BEWARE "these three pillars of machine learning"

BEWARE this concept : there are various accounts of what are the "three pillars of machine learning", and so far I haven't seen one which is properly MECE, though there are some decent ones. 

One account which says that the three pillars are

• 1. supervised learning
• 2. unsupervised learning
• 3. reinforcement learning

... and the MECE structure is not always laid out clearly. 

The space which these actually refer to :

• Axis 1 : training inputs are pre-determined, vs. undetermined
• Axis 2 : training goals are concrete, vs abstract

... and what they actually refer to :

1. supervised learning :

• - pre-determined inputs
• - goals are concrete

2. unsupervised learning :

• - pre-determined inputs
• - goals are abstract "just put things that look the same, together, and give me a report"

3. reinforcement learning :

• - both pre-determined ( closed world ) and undetermined ( open world ) inputs
• - goals are concrete "you get points based on specific criteria"

Roughly corresponding to the Johari window : 

• supervised learning : figure out for me
  [ what I know,
  [ that I know ] ]
• unsupervised learning : figure out for me
  [ what I don't know,
  [ that I know ] and [ that I don't know ] ]
• reinforcement learning : figure out for me
  [ what I know,
  [ that I don't know ] ]