Expectation Maximization
Expectation Maximization (EM) is an iterative statistical algorithm used to find maximum likelihood or maximum a posteriori estimates of parameters in probabilistic models, particularly when the data has missing or latent variables. It alternates between an expectation step (E-step), which computes the expected value of the log-likelihood function given current parameter estimates, and a maximization step (M-step), which updates the parameters to maximize this expected log-likelihood. This method is widely applied in machine learning, data science, and statistics for tasks like clustering, density estimation, and handling incomplete data.
Developers should learn Expectation Maximization when working with probabilistic models involving hidden variables, such as in Gaussian Mixture Models for clustering, Hidden Markov Models for sequence analysis, or in scenarios with missing data like in recommendation systems. It is essential for unsupervised learning tasks where data labels are unavailable, enabling parameter estimation in complex models that would otherwise be intractable. Use cases include image segmentation, natural language processing (e.g., topic modeling with Latent Dirichlet Allocation), and bioinformatics for gene expression analysis.