In Part II we address how to learn a model from data. In particular we will discuss learning a model as a form of inference on an extended distribution, now taking into account the parameters of the model.
Learning a model or model parameters from data forces us to deal with uncertainty since with only limited data we can never be certain which is the ‘correct’ model. We also address how the structure of a model, not just its parameters, can in principle be learned.
In Part II we show how learning can be achieved under simplifying assumptions, such as maximum likelihood that set parameters by those that would most likely reproduce the observed data. We also discuss the problems that arise when, as is often the case, there is missing data.
Together with Part I, Part II prepares the basic material required to embark on understanding models in machine learning, having the tools required to learn models from data and subsequently query them to answer questions of interest.
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