Unsupervised learning is the training of an artificial intelligence (AI) algorithm using information that is neither classified nor labeled and allowing the algorithm to act on that information without guidance.
A key motivation for unsupervised learning is that, while the data passed to learning algorithms is extremely rich in internal structure (e.g., images, videos and text), the targets and rewards used for training are typically very sparse (e.g., the label ‘dog’ referring to that particularly protean species, or a single one or zero to denote success or failure in a game). This suggests that the bulk of what is learned by an algorithm must consist of understanding the data itself, rather than applying that understanding to particular tasks.
This SlideShare explores the concept of unsupervised learning in brief.
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