Machine Learning Ontology

Instead of seeing each Machine Learning (ML) method as a "shiny new object", here is an attempt to create a unified picture. There is no consensus when it comes to an ontology for ML methods; organizational principles are simply ways to get our arms around knowledge so that we are not swamped by too many unconnected notions. A powerful organization of the concepts or Ontology of ML is based on conditional expectation. Conditional Expectation of Class'y' given input attributes, x, denoted by E[y x]. Implementation of estimation of the conditional expectation with various assumptions lead, one way or the other, to ALL the ML techniques that we have today in 2016.