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What are the most important statistical and ML concepts to know for interviews?

I'm trying to narrow in my interview prep. Some firms seem to test on fundamental statistical and ML concepts. I've heard that OLS is a very commonly tested interview topic. I also recall decision trees, bias-variance tradeoff, boosting/bagging/bootstrapping, to have shown up as well.
What other ML-related knowledge is frequently encountered in interviews?

What about more fundamental stats stuff? E.g., hypothesis testing, confidence intervals, t-tests, etc...?
 
For me personally as a stats major undergrad I’ve always been asked the assumptions behind ols and maybe what does a confidence interval actually mean and maybe even deriving one. I feel like I’m the beginning of you don’t know the fundamentals they will not take you seriously after. Know p value, t stats etc and that will at least get you past the first part of your interview. After you will get asked harder questions which depend greatly on where your interviewing.
 
regression, basic stats, maybe basic unsupervised like PCA, are the most important. depending on the shop, you might have to know more, but everywhere you go you will be expected to know the fundamentals
 
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regression, basic stats, maybe basic unsupervised like PCA, are the most important. depending on the shop, you might have to know more, but everywhere you go you will be expected to know the fundamentals
Do you mind expanding on what "basic stats" entails? I have a hard time distinguishing between stats and ML; however, I feel like "basic" stats typically entails stuff not commonly found in ML.

I would consider the topics in Elements of Statistical Learning to be more on the ML side than stats.
 
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