# Machine Learning

## Abstract

Machine Learning is a huge and growing area. In this chapter, we cannot possibly even survey this area, but we can provide some context and some connections to probability and statistics that should make it easier to think about machine learning and how to apply these methods to real-world problems. The fundamental problem of statistics is basically the same as machine learning: given some data, how to make it actionable? For statistics, the answer is to construct analytic estimators using powerful theory. For machine learning, the answer is algorithmic prediction. Given a dataset, what forward-looking inferences can we draw? There is a subtle bit in this description: how can we know the future if all we have is data about the past? This is the crux of the matter for machine learning, as we will explore in the chapter.

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