Unsupervised Learning

These terms highlight key areas and methodologies within unsupervised learning, illustrating how AI and ML models can infer patterns and make decisions from data without being given explicit instructions or labels.

Anomaly Detection - The identification of rare items, events, or observations which raise suspicions by differing significantly from the majority of the data.

Autoencoder - A type of artificial neural network used in unsupervised learning for tasks such as dimensionality reduction and feature learning by learning to encode the input data into a compact representation and then decoding it back to the original form.

Clustering - A fundamental unsupervised learning technique where the algorithm groups a set of objects in such a way that objects in the same group (or cluster) are more similar to each other than to those in other groups.

Dimensionality Reduction - A process used in unsupervised learning to reduce the number of random variables under consideration, by obtaining a set of principal variables, which can help in visualizing, compressing, and understanding the data better.

Feature Engineering - The process of transforming raw data into features that better represent the underlying problem to the predictive models, resulting in improved model accuracy on unseen data.

Feature Learning - A technique that allows a machine to automatically discover the representations needed for feature detection or classification from raw data.

Unlabeled Data - Data that does not have explicit labels, making it ideal for unsupervised learning tasks where the algorithm tries to learn the patterns and the structure from the data itself without any external guidance or labels.

Unstructured Data - Often the focus of unsupervised learning, this type of data does not follow a predefined data model, making it more complex and varied, encompassing formats like text, images, and more, where unsupervised learning can be applied to discover inherent patterns or groupings.