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Machine learning

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The branch of AI in which a system improves at a task by learning patterns from data rather than by following rules people wrote. ISO/IEC 22989 groups its approaches into supervised, unsupervised, semi-supervised and reinforcement learning 1.

Developed in
ch. 11, By learning paradigm
Chapters
ch. 11, AI Defined
Source
1 numbered reference, listed below

Where it is used

8 chapters of the Body of Knowledge use the term. Each link opens the first section that does.

Patterns that use this term

One pattern page uses the term.

  • Reinforcement learning

    Learning to maximise a reward signal through trial and feedback.

  • Reinforcement learning from human feedback (RLHF)

    A way to align a pre-trained model: supervised fine-tuning on human demonstrations, then reinforcement learning against a reward model trained on human…

  • Reward hacking

    A system finding an unintended way to maximise its reward or objective without doing what its designers meant.

  • Self-supervised learning

    Learning by predicting parts of the input itself, such as the next token, over large corpora; the AI Act's definition of a general-purpose model names…

  • Supervised learning

    Learning from labelled examples.

  • Unsupervised learning

    Learning structure (clusters, anomalies) from data without labels.

  • Inferred sensitive data

    Sensitive information a system derives from ordinary inputs (health from purchases, beliefs from behaviour) or carries through a proxy feature.

  • ISO/IEC 22989

    The ISO/IEC standard (2022) that establishes AI concepts and terminology for use by other standards and by diverse stakeholders.

Sources

  1. [1] ISO/IEC 22989:2022, Artificial intelligence concepts and terminology (referenced by identifier only; autonomy and heteronomy; clause 5.11 machine learning approaches: supervised, unsupervised, semi-supervised, reinforcement). ISO/IEC. 2022-07. https://www.iso.org/standard/74296.html (verified: secondary)

Definitions of legal terms paraphrase the cited text, which governs. Dated statements are as of .

Cite this term

García Aibar, J. (2026). Machine learning. In AI Governance Engineering: The Thesis & Body of Knowledge (v0.5.0), Glossary. https://doi.org/10.5281/zenodo.22956197. https://aigovernanceengineer.com/glossary/machine-learning. CC BY 4.0

BibTeX

@misc{aige2026machinelearning,
  author  = {Jorge García Aibar},
  title   = {{Machine learning}},
  note    = {Glossary, AI Governance Engineering: The Thesis \& Body of Knowledge, version 0.5.0},
  year    = {2026},
  doi     = {10.5281/zenodo.22956197},
  url     = {https://aigovernanceengineer.com/glossary/machine-learning}
}