Multiple Features Note: [7:25 - \theta^TθT is a 1 by (n+1) matrix and not an (n+1) by 1 matrix] Linear regression with multiple variables is also known as "multivariate linear regression". We now introduce notation for equations where we can have any number of input variables. x(i)jx(i)mn=value of feature j in the ith training example=the input (features) of the ith training example=the number o..