Quiz Yourself on ML Basics

Data consists of observations $x$ and labels $y$.

The learnable parameters $\theta$ of a model are adjusted via the optimization process.

Some examples of supervised learning are principal component analysis (PCA) and anomaly detection.

A loss function measures the error between predicted and true values for $y$.