# Why does Andrew Ng use the form h(x) = Theta subscript 0 + Theta subscript 1 x instead of the more common slope-intercept form?

In this course on machine Learning by Stanford, Andrew Ng describes a function for a line as h(x) = Theta subscript 0 + Theta subscript 1 x instead of the more common slope-intercept form y = mx + c.

Thanks to a previous question, I now understand how both forms represent a line (via the y intercept and slope) but am unclear why Andrew Ng decided to use the Theta form of the equation rather than the more common slope-intercept form.

Can anyone help me understand this please?

• $h(x)=\theta_0+\theta_1x$ is a common slope-intercept form. He just decided to name the parameters $c$ and $m$ differently. Sep 2, 2018 at 9:20
• I've never done Ng's ML course, but I'm guessing it is because that notation is commonly used in the context of gradient descent and cost functions. Sep 2, 2018 at 9:31
• notation like $\theta_0+\theta_1 x$ generalises much better than $c+mx$ to the situation where there are multiple variables, which is going to be of interest in ML. But really it's a purely notational choice with no meaning. Sep 2, 2018 at 9:31
• Someone downvoted this question. Any ideas why? It was a lefitimate question and the answers have helped me enormously. So I am curious if I did something contrary to protocol here? Sep 3, 2018 at 17:46