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Most ML is disjoint from the current NN trends, IMO. Compare Bishop's PRML to his Deep Learning textbook. First couple chapters are copy+paste preliminaries (probability, statistics, Gaussians, other maths background), and then they completely diverge. I'm not sure how useful classical ML is for understanding NNs.

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That's fair. My understanding is that NN and ML are similar insofar as they are both about minimizing a loss value (like negative log likelihood). And then the methods of doing that are very different and once you get even more advanced, NN concepts feel like a completely different universe.

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