Expectation maximization provides an iterative solution to maximum likelihood estimation with latent variables. Gaussian mixture models are an approach to density estimation where the parameters of the distributions are fit using the expectation-maximization algorithm.
For problem description and an rough idea of solving the problem see "Expectation Maximization Algorithm for Gaussian Mixture Model.pdf" file.