Uncovering Hidden Clusters: A Hands-On Guide to the EM Algorithm for Gaussian Mixture Models
Unsupervised clustering is a fundamental problem in machine learning: how can we find meaningful groups in data without any pre-existing labels? A powerful answer to this is the Gaussian Mixture Model (GMM), which assumes that the data is generated from a mix of several Gaussian (or normal) distributions. The challenge, however, is to find the parameters of these hidden distributions.
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