No - layers in neural networks are often a linear map followed by component-wise non-linear maps. The resulting operation is non-linear.

Note that without the non-linear part each layer would just be a single linear map. The composition of multiple layers would then be a linear map again, so there would be no need for multiple layers in a model.

There are many ML models that are not neural networks.

Absolutely, but this was the clearest counter-example I could think of.

A Bayesian-updated linear regression system will indeed give you a linear inference step and count as ML in my opinion.

Is this a homework question?

1. Why is it matter?
2. No, just curiosity

It matters, because this sub isn't to answer homework questions.

I asked, because the way you asked somehow gave me the exact vibes I get when my students ask me questions when they are trying to get me to solve the problem for them.

You're right, of course. I think I just misunderstood what you were getting at with your answer when I made that comment.