A query regarding vector portability in hypothesis functions (Stanford Computer Learning Lecture 2)

I watched a lecture from a series of 2 lectures on the study of machines at Stanford taught by Professor Andrew N. NG, and I had a question about something which, in my opinion, might be rather rudimentary, but just don’t click in my head. So, we consider two vectors θ and x, where both vectors contain real numbers.

Let h (x) be a function (in this particular case, called a hypothesis), but let it be some function denoted by:

h(x) = "summation from i = 0 to i = n" of θ(i)*x(i) = θ(transpose)*x

I do not understand the last part, where he says that h (x) is also equal to θ (transpose) * x.

If someone could clarify this concept for me, I would be very grateful.