Kevin Korb

Prerequisites: CSE 458.

The subject follows up Bayesian Modelling by addressing the machine learning of Bayesian Networks for data mining. This has become a hot topic because of the difficulty of eliciting Bayesian Networks from domain experts. Topics include:

• Can we learn causal structure from correlational data?
• Bayesian confirmation theory
• Algorithms for learning causal structure
• How to estimate probabilities
• Linear and discrete Bayesian networks
• Integrating prior knowledge with data
• Stochastic sampling methods
• Metric learning with MML and alternatives
• Evaluating machine learning methods

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