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Machine learning is a subfield of AI that uses statistical models and algorithms to discover patterns in data and make predictions. It enables automated decision support and iterative model improvement through training on labeled or unlabeled datasets. Typical applications include forecasting, personalization, and anomaly detection.
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Theoretical construct: explains a term, principle, or mental model.
What you need to understand to reason about a domain.
Machine learning lets models derive patterns from example data and apply predictions, classifications, or decisions to new cases.
Arthur Samuel coined the term in 1959 with a learning checkers program; the field has since grown from statistical learning to today’s deep neural models, alongside risk guidance such as NIST’s AI RMF.
Define the task and target, separate training, validation, and test data, train a model, check generalization and error distribution, and monitor it in use.
Examples from which a learning method derives model parameters or rules.
A model’s ability to perform reliably on previously unseen data.
The outcome a supervised learning method is intended to predict or classify.
Machine learning finds regularities in large datasets, while demanding clear targets, reliable data, and continuous quality control.
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