Types of machine learning

If we consider the kind of signal or feedback available to a learning system, then machine learning falls into three categories:

  • supervised learning: uses labelled data for training, meaning each piece of data includes both a feature vector x and the desired output y;
  • unsupervised learning: uses unlabelled data X for training the model;
  • reinforcement learning: the learning system is given a goal and interacts with a dynamic environment.

If instead we consider the desired output of a machine learning system, then we get these categories:

  • classification (inputs are assigned to a class, out of a set of predetermined classes)
  • regression (inputs are assigned a value from a continuous range)
  • clustering (inputs are divided into a series of groups or classes, which are not known beforehand)
  • density estimation
  • dimensionality reduction.


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