Machine learning, possibly contrary to popular belief, is not just about endless variations of neural networks. There is also a thriving subculture of probabilistic programming based on Bayesian principles. A large advantage of the latter approach is that most models provide some level of explanation of what was learned, whereas extracting such information from a neural network is (very) active research.
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