Explain 'Expectation-Maximization' (EM) Algorithm.

Explain 'Expectation-Maximization' (EM) Algorithm.

Approach: Describe an iterative method for finding maximum likelihood estimates in models with latent variables.

An iterative method to find maximum likelihood estimates of parameters in statistical models where the model depends on unobserved latent variables. E-step: calculate expected value of latent variables. M-step: maximize likelihood given those expectations.

Follow-up: How is EM used in Gaussian Mixture Models (GMMs)?

Key concepts: latent variables, e-step, m-step, gmm.