## 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....

February 15, 2020 · (Last Modification: April 28, 2022) · Anthony Tan