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I was taught previously in statistics class, that the equation for simple linear regression model :

E(Y)=Beta0+beta1*x1

I recently took up a course on machine learning and in that the equation is represented by:

H theta(x)=theta0+theta1*x1

Moreover in the statistics class to measure the performance of the model, we used R-squared or adjusted R-squared, which was just SSR/SST, but here we use the cost function, which is 1/2m(H(x theta)-y theta)squared. I understand that the intuition behind everything still remains the same, with least squares regression being the technique, but why the change in the symbols, it was very easy in stats but here in machine learning they look to be a little complex. So why did they do that?

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