Tutorial on Learning Bayesian Networks for Complex Relational Data

Presenters: Oliver Schulte and Ted Kirkpatrick

Duration: 4 hours (including 30 min break)

Intended Audience: Researchers with a background in machine learning who wish to apply machine learning to relational data, by combining graphical probabilistic models with first-order logic.

Code and Datasets


The supplementary slides provide further details but will not be discussed during the tutorial.
  1. Section 0: Introduction.
  2. Section 1: Relational Data: Definition
  3. Section 2: First-order Bayesian Networks
  4. Section 3: Parameter Learning
  5. Section 4: Structure Learning
  6. Section 5: Relational Classification and Dependency Networks
  7. Section 6: Relational Outlier Detection and Exception Mining
  8. Supplementary Material: Definitions. Concise summary of the mathematical content. No examples.

Sample Bayesian Networks

The Bayesian networks can be opened using the AIspace tool.