This is a bit of a weird question.
I am hoping to create an online reference since I have some downtime. I know some about statistics but very little about computer science. As a result, the reference guide I am hoping to create will be very statistics oriented - even though I wish that it could be a reference for someone who wants to start from scratch and work their way to AI.
While I would love to be involved with AI, from what I have read about ML and AI, seems like AI does not involve much statistics. (A lot of statistical theory is based off normal assumption and math, and ML seems to bypass that by not requiring strong assumptions nor analytical results). CS seems to be more relevant.
And so my question is, since my guide will mostly cover statistics, how relevant would it be for someone who wants to get into AI? If it's not relevant, then I guess I'll just make my guide for someone who wants to get into stats/data science, as opposed to someone who wants to be an AI researcher.
I guess another way to phrase my question is, as an AI researcher, when you "google" stuff, wikipedia things, or go to your notes, what subjects are you looking at and what exactly are you googling? Are you getting a refresher on how to code back propagation? Or are you getting a refresher on the pros and cons of L1 vs. L2? Do you ever look at how to implement a boosting tree or NN using a pre-existing package?
Basically, I know that what I can provide will be relevant to HS/college stats and data science students. But what really want to do is create something useful for aspiring/current AI researchers. The former is realistic, the latter is a dream. I want to see if my dream is realistic.