It's just because you're having a hard time seeing that most ML is linear algebra at the core.

No.

I mean it's true that ML uses linear algebra. But the difference between ML and PageRank is that ML is Machine Learning. The learning part (involving storing and updating based on new data) is different to statically calculating Pagerank using Map/Reduce and applying it as a ranking function.

Isn't updating parameters(in ML) same updating some ranking function parameter. In my opinion any algorithm that update its model parameter based on data is ML.

This is true, but in this case it was months between updates (in the early days of Google).

I take a pretty broad view of ML, but there certainly is a difference in degree if not principle.

Most machine learning in use today is “train once (and maybe finetune) and then deploy“, not online, continuous learning. I don’t think the frequency of model updates is generally a good indicator of whether or not something is considered machine learning.

More to the original point, nowadays when most people thick of machine learning they are thinking of deep neural networks, whereas Google‘s original pagerank was very simple and shallow by comparison. But they built an algorithm that allowed machines to learn what pages were high value and what pages were low value. If that seems simple by today’s standards, it’s evidence of the AI goal posts moving more than anything else.