How are things learned?
    Memorization
        Accumulation of facts
        Limited by:
            Time to observe facts
            Memory to observe facts
        ----------
        This is "declarative knowledge" - based on statements of truth
        ----------
    Generalization
        Deduce new facts from old facts
        Limited by:
            Accuracy of the dedeuction process
        Essentially a predictive activity
        Assumes that the past predicts the future.
        ----------
        This is "imperative knowledge" 
        ----------    


Basic paradigm:
    - provide a set of - seen, observed - training data 
    - decide on a characteristic of that training data as representative for the issue
    - infer something (a rule?) about the process that has generated that data
    - use inference to make predictions about previously unseen data
    - confirm inference using a set of test data

A choice might have to be made between "Will I have false negatives or false positives allowed by my rules" and it would depend on what side is the risk higher.

Issues of concern when learning models:
    Leaned models will depend on :
        - distance metric between examples
        - choice of features vectors 
        - constraints of complexity model
            - specified or unknown number of clusters
            - complexity of separating surface
            - need to acoid overfitting problems like "each example is its own cluster"