Since I'm working in history and philosophy of science (HPS) and I'm trying to build my HPS model of alignment, in particular, I think it'd be good to read a book or two on the history of machine learning.
If you know of a good one, please share. Thanks!
I am interested in early material on version space learning and decision-tree induction, because they are relatively easy for humans to understand. They also provide conceptual tools useful to someone interested in cognitive aids.
Given the popularity of neural network models, I think finding books on their history should be easier. I know so little about genetic algorithms, are they part of ML algorithms now, or have they been abandoned? No idea here. I could answer that question with 10 minutes on Wikipedia, though, if my experience follows what is typical.