Foundations of Supervised Learning

Supervised learning definition and goal

Supervised learning is a machine learning paradigm in which an algorithm learns a mapping from input data to output labels using labeled example pairs (input, expected output). The “supervised” aspect comes from a teacher-like role: the training data includes the correct answers, guiding the model toward making accurate predictions. The goal of supervised learning is to generalize—i.e., to predict the correct outputs for new, unseen data. This requires balancing how well the model fits the training examples with how well it performs on new data, often described using generalization error. Supervised learning is commonly used for classification (predicting categories) and regression (predicting continuous values).

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Training data labeled input-output pairs

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