Unsupervised Learning

Overview

A machine learning paradigm where a model is trained on an unlabeled dataset, meaning that the input data is not paired with corresponding output labels. The model's objective is to discover patterns, structures, and relationships within the data without explicit guidance. Common unsupervised learning tasks include clustering, dimensionality reduction, and anomaly detection, all aiming to gain understanding without explicitly labeled data.

Learning from Unlabeled Data

Unsupervised learning uses unlabeled data to discover patterns without specific guidance.

Finding Hidden Structures

The model tries to find patterns, structures, and relationships within the data.

Common Use-Cases

These models are often used in clustering, anomaly detection, and data reduction applications.

Related Concepts

• Machine Learning
• Supervised Learning
• Deep Learning
• Self-Supervised Learning