Training set: a set of ** labeled examples** of the form

\[\langle x_1,\,x_2,\,\dots x_p,y\rangle,\]

where \(x_j\) are ** feature values** and \(y\) is the

*output*- Task:
*Given a new \(x_1,\,x_2,\,\dots x_p\), predict \(y\)*

What to learn: A ** function** \(h:\mathcal{X}_1 \times \mathcal{X}_2 \times \cdots \times \mathcal{X}_p \rightarrow \mathcal{Y}\), which maps the features into the output domain

- Goal: Make accurate
(on unseen data)*future predictions* **From Reintroduction to Statistics, we saw how this goal is formalized in terms of Generalization Error**