Also known as: ul Β· Unsupervised Machine Learning
Unsupervised learning is a machine learning approach that finds patterns, structure, or representations in data without pre-existing labels. Common uses include clustering, dimensionality reduction, anomaly detection, topic discovery, and density estimation.
A type of self-organized Hebbian learning that helps find previously unknown patterns in data set without pre-existing labels. It is also known as self-organization and allows modeling probability densities of given inputs. It is one of the three basic paradigms of machine learning, alongside supervised and reinforcement learning. Semi-supervised learning has also been described and is a hybridization of supervised and unsupervised techniques.
A 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'). See also semi-supervised learning, supervised learning and reinforcement learning and training datasets.
is, like supervised learning, a type of AI or machine learning that operates on data. However, with unsupervised learning, we don't need to have a specific question in mind. We don't need to label any data. Consider our example with photos before. Instead of labeling them 'Has cat' or 'No cat,' we may want to cluster this same collection of photos. Here, by cluster, we mean we want to give these photos to an algorithm that will determine which photos are alike and which are dissimilar. We expect that the similar photos will 'cluster' together. Importantly, we do not have to explicitly define a label for these clusters! However, we may learn new things: for example, cat photos may be in a cluster together that also includes lions and tigers, but doesn't include dogs or people.