This document discusses different approaches for learning with complete data, including: 1) Parameter learning aims to find numerical parameters for a fixed probability model given complete data for all variables. 2) Maximum likelihood parameter learning derives parameter expressions as log terms and finds values by equating logs to 0. 3) Naive Bayes models assume attributes are conditionally independent, and truth is not representable as a decision tree. 4) Continuous models represent real-world applications using linear Gaussian models that minimize sum of squared errors via standard linear recursion.