Unsupervised learning (unlabeled data)

• Algorithms used in unsupervised learning vary, including:
  • Clustering
    • k-means
    • mixture models
    • hierarchical clustering
  • Anomaly detection
  • Neural Networks
    • Autoencoders
    • Deep Belief Nets
    • Hebbian Learning
    • Generative Adversarial Networks
    • Self-organizing map
  • Approaches for learning latent variable models such as
    • Expectation–maximization algorithm (EM)
    • Method of moments
    • Blind signal separation techniques, e.g.,
      • Principal component analysis
      • Independent component analysis
      • Non-negative matrix factorization
      • Singular value decomposition

Reference:

• Unsupervised learning - Wikipedia