Supervised learning vs Unsupervised learning

Why they differ

Supervised learning is given the right answers to learn from; unsupervised learning gets no answers and must find groups or oddities by itself.

Supervised learning

Machine learning basics

Machine learning from examples that come with the right answer attached, such as emails already marked spam or not spam.

Formal

A form of machine learning in which each training example is paired with a label giving the wanted output, and the model learns to map new inputs to the right label.

In plain English

Like learning with an answer key. You try each exercise, check the key, and correct yourself.

In practice

A region's security team labels thousands of past alerts as real attacks or false alarms, and a model trained on them learns to sort new alerts the same way.

Why it matters

The model can only be as good as its labels; labels are often made by people paid per item, and their mistakes and biases become the model's.

Unsupervised learning

Machine learning basics

Machine learning from examples with no answers attached, where the model finds groups, patterns or odd cases on its own.

Formal

A form of machine learning that works on training data without labels, finding structure in it, for example grouping similar items or flagging items that differ from the rest.

In plain English

Like sorting a box of mixed buttons into piles by colour and size without anyone telling you what the piles should be.

In practice

A monitoring tool at a hospital learns what normal logins look like and raises an alert when a nurse's account suddenly logs in at 3 a.m. from another country.

Why it matters

It can spot new, unknown threats nobody has labelled yet, but it also flags harmless unusual behaviour, so people must still judge the alerts.

Shared connections

Machine learning Reinforcement learning Self-supervised learning Training data