Who so neglects learning in his youth, loses the past and is dead for the future.

– Euripides (484 BC – 406 BC), Phrixus, Frag. 927

Learning is the ability of an agent to improve its behavior based on experience. This could mean the following

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  The range of behaviors is expanded; the agent can do more.

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  The accuracy on tasks is improved; the agent can do things better.

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  The speed is improved; the agent can do things faster.

The ability to learn is essential to any intelligent agent. As Euripides pointed out, learning involves an agent remembering its past in a way that is useful for its future.

This chapter considers the problem of making a prediction as supervised learning: given a set of training examples made up of input–output pairs, predict the output of a new example where only the inputs are given. We explore four approaches to learning: choosing a single hypothesis that fits the training examples well, predicting directly from the training examples, selecting the subset of a hypothesis space consistent with the training examples, or (in Section 10.4) predicting based on the posterior probability distribution of hypotheses conditioned on the training examples.

Chapter 10 considers learning probabilistic models. Chapter 12 covers reinforcement learning. Section 15.2 considers learning relational representations.