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learning algorithms adapting to situations.
These learning algorithms don't adapt and they don't really "learn"-- they just try to fit probabilty weights and their distribution to minimize loss (misses). See what is called SGD ("stochastic gradient descent"):
http://ufldl.stanford.edu/tutorial/supervised/OptimizationStochasticGradientDescent/. Also https://en.wikipedia.org/wiki/Stochastic_gradient_descentMachine learning and human learning are very different. We might have a model of Neuron but its apples and wales.
-- yes most of what people are doing is "supervised" (results compared against truth or known results aka "learn by example") rather than "unsupervised".
EdwardZ
@mashton
Ironically, you have just perfectly illustrated the point that EdwardZzzzzz was trying to make about learning algorithms adapting to situations.
But you knew that.