Unsupervised Learning: AI term explained

In the world of artificial intelligence and machine learning, unsupervised learning is an important term.

How Unsupervised Learning Works

Unlike supervised learning, unsupervised learning does not require labelled data, but allows the AI to recognise patterns and structures in the data on its own.

• Unsupervised learning does not provide the algorithm with labelled training data. It works with unstructured raw data.

• To detect patterns and similarities in the data, the algorithm uses techniques such as clustering and dimensionality reduction.

• Clustering is one of the most common methods of unsupervised learning, where the algorithm groups similar data points together. This allows natural groupings to be discovered in the data.

• Unsupervised learning is used in many fields, including speech processing, image recognition, anomaly detection and recommender systems.

Application fields of Unsupervised Learning

Unsupervised Learning has established itself as an indispensable tool in a wide range of application areas. The ability to extract valuable information from unlabelled data opens up many new possibilities for companies and research institutions.

• Companies use Unsupervised Learning to segment customers into different groups based on behavioural patterns, buying patterns and preferences. This enables personalised marketing strategies.

• In finance, Unsupervised Learning is used to detect unusual transactions or activity that could indicate potential fraud.

• Unsupervised Learning can also be used to analyse large text corpora to automatically identify relevant topics and group documents accordingly.

• In image processing, Unsupervised Learning is used to segment objects or regions in an image, which is important for autonomous driving, medical imaging and surveillance systems.

• Unsupervised learning can also be used for generative models such as Generative Adversarial Networks (GANs) to generate new data that resembles the distributions of the training data. This is mainly applied in the fields of art, media and content creation.