In supervised learning, with each input point, there’s an associated reference output value.
Learning a model = constructing a function that approximates (minimising some error measure) the given data.
Regression = the output variable \(Y\) is continuous.
We studied linear models with a single independent variable based on the least squares (SSR) fit.
In the next part we will extend this setting to the case of many variables, i.e., \(p>1\), called multiple regression.