I think the author means a non-ML expert interest in using ML. For example a geologist who wants to apply ANNs her field data
Said geologist, would likely run into the problems I describe above if looking for that ML SME.
In this context, the geologist is the SME. The "subject matter" is the geological application, not Machine Learning. The whole point of this document is advice for people who are SME's in some domain, but not ML experts, but who are working on applying ML in their domain.
This report is targeted to groups who are subject matter experts in their application but deep learning novices.
Of course it goes without saying that, in a ideal world, you'd have both domain SME's and ML experts working on the problem together, but given that we don't live in an ideal world, the point here - at least as I read it - is to help the non-ML-expert folks get started applying Deep Learning.
And for all the stuff we can say about the difficulty of applying DL and the need for experts, the reality is that you don't actually always need a "deep learning expert". Let's say your problem just happens to be similar to, say, handwritten digit recognition. Given that anybody can download DL4J, go through the tutorials, and get a network going in an hour or two that gives something like 98%+ accuracy, there's a good chance that our "geologist who's a DL novice" can create a network that will yield useful results.
Maybe getting the last couple of percentage points of accuracy out will require a "real" DL expert, but hell, depending on the scenario, that might not even be needed.
the point here - at least as I read it - is to help the non-ML-expert folks get started applying Deep Learning.
If that is in fact the case, I would argue that this document is not really giving that user the best starting point - though they get points for trying.
As a practitioner perhaps I am biased, and our applications are on the boundaries of solved problems (though include some reasonably solved CV solutions) - however I would argue that unless your application is strictly the simplest and can use off the shelf solutions, for example simply implementing Google Cloud Vision API or the Microsoft Cognitive Computing API - you're going to need someone with years of study/practice with ML to get to a good outcome.
It's also disambiguated by the author saying "subject matter expert for your application" (application being an application of machine learning, eg to radiology, geology...), and saying "subject matter experts in their application but deep learning novices" in the paper's abstract.