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Unsupervised Learning

  • Unsupervised learning uses unlabeled data for training.
  • This is particularly useful in problems where it is difficult to obtain labeled data.
  • Unsupervised learning algorithms are useful for discovering patterns in data.
  • The goal of unsupervised learning is use a feature vector as input and either outputs a feature vector or a label/value.

Use Cases

  • Clustering algorithm takes a feature vector as input and outputs a label.
  • Dimensionality reduction algorithms take a feature vector as input and output a feature vector with less features.
  • Anomaly detection algorithms take a feature vector as input and output is a real number indicating the degree of anomaly.

Examples