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Within artificial intelligence (AI) and machine learning, there are two basic approaches: supervised learning and unsupervised learning. The main difference is that one uses labeled data to help predict outcomes, while the other does not.
Unsupervised learning, also known as unsupervised machine learning, uses machine learning (ML) algorithms to analyze and cluster unlabeled data sets. These algorithms discover hidden patterns or data groupings without the need for human intervention.
While supervised and self-supervised learning are largely used for the same kinds of tasks and both require a ground truth to optimize performance via a loss function, self-supervised models are trained on unlabeled data whereas supervised learning requires labeled datasets for training.
Unsupervised machine learning and supervised machine learning are frequently discussed together. Unlike supervised learning, unsupervised learning uses unlabeled data. From that data, it discovers patterns that help solve for clustering or association problems.
For a deeper dive into the differences between these approaches, check out Supervised versus unsupervised learning: What’s the difference? A third category of machine learning is reinforcement learning, where a computer learns by interacting with its surroundings and getting feedback (rewards or penalties) for its actions.
Both semi- and self-supervised learning aim to circumvent the need for large amounts of labeled data—but whereas semi-supervised learning involves some labeled data, self-supervised learning methods like autoencoders are truly unsupervised.
Unsupervised learning, also known as unsupervised machine learning, uses machine learning algorithms to analyze and cluster unlabeled datasets (subsets called clusters). These algorithms discover hidden patterns or data groupings without the need for human intervention.
The fifth type of machine learning technique offers a combination between supervised and unsupervised learning. Semi-supervised learning algorithms are trained on a small labeled dataset and a large unlabeled dataset, with the labeled data guiding the learning process for the larger body of unlabeled data.
Supervised learning uses manually labeled data to produce predictions or classifications. Unsupervised learning aims to uncover and learn hidden patterns from unlabeled data. In contrast to supervised learning, reinforcement learning does not use labeled examples of correct or incorrect behavior.