Machine learning is not just glorified statistics

>Throughout the class, my fellow students and I successfully trained models

On top of that, arguing that training models is some sort of contribution to the field?

In High School Statistics, we were taught how to do everything by hand, with arithmatic. Then after we demonstrated we understood the formula, how it worked, and why, the teacher would show us that the graphing calculator had a function to do it automatically all along. Rinse and Repeat over the duration of the course. From then on, we could use the prewritten function.

Training a model is skipping to executing the function, without first learning how to write it. Is training a model even a feat worth bragging about?

>Yet, if you had asked me, or most of the students in that class, how to calculate the variance of a population, or to define marginal probability, you likely would have gotten blank stares.

But if I showed you the automatic function in the calculator or excel, and you understood how to input the paramaters correctly, I would be disappointed if you COULDNT get it to work.

>Information theory, in general, requires a strong understanding of data and probability

Probability theory is the underlying foundation of statistics. If you are going to argue that ML isnt Statistics, youre still agreeing that they are both subsets of probability theory.

>How could someone with virtually no background in stats develop a deep understanding of cutting-edge ML concepts?

Because you either a) learned statistics under a different name or b) learned how to input data into someone elses black box formulas and take credit for the output. Overall, this was a pretty painful read. The writer should check out a Philosophy of Science or Logic course. And are you really claiming a deep understanding of the concepts, or are we back to you can input data into someone elses function, and think the output is novel.

> as if to distinguish themselves from the regular, less statistical kind. Furthermore, most of the hype-fueling innovation in machine learning in recent years has been in the domain of neural networks, so the point is irrelevant.

.....what is an example of this "less statistical kind" of Machine learning? Am I taking crazy pills, neural networks are still probabilistic models. Giving such significance to the difference between AI vs ML vs deep learning vs neural networks, probably demonstrates more than anything, not knowing the unifying and underlying probability theory that well.

>Batch normalization? ... (probably because they are not statistical techniques at all).

Are you really arguing that Batch normalization isnt an application of NORMALIZATION, you know, from statistics??

So the author is asserting that... ML is statistics for people who don’t know statistics? I wouldn’t necessarily argue with that, but I wouldn’t brag about it either...