基礎

Unsupervised Learning

定義

A machine learning approach that discovers structure in unlabelled data. Common techniques include clustering (grouping similar items) and dimensionality reduction. It is often used for anomaly detection and exploratory data analysis.

関連用語

Machine Learning (ML)

A subset of AI that enables systems to automatically learn and improve from experience without being explicitly programmed. ML algorithms build models based on training data to make predictions or decisions.

Supervised Learning

A machine learning approach where the model is trained on labelled input-output pairs. The model learns to map inputs to correct outputs, enabling predictions on new, unseen data. Most commercial ML models in production today are supervised.

Self-Supervised Learning

A training paradigm where a model generates its own supervision signal from unlabelled data—for example, by predicting masked words in a sentence. Self-supervised pre-training on internet-scale text or images is what enables foundation models to acquire broad world knowledge.

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