Review Unsupervised Learning

Unsupervised training is a method where a model learns patterns from data without being given explicit correct answers or labels.

Unsupervised learning looks for structure in data without using a provided target answer for each example. It may group examples, estimate distributions, or learn lower-dimensional representations.

The method can reveal patterns in data that lacks labels, but the patterns are shaped by the algorithm, features, and objective. Human interpretation is still required to decide whether they matter.

A clustering method groups customers by similar purchasing behavior without being given named customer segments in advance.

Unsupervised does not mean objective, autonomous, or free of design choices. People still select the data, representation, method, and criteria for judging the result.

Find structure without supplied answers.

* [[self-supervised-learning|Self-Supervised Learning]] * [[supervised-learning|Supervised Learning]] * [[latent-representation|Latent Representation]] * [[dataset|Dataset]]

* [Google for Developers, “Machine Learning Glossary”](https://developers.google.com/machine-learning/glossary) — provides established boundaries for core machine-learning vocabulary.

* Compare [[self-supervised-learning|Self-Supervised Learning]], [[supervised-learning|Supervised Learning]], [[latent-representation|Latent Representation]], and [[dataset|Dataset]] to place Unsupervised Learning in the surrounding system. * Follow the cited source material above for the method, standard, or research context behind this draft.