Audio available in app
Unsupervised learning deals with finding patterns in data without labeled outputs from "summary" of Machine Learning by Stephen Marsland
In unsupervised learning, the goal is to find patterns in data without having any labeled outputs to guide the process. This means that the algorithm must discover the underlying structure or relationships in the data on its own, without being given explicit examples to learn from. This type of learning is often used when the data is unlabelled or when it is too costly or time-consuming to obtain labels for the data. Unsupervised learning can reveal hidden patterns, groupings, or associations in the data that may not be immediately apparent to the human eye. One common application of unsupervised learning is clustering, where the algorithm groups similar data points together based on some similarity measure. This can help to identify natural groupings or clusters within the data that can be useful for further analysis or decision-making. Another application is dimensionality reduction, where the algorithm seeks to represent the data in a lower-dimensional space while preserving its essential structure. This can help to reduce the complexity of the data and make it easier to visualize or analyze.- Unsupervised learning is a powerful tool for discovering patterns and relationships in data that may not be readily apparent. By allowing the algorithm to explore the data on its own terms, without the need for explicit labels, unsupervised learning can uncover valuable insights and knowledge that may have otherwise gone unnoticed.
