Intermediate · Fundamentals

Unsupervised Learning

Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.

TL;DR. Learning patterns from data that has no labels — only the inputs.

Technical Definition

Learning patterns from data that has no labels — only the inputs.

How it works

Unsupervised methods uncover hidden structure: clustering groups similar points, dimensionality reduction (PCA, autoencoders) compresses data into a lower-dimensional space, density estimation models the distribution. It is essential when labels are scarce or expensive, and it is the foundation of self-supervised pretraining used in modern LLMs.

Related Concepts

• Autoencoder — A neural network that learns compressed representations by training to reconstruct its own input through a bottleneck layer.
• Self-Supervised Learning — A training paradigm that generates supervisory signals from the data itself, eliminating the need for human labels.
• Clustering — An unsupervised technique that groups similar data points without using labels.