Unsupervised Learning

Unsupervised methods include clustering, dimensionality reduction, anomaly detection, and representation learning. They are useful for exploration, segmentation, feature discovery, and problems where labeled examples are unavailable or expensive to create. In practice, it often connects technical model behavior with governance, human oversight, data quality, and the business process the AI supports.