So I've been watching Andrew Ng's machine learning lectures, and I'm on a video about univariate linear regression. He was talking about how a Hypothesis takes an input and predicts an output, like a typical function we learn in math class such as $f(x) = mx + b$, where f is a function with input x that outputs a line with a slope of m and a y-intercept of b. However, Ng said that the general hypothesis equation in linear regression is $h_\theta(x) = \theta_o + \theta_1 x$. I get that this is a function h of input x, and it looks like $\theta_1 x$ is equivalent to $mx$ while $\theta_o$ is equivalent to $b$, but why use all the thetas instead of the other variables? Is $\theta_1$ a slope like $m$? Why use theta multiple times? What is the meaning of the subscripts?