Learning to map well-defined inputs to well-defined outcomes. Classification — for example, identifying photographs of distinct animals or predicting the possibility of cancer in medical data — and forecasts — for example, predicting the likelihood of defaulting on a loan application — are two examples of such functions. These are merely statistical correlations, not causative consequences.

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Input-output pairs can be found in large (digital) data sets or can be constructed. The more training data sets there are, the more precise the learning will be. Unlike traditional analytic methods, deep learning algorithms have no asymptotic data size limit at which they cease advancing.

The assignment gives explicit feedback in the form of goals and metrics that may be easily defined. “ML works well when we can clearly specify the goals, even if we can't always articulate the best way to achieve those goals.” When there are precise, system-wide performance goals — for example, getting the most points in a video game, or optimising the overall traffic flow of a city — and such measurements can be incorporated in the training data, machine learning is very useful.

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There are no complex logic or reasoning chains that rely on a variety of background knowledge or common sense. “ML systems are particularly good at learning empirical relationships in data, but they struggle when the task needs extensive chains of reasoning or complicated planning that relies on common sense or past information that the computer doesn't have.” In cases where quick reaction and feedback are required, such as a video game, machine learning excels. It performs poorly in events that rely on the context set by a series of preceding occurrences.