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What is Implementing GMM with EM Algorithm about?

This document describes implementing the expectation-maximization (EM) algorithm for Gaussian mixture models (GMM). It provides background on EM, outlines the E-step and M-step, and gives tasks to complete the E-step and M-step code. Numerical tricks are discussed like avoiding overflow in the E-step. Matrix derivatives needed for the M-step optimization are also reviewed. The goal is to learn the parameters (priors, means, covariances) of a GMM from sample data of unknown origin.

Author

ankurkothari

Language

EN