I thought The Alignment Problem was pretty good at giving a high level history. Despite the name, only a pretty small portion is actually about the alignment problem and a lot is about ML history.

For the history leading up to machine learning I would recommend A Brief History of Artificial Intelligence by Michael Wooldridge

Each chapter of Russell & Norvig's textbook "Artificial Intelligence: A Modern Approach" ends with historical notes. These are probably sparser than you want, but they are good and cover a very broad array of topics. The 4th edition of the book is decently up to date (for the time being!).

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.