I'm interviewing for some quantitative researcher positions at some hedge funds, and I've been told that there will be one interview session focused on stats, and one focused on ML, among others. This made me realize that I have a hard time distinguishing between stats and ML because there's such a great deal of overlap, although I think I have some kind of idea of what parts of stats might not be typically apart of ML.
I took stats courses in college and high school, and never were the words "machine learning" mentioned in those courses. In those courses, I recall more of a focus on things (most of which I have forgotten) like hypothesis testing, basic probability and common distributions, confidence intervals, bar/box plots/histograms, and univariate regression.
What part of stats would you consider to not typically be part of ML and are important for practical data inference/analysis/prediction in the financial industry, particular at hedge funds / prop trading firms?