Q:

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:
 

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)?  

 

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)  

 
Thank you!

A:

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. 

