Supervised learning is one of the primary categories of machine learning. It involves training a model on a labeled dataset, which means that every training example is paired with an output label. The model learns from these examples and makes predictions or decisions without human intervention.

Here’s a brief overview of supervised learning:

1. Training Data: The data used in supervised learning consists of input-output pairs. The output label is often manually annotated by humans.

2. Model Training: A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used to map new examples.

3. Making Predictions: Once the model is trained, it can be used to predict the output for new, unseen data.

4. Evaluation: Models are often evaluated using a separate set of data (testing data) to gauge their accuracy and efficiency.

5. Applications: Supervised learning is widely used in various applications such as image classification, spam detection, fraud detection, speech recognition, and more.

6. Algorithms: Common algorithms used in supervised learning include linear regression, logistic regression, support vector machines, decision trees, and neural networks.

Supervised learning is different from unsupervised learning, where the algorithm is provided with data without explicit instructions on what to do with it. The system tries to learn the patterns and the structure from the data.