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GP means artificial neural network, not an actual nervous system.

Machine learning contains ANNs as a sub-discipline. Other non-ANN topics in ML include ensembled trees, Gaussian processes, and sampling theory.

I think one can't compare deep learning to machine learning since they have different purposes. While there have been great strides in the interpretability of NNs, the analytic models of classical machine learning are favored from a computational and interpretability perspective. Conversely, if the relationships in our data are constantly evolving (e.g. the change in the stochastic process followed by a time-series from one interval to another), then NNs are more appropriate.

linear-algebraic matrix multiplication complicated by non-linear threshold discontinuities evaluators, no?