**Training Data**

Supervised learning requires labelled training data, where each example in the dataset is paired with its corresponding output label.

**Classification vs. Regression**

Supervised learning tasks can be categorized into two main types:

- Classification: Predicting a categorical label or class for each input example.
- Regression: Predicting a continuous value or quantity for each input example.

**Algorithms**

Supervised learning algorithms include:

- Linear regression
- Logistic regression
- Decision trees
- Random forests
- Support vector machines (SVM)
- k-Nearest Neighbors (k-NN)
- Neural networks

**Evaluation Metrics**

Common evaluation metrics for supervised learning include accuracy, precision, recall, F1-score, and area under the ROC curve (AUC).