Technical but important quibble. You've described supervised learning: given pairs (X, Y), learn the function F such that Y = F(X). Unsupervised learning take

Technical but important quibble. You've described supervised learning: given pairs (X, Y), learn the function F such that Y = F(X). Unsupervised learning takes samples and generates more of the same kind of data. We can see unsupervised learning as being given samples (X) and training a function X = F(noise) that transforms a random vector into an output whose distribution resembles that of the original generating function. Reinforcement learning sees a reward R(X) of policies X and learns to find X such that R(X) is high. All of these have been known to operate on trees or other representations instead of arrays. This matters because there are business problems that are not supervised learning. Reinforcement learning problems especially. Sometimes I can say how well an output is doing and I want an output that does better.