Fundamentals
Supervised learning
Supervised learning fits a mapping from inputs to known target outputs.
How it works
Each training example pairs evidence with an answer. The model generalizes that mapping to unseen inputs after minimizing prediction error.
A concrete example
Illustrative: train on 80,000 labeled images and evaluate on 10,000 held-out images.
Do not confuse it with Unsupervised learning
supervised data has explicit targets; unsupervised data does not.