Supervised and unsupervised learning are two major types of machine learning.

Supervised learning involves training a model using labeled data, where the input data is accompanied by the correct output. The goal of supervised learning is to learn a mapping function from input to output based on the training data, so that the model can accurately predict the output for new input data. Common examples of supervised learning include classification and regression tasks.

Unsupervised learning, on the other hand, involves training a model on unlabeled data, without any explicit output information. The goal of unsupervised learning is to discover patterns, relationships, and structure in the input data. Common examples of unsupervised learning include clustering, dimensionality reduction, and anomaly detection.

* In supervised learning, the model is provided with labeled data, which enables it to learn the relationship between the input and output variables.

* In unsupervised learning, the model is provided with only input data, which requires it to discover meaningful patterns or relationships on its own.

The choice of supervised or unsupervised learning depends on the problem at hand and the availability of labeled data. Supervised learning is typically used when the output variable is known and the goal is to predict it for new input data. Unsupervised learning is typically used when the goal is to discover structure or patterns in the input data, without a specific output variable in mind.