Maximum Likelihood Estimation

Priliminaries A Simple Linear Regression Least Squares Estimation linear algebra Square Loss Function for Regression1 For any input (mathbf{x}), our goal in a regression task is to give a prediction (hat{y}=f(mathbf{x})) to approximate target (t) where the function (f(cdot)) is the chosen hypothesis or model as mentioned in the post https://anthony-tan.com/A-Simple-Linear-Regression/.
The difference between (t) and (hat{y}) can be called ‘error’ or more precisely ‘loss’. Because in an approximation task, ‘error’ occurs by chance and always exists, and ‘loss’ is a good word to represent the difference.