Machine learning is not just glorified statistics

The post makes two main claims

"Machine Learning Does Not Require An Advanced Knowledge of Statistics"

Lets take it at its face value, even then it does not preclude ML from being glorified stats. Depending on how advanced the 'advanced' is in that statement I would agree with it. To use ML tools well you do need some familiarity with understanding how uncertainty effects the results and that is as up one can be statistics alley. I say this although I am firmly in the ML side of the tribe. BTW I would even claim this

"[Practicing] Machine Learning Does Not Require An Advanced Knowledge of Machine Learning"

The other claim that the post makes is

"Machine Learning = Representation + Evaluation + Optimization"

Whoah! big blind spot there. Dealing with and reasoning about uncertainty, generalization is a big deal in ML. Sure, it helps to argue that ML and stats is different if one ignores that bit.

I do think ML and Stats is different but not for those reasons.

Yeah ML brings to bear some tools that card holding statisticians have traditionally not used in anger before, for example, advanced and large scale optimization, algorithms, data structures.

Using new tools to address the same question does not qualify as a deep difference in my books. For example, even the fields of optimization and algorithms themselves use tools that are different from what the tools were 50 years ago. I think the main difference is in the questions that ML and Stats wants to answer, and here there indeed are differences.

Stats (barring edge cases) is primarily interested in going from a sample to making claims about the population, or making claims about something via a claim about the population. ML is primarily interested in going from a sample to another sample. (Do note sample is a collective noun.) Now, there have been statisticians (fewer in number) and a body of statistics literature that has focused on prediction as opposed to parameter recovery, but that's not main stream in Stats.

And finally in pseudo-quotes "I managed to train a model without knowing what variance is. I know variance is statistics. ML is not Statistics ... QED ". Ah I see, nevertheless, not what I would call a brilliant case of logical deduction".

By definition machine learning is Statistics + Decision Theory. Statistics simply tells you what information you have about the world, not what to do with that information.

Yes it is. Having a bigger model state makes it more complex. It doesn’t make it not statistics.

There is a difference between 'can' and "is the central question". As I said in my comment [0], traditionally statistics is about making claims about the nature of the population from a sample. If one is operating with a parametric model, the running assumption is that one knows absolutely everything about the population except for a few parameters. Those are estimated from the data. One you have done that, one can indeed use those to do prediction and if you were lucky that the data did come from the distribution assumed, then you wont do too badly, otherwise you could perform very poorly.

ML on the other hand short circuits the step of making any claims about, or even trying to understand the population. ML straight away jumps to claims about prediction quality. Note there is non-parametric statistics, but even there (barring edge cases) the central object of inquiry is the population. There is also prequential statistics that also jumps directly to claims about prediction without making any claims about understanding the population. Prequential stats is fascinating, and it is what is closes to ML in spirit.

[0] https://news.ycombinator.com/item?id=18591143

One branch of statistics is about understanding the past. Statisticians are very much interested in predicting the future and have methods that look nothing like ML.