That's right. I think the approach that is lacking is best explained in the submission guidelines for the Journal of AI Research:
>> Papers describing systems should clearly describe the contributions or the principles underlying the system. Papers describing theoretical results should also discuss their practical utility. In general, it should be clear how the work advances the current state of understanding and why the advance matters. Papers should report on what was learned in doing the work, rather than merely on what was done.
https://www.jair.org/index.php/jair/about/submissions
Doing stuff is not science, even if it's doing stuff that's never been done before. In science, we experiment to understand why stuff can be done, and how, not just that it can be done. And of course it matters what is being done, so for example experimenting to find the best combination of spices for a dish is not an instance of science.
but do not forget the apparent point in the original poster's is the lacking approach below the implementations of above said techniques.
The techniques you listed in a substantial way expanded our knowledge ("oh, we can do this ... and solve more problems") while raising little more questions.
The said implementations on topic give results but raise many more unanswered questions.
(This post is unfortunately an "immature" reply - just a provisional tentative point. The matter of "ML algorithms ∩ science" requires more time and concentration and consultation of the texts of the Great Ones.)