What is the hypothesis space of decision tree learning?
As per Tom Mitchell's,

".....For example, consider the space of
hypotheses that could in principle be output by the above checkers learner. This
hypothesis space consists of all evaluation functions that can be represented by
some choice of values for the weights wo through w6. The learner's task is thus to
search through this vast space to locate the hypothesis that is most consistent with
the available training examples....."

Hence , Basically all possible combination of distinct trees makes the hypothesis space. 

Lets say if you have chosen to represent your function to be a linear line then all possible linear lines which go through the data (given input, output) makes up your hypothesis space.

Each tree= Single hypothesis , that says this tree shall best fit my data and predict the correct results

therefore combination of all such possible tress= hypothesis space.

Here is the snippet of PPT from lecture