Supervised learning

Supervised learning is a way of training machine learning systems for a specific application. In a training phase, an AI system is fed labelled data. The system trains from the input data, and the resulting model is then tested to see if it can correctly apply labels to new unlabelled data (such as if it can correctly label unlabelled pictures of cats and dogs accordingly). This type of learning is useful when it is clear what is being searched for, such as identifying spam mail.

Semi-supervised learning also a way of training machine learning systems for a specific application, is where an AI system uses a mix of supervised and unsupervised learning and labelled and unlabelled data. This type of learning is useful when it is difficult to extract relevant features from data and when there are high volumes of complex data, such as identifying abnormalities in medical images, like potential tumours or other markers of diseases. See also supervised learning, unsupervised learning, reinforcement learning and training datasets.

Unsupervised learning another way of training machine learning systems for a specific application. An AI system is fed large amounts of unlabelled data, in which it starts to recognise patterns of its own accord. This type of learning is useful when it is not clear what patterns are hidden in data, such as in online shopping basket recommendations (“customers who bought this item also bought the following items”).

This appears in the UK Government website as 'Artificial intelligence (AI) glossary' dated January 2024.

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