Karl Ni | day 2 - question

Derive the maximum likelihood estimate of the mean and variance of a dataset that is distributed as a Gaussian. So, let the dataset consist of data points $x_i$ . That is,

$\mathcal{D} = \{ x_1, x_2, x_3, \cdots \}$

where $x_i \in \mathbb{R}^d$ . What is the maximum likelihood estimate of $\mu$ and $\Sigma$ , provided that

$x_i \sim \mathcal{N}(\mu, \Sigma)$ ?