Explain the difference between supervised and unsupervised learning in the context of pattern recognition.

In supervised learning, algorithms learn from labeled data, where each input is associated with a corresponding output. The algorithm learns to map inputs to outputs based on examples provided during training. In contrast, unsupervised learning deals with unlabeled data, where the algorithm must find hidden patterns or structures in the data without explicit guidance. Supervised learning requires human intervention to provide labels for training data, while unsupervised learning doesn't rely on labeled data and can uncover previously unknown patterns or relationships in the data.