  We study multivariate Gaussian models that are described by linear conditions
on the concentration matrix. We compute the maximum likelihood (ML) degrees of
these models. That is, we count the critical points of the likelihood function
over a linear space of symmetric matrices. We obtain new formulae for the ML
degree, one via Schubert calculus, and another using Segre classes from
intersection theory. We settle the case of codimension one models, and
characterize the degenerate case when the ML degree is zero.
