the most important slide - Markov Logic Networks
  • 16:20 Markov Network refresher
  • 20:00 First-Order Logic refresher
  • 23:03 Markov Knowledge Networks
  • 39:52 Markov Chain Monte Carlo / Gibbs Sampler
  • 45:28 Pseudo-Likelihood
  • 47:13 real world experiment
  • 50:10 link prediction
  • 52:05 Future Work: Inference & Learning
  • 52:46 Future Work: Applications
Location of Original: ?? University of Washington ?? ResearchChannel
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Additional videos from the CSE Colloquia - 2005, The University of Washington Computer Science & Engineering Colloquium Series, are available at

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Contributor: Jon Glick
Collections: WashingtonVideos
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