I'm trying to understand the context of the famous Minsky and Papert book "Perceptrons" from 1969, so critical to neural networks.

As far as I know, there were no other generic supervised learning algorithms yet except for perceptron: decision trees started to become actually useful only in late '70s, random forests and SVMs are '90s. It seems that the jackknife method was already known, but not k-cross validation (70s) or bootstrap (1979?).

Wikipedia says the classical statistics frameworks of Neyman-Pearson and Fisher were still in disagreement in '50s, despite that the first attempts at describing a hybrid theory were already in '40s.

Therefore my question: what were the state-of-the-art methods of solving general problems of predicting from data?