##### [Mixture Models] Maximum Likelihood of Gaussian Mixtures

Gaussian mixtures had been discussed in 'Mixtures of Gaussians'. And once we have training data and a certain hypothesis, what we should do next is estimating the parameters of the model. Both kinds of parameters from a mixture of Gaussians

##### [Linear Regression] Maximum Likelihood Estimation

To any input $\boldsymbol{x}$, our goal in a regression task is to give a prediction $\hat{y}=y(\boldsymbol{x})$ to approximate target $t$ where the function $y$ is the chosen hypothesis. And the difference between $t$ and $\hat{y}$ can be called 'error' or more precisely 'loss'.