I mean, I know that there's a bias-variance tradeoff in stats and ML, but what does it mean in the context of introduction to ML for physicists?
My guess is they mean they aren't going into as heavy detail in ML, which means the reader may lack some knowledge (high bias) but won't miss the forest/wood for the trees (low variance).
Anyone else care to speculate?
Low-variance, I think the author means to have a sharp and quality focus on the subjects he/she is going to talk about.
[1]: https://www.quantamagazine.org/deep-learning-relies-on-renor...
- The book has a high bias (since written from physics perspective), but low variance since assuming physics background allows authors to write a very focussed narrative that works for physicists (low variance on subset of physisicts readers).
- If we define the problem of choosing which topics t1, t2, ..., tn to cover in a ML book, the authors are saying the using the physicists approach will lead to consistency good results (i.e. not all over the place). It something like an argument from physicists-are-sensible people, unlike CS theoreticians, industry practicians, and mathematicians.