what are the top level subsets/domains of ML? I'm not really happy with the mind maps I've been able to find on Google, most of them are algorithm based.  I want to make a good one that is problem/solution domain based.  Do I have this right for my top level nodes?  Here is the general direction I am headed: https://imgur.com/gallery/CugcS

My questions/doubts about what I have so far are:

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Is my starting point below generally correct?  e.g. no high level subclass is missing, and everything presented as a subclass deserves to be here?
is Hybrid learning always just a combination of supervised and unsupervised?  Or, are there real examples of other hybrid models (e.g. 'reinforcement' and 'supervised', etc.).  I know theoretically we can combine any methods...I'm looking for what's real/applied/demonstrable today.
does Reinforcement learning belong at this high level, or is it actually a subset of one of the others (or one I've omitted)?  


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Machine Learning

1.1 Supervised (uses labelled data to train and validate)

1.2 Unsupervised (uses unlabeled data, or ignores labels if they are present)

1.3 Semi-supervised (uses partially labelled (mostly unlabeled) data)

1.4 Hybrid (combines a supervised method and an unsupervised method)

1.5 Reinforcement Learning (uses data from the environment)  


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Thank you!
Google (in images!) on 'machine learning cheat sheet', to find an example like this:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/algorithm-cheat-sheet

So you are well on your way in identifying all kinds of techniques, but you may want to turn your plot 'sideways', e.g. two-way / multi-class classification are problem (sub)domains and result in different recommended machine learning techniques.