We emphasize "unsupervised" learning, where we do not have the benefit of a teacher showing us the information of interest in learning. Discuss the role you see for unsupervised learning in a larger data modeling project. What, exactly, is the type of information one can glean from the data without the benefit of a teacher? How is this information of value? How might you combine unsupervised and supervised learning to yield a more effective model? Discuss the various inductive biases of each of the components in your strategies and assess the possible impacts on performance based on the design decisions made.

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It can be risky sometimes to use unsupervised learning methods for larger data modeling projects, because this approach is more unpredictable than more natural alternatives. In this context, more natural alternatives mean algorithms that obtain more predictable results. For instance, it can be risky to use pure unsupervised learning for classification models. A potential solution is to deploy approximation algorithms if the trend is partially known....

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