Bayesian Learning

• Introduction
• Bayes Theorem
• A General Example
• Bayes Theorem & Concept Learning
• MAP Hypotheses and Consistent Learners
• Maximum Likelihood & Least-Squared Error
• Maximum Likelihood for Predicting Probabilities
• Gradient Search to Maximize Likelihood in ANN
• Minimum Description Length
• Bayes Optimal Classifier
• Gibbs Algorithm
• Naive Bayes Classifier
• An Example
• Estimating Probabilities
• Learning to Classify Text
• Learn Naive Bayes Text Algorithm
• Experimental Results
• Bayesian Belief Networks
• A Bayesian Belief Network
• Representation
• Inference
• Learning BBNs
• Gradient Ascent Training of BBN
• Learning BBN Structure
• EM Algorithm
• Estimating Means of Gaussians
• -means Problem Visualization
• Practical Implementation for -means EM
• Summary