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Machine learning

By Updated

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.

Machine learning: mentions per chapter20 mentions of the term across 8 of the 23 chapters of the Body of Knowledge; the chapters the glossary cross-references are drawn solid.Cross-referenced chapterOther chapter08 Regulatory Map: 1 mention08 Regulatory Map110 Reading List: 10 mentions10 Reading List1011 AI Defined: 3 mentions11 AI Defined314 Development: 1 mention14 Development115 Deployment: 1 mention15 Deployment119 Privacy & AI: 2 mentions19 Privacy & AI220 Existing Law: 1 mention20 Existing Law122 Principles & Standards: 1 mention22 Principles & Standards1No mentions in the other 15 chaptersSource: the Body of Knowledgev0.5.0
Machine learning: mentions per chapter20 mentions of the term across 8 of the 23 chapters of the Body of Knowledge; the chapters the glossary cross-references are drawn solid.Cross-referenced chapterOther chapter000102030405060708 Regulatory Map: 1 mention10810 Reading List: 10 mentions101011 AI Defined: 3 mentions311121314 Development: 1 mention11415 Deployment: 1 mention11516171819 Privacy & AI: 2 mentions21920 Existing Law: 1 mention1202122 Principles & Standards: 1 mention12223Source: the Body of Knowledge v0.5.0
Mentions per chapter How often each chapter names the term, with the chapters the glossary cross-references drawn solid. The longest bar is where to read the term in use; bars spread across many chapters mark a term that runs through the discipline.
Mentions as a table
Mentions of Machine learning per chapter
ChapterMentionsCross-referenced
00 Preface0No
01 Definition0No
02 Why Now0No
03 Values & Principles0No
04 The Stack0No
05 Patterns0No
06 The Role0No
07 Maturity Model0No
08 Regulatory Map1No
10 Reading List10No
11 AI Defined3Yes
12 Governance Program0No
13 Risk Management0No
14 Development1No
15 Deployment1No
16 Fairness & XAI0No
17 Incidents0No
18 EU AI Act0No
19 Privacy & AI2No
20 Existing Law1No
21 AI Laws Worldwide0No
22 Principles & Standards1No
23 AI Agents0No

Patterns that use this term

One pattern page uses the term.

Machine learning: related terms and patternsThe term at the centre; related terms on solid edges, the terms it is contrasted with on dashed edges, and the patterns that use it most in the colour of their layer.RelatedColour: stack layer of the pattern03 EvalsMachine learningRelated terms (8)Frontier Safety Framework · RelatedFrontier Safety FrameworkInferred sensitive data · RelatedInferred sensitive dataReinforcement learning from human feedback (RLHF) · RelatedRLHFReinforcement learning · RelatedReinforcement learningReward hacking · RelatedReward hackingSelf-supervised learning · RelatedSelf-supervised learningSupervised learning · RelatedSupervised learningUnsupervised learning · RelatedUnsupervised learningPatterns using it (1)Adversarial Red-Team Suite, 1 mention · Uses the termAdversarial Red-Team Suite
Machine learning: related terms and patternsThe term at the centre; related terms on solid edges, the terms it is contrasted with on dashed edges, and the patterns that use it most in the colour of their layer.RelatedColour: stack layer of the pattern03 EvalsRelated terms (8)Frontier Safety Framework · RelatedFrontier Safety FrameworkInferred sensitive data · RelatedInferred sensitive dataReinforcement learning from human feedback (RLHF) · RelatedRLHFReinforcement learning · RelatedReinforcement learningReward hacking · RelatedReward hackingSelf-supervised learning · RelatedSelf-supervised learningSupervised learning · RelatedSupervised learningUnsupervised learning · RelatedUnsupervised learningPatterns using it (1)Adversarial Red-Team Suite, 1 mention · Uses the termAdversarial Red-Team SuiteMachine learning
The term's neighbourhood Terms related to this one on solid edges, terms it is often confused with on dashed edges, and the patterns that use it most in the colour of their layer. Follow a dashed edge to check the distinction before you use either term.
Neighbourhood as a table
Neighbourhood of Machine learning
FamilyItemRelation
Related termsFrontier Safety FrameworkRelated
Related termsInferred sensitive dataRelated
Related termsReinforcement learning from human feedback (RLHF)Related
Related termsReinforcement learningRelated
Related termsReward hackingRelated
Related termsSelf-supervised learningRelated
Related termsSupervised learningRelated
Related termsUnsupervised learningRelated
Patterns using itAdversarial Red-Team Suite, 1 mentionUses 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.

  • Frontier Safety Framework

    Google DeepMind's published policy for severe risks from its most capable models: Critical Capability Levels for misuse, machine learning R&D and…

  • Inferred sensitive data

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

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}
}