Part 1 - context. Two contextual examples illustrating when we might want to use the EM algorithm.

Part 2 - theory. Explaining the fundamental theory behind the EM algorithm.

Part 3 - Gaussian Mixture Model E-step. Showing how the Expectation step is applied for the case of a Gaussian Mixture Model.

Part 4 - Gaussian Mixture Model M-step. Showing how the Maximisation step is applied for the case of a Gaussian Mixture Model.

Part 5 - Missing Data E-step. Showing how the Expectation step is applied for a missing data problem.

Part 6 - Missing Data M-step. Showing how the Maximisation step is applied for a missing data problem.