I am working on my bachelor thesis about gradient descent. I have a problem understanding something in this note.
In section 1.2 (linear regression) the author claims that
$$ f(x) = \frac{1}{2} \sum_{i=1}^n (\langle x , a_i \rangle - y_i)^2 = \frac{1}{2} \| Ax-y \|^2$$
is the least square quadratic risk function. As far as I know the risk function gives the average measure of loss, or expected loss. A loss function is a measure of how good a prediction model does in terms of being able to predict the expected outcome.
So I would have expeted a factor $\frac{1}{n}$ instead of $\frac{1}{2}$. I would be grateful if someone could help me understand.