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I know that there are three types of machine learning algorithms, supervised, unsupervised, and reinforcement learning, and that often neural networks are used to implement them. However, neural networks is said to be a subfield of machine learning, and deep learning a subfield of neural networks.

Thus, I would like to know:

Which specific algorithms/models belong in Machine Learning but are not considered neural networks? Any specific names for these fields?

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There are many techniques (algorithms and models) in ML other than neural networks, for example

  • decision trees
  • support vector machines
  • hidden Markov models
  • Bayesian networks
  • linear regression
  • k-means
  • tabular reinforcement learning (e.g. tabular Q-learning)

If you pick any good book on ML, you will find more details about these.

I don't think these approaches fall into a subfield of ML with a name. Occasionally, some people may call them "traditional ML approaches" or something like that.

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