AAISM classifies learning paradigms by the presence of labeled targets. Creating categories (labels) and training on them is supervised learning, where input features are mapped to known outputs and optimization minimizes prediction error against ground truth. Unsupervised (B) discovers structure without labels; reinforcement (A) optimizes behavior via rewards; “machine learning” (C) is the broad field, not the specific technique.
[References: AI Security Management™ (AAISM) Body of Knowledge — AI/ML Foundations; Learning Paradigms and Data Requirements. AAISM Study Guide — Supervised vs. Unsupervised vs. Reinforcement Learning; Label Quality and Model Performance Dependencies., ===========]
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