Are concept and hypothesis the same in the context of concept learning?

Question

The lecturer is computing the size of concept space, regarding the EnjoySport example in Tom M. Mitchell. Machine Learning (free)

 Sky: sunny, cloudy, rainy
 Air Temp: warm, cold
 Humidity: normal, high
 Wind: strong, weak
 Water: warm, cold
 Forecast: same, change

the result that professor gives is $2^{96}$.

I am not sure if concept and hypothesis are the same in the context of concept learning, yet I have no idea about the difference between them.

assume they are the same, then the size of that concept space is the size of the size of the hypothesis space in the EnjoySport learning task, which is 973, per EXERCISE 2.1 in that book.

What am I missing?

Answer 1

Quoted from wiki:

A concept is an idea of something formed by combining all its features or attributes which construct the given concept. Every concept has two components:

• Attributes: features that one must look for to decide whether a data instance is a positive one of the concept.
• A rule: denotes what conjunction of constraints on the attributes will qualify as a positive instance of the concept.

So to say a hypothesis can be same as the learned concept but the concept may combine several hypothesis for correct classification as well.