I guess the author is not claiming a well rounded (low bias) introduction to ML to statistics, but a highly biased and specialized (low variance) course that is tailored to the author's own interests and tastes.

Quite possibly.

High-bias, meaning it is opinionated from author's own perspective.

Low-variance, I think the author means to have a sharp and quality focus on the subjects he/she is going to talk about.

Almost agree to what you said. Just a piece of more context: the 1st author wrote about connection between physics's Renormalization Group and Deep learning which was featured in [1] 4 years ago.

[1]: https://www.quantamagazine.org/deep-learning-relies-on-renor...

Seems the most plausible explanation to me, so far.

Yeah after looking through the PDF, I'm still wondering what they meant. Here are some conjectures:

- 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.

The reference is to the bias-variance tradeoff: https://en.wikipedia.org/wiki/Bias–variance_tradeoff

Yes, I know. I mention that in my original comment.

I interpreted “high-bias” as meaning it has an idiosyncratic and specific audience, but “low variance” as meaning with high probability it will be an effective teaching tool for that audience.

Yeah, seems like a sane interpretation.

all these different interpretations suggest that lots of people, myself included, have a fuzzier understanding of the bias-variance tradeoff than we'd care to admit.

my take on this: The high bias, low variance means that the models used in the book are the standard used in physic. The low variance means that the author doesn't use any artificial model or parameter except those that arise from physical considerations. Care is taken to motivate physically ML models and those far from physic can't be used with many parameters (hyperparameters).

The "model" presented for ML is low complexity, hence lives on the high bias, low variance side of the space of possible models.