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What is Understanding the EM Algorithm in ML about?

The EM algorithm, proposed in 1977, is a latent variable model used to find maximum likelihood estimates for unobservable variables in statistical models. It operates through two main steps: the Expectation step (E-step) estimates missing data, while the Maximization step (M-step) updates model parameters based on this data. The algorithm has various applications in machine learning, including data clustering, computer vision, and natural language processing.

Author

Parth Singh

Language

EN