Introduction to Machine Learning

During the next few lectures we will be looking at the inference from training data problem as a random process modeled by the joint probability distribution over input (measurements) and output (say class labels) variables. In general, estimating the underlying distribution is a daunting and unwieldy task, but there are a number of constraints or ”tricks of the trade” so to speak that under certain conditions make this task manageable and fairly effective.

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