stats:
- Bayesian statistics and Markov chains
- ANOVA
- how do you decide what sample size is big enough?
- likelihood ratio tests
- go through all of the different distributions. I only really know the normal one :) (this is a really cool question actually! Understanding what distributions come up in real life and how to handle them is super interesting .)
ML:
- Practical guide on how to prevent overfitting (and navigating its trade offs)
- "machine learning algorithms"
questions where I don't really know where to start:
- dynamic monte carlo methods
- sampling bias
- what is standard deviation (other than "well here's the definition" -- why do we use the standard deviation actually?)
- how to choose the right hypothesis test!