Maximum likelihood estimates in simple regression

Consider the linear regression model $y_i=\beta_0+\beta_1x_i+\epsilon_i$; $i=1,2,…,n$, where $\epsilon_i$'s are i.i.d. standard normal random variables. Given that

$$\frac1n\sum_{i=1}^nx_i=3.2,\quad\frac1n\sum_{i=1}^ny_i=4.2,\quad\frac1n\sum_{j=1}^n\left(x_j-\frac1n\sum_{i=1}^nx_i\right)^2=1.5 \text{ and}$$

$$\frac1n\sum_{j=1}^n\left(x_j-\frac1n\sum_{i=1}^nx_i\right)\left(y_j-\frac1n\sum_{i=1}^ny_i\right)=1.7,$$

the maximum likelihood estimates of $\beta_0$ and $\beta_1$, respectively, are