A hypothesis is a statement that suggests an as yet unproven explanation of a relationship between two or more phenomena that you intend to test. An agronomist thinks that more nitrogen on canola will always increase the crop output $$Harvest = f(N)$$, or a meteorologist thinks he can show that the path of a hurricane over the ocean can be determined by knowledge of the sea temperature and the wind speed at an altitude of 1000 feet one minute before. $$D(t,0) = f(T(t-1,1000),S(t-1,1000)$$
Both hypotheses are pegs on which later steps are based; testing follows with a conclusion whether the hypothesis can be rejected or not.
Changing a hypothesis can be simply adding or subtracting arguments to the function or changing the nature of the relationship such as the acceleration of the wind as opposed to its velocity.
A "learning" algorithm describes how the parameters of a numeric model are changed in accordance with the delta rule, that is what the learning rate is and whether momentum is to be applied.
Random Forest and Decision Tree are "classification" algorithms. They are clearly stepwise processes that proceed towards the goal of a model, but they start by specifying the shape that the model will take and place boundaries on what values the parameters may take.
Both learning and classification algorithms specify a priori what shape the model will take and by doing so limit its relevance to particular problems.