What does the Ip mean in the Bayesian Ridge Regression formula? From http://scikit-learn.org/stable/modules/linear_model.html#bayesian-ridge-regression, they gave the bayesian ridge distribution as this:

$p(w|\lambda) = \mathcal{N}(w|0,\lambda^{-1}{I_{p}})$

And there is a variable $I_p$ but it's unexplained what does the $I_p$ refer to?

Also, the variable $\mathcal{N}$ is unexplained but I'm not sure whether I've guessed correctly but is that the Gaussian prior as described in the Bayesian regression section above the Bayesian Ridge?
$\mathcal{N}$ does indeed denote a (multivariate) normal / Gaussian distribution. $I_p$ is just an identity matrix of dimension $p$. So this a matrix with $\lambda^{-1}$ along the diagonal. Read this as the covariance matrix, so this is a spherical Gaussian (0 covariance between different dimensions) where each variable has variance $\lambda^{-1}$.