I’m pretty much self taught software engineer so I probably know about machine learning more than the average person, but haven’t really tried to use it in any way.
Since I skipped uni (most unis would have statistics in the curriculum), I haven’t really touched statistics either and all I know about it is how to make a graph in excel.
Definition of statistics:
> the practice or science of collecting and analysing numerical data in large
> quantities, especially for the purpose of inferring proportions in a whole from
> those in a representative sample.
And machine learning does exactly that. Collects and analyses some data and infers whatever it was trained to infer.
My understanding is, that it actually is applied statistics, but I’ve read some articles that say it isn’t. I didn’t really understood why.
I understand the data gathering is different, the problems it’s trying to solve are different and even the “how it works behind the scenes” is different. But I don’t see how it’s abstract core differs.
so .. why ML isn’t applied statistics?