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Core
Gaussian Mixtures and the EM Algorithm
A Gaussian mixture model treats data as generated by several Gaussian components and assigns each point a soft, probabilistic membership rather than a hard cluster label. Expectation-maximization fits it by alternating between computing those memberships and re-estimating each component. Interviews probe it because it is the cleanest example of a latent-variable model and exposes whether a candidate understands soft clustering, local optima, and how GMM generalizes k-means.
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Coding & DSAImplement a Gaussian Mixture Model with EM from scratch: E-step responsibilities, M-step updates.→Coding & DSAImplement k-means from scratch, including k-means++ initialization and a convergence check.→Machine Learning & Data ScienceExplain the EM algorithm and walk through it for a Gaussian Mixture Model.→SQL & Data EngineeringHow do you choose a partitioning and clustering strategy for a large analytics table?→Machine Learning & Data ScienceCompare clustering methods: k-means, hierarchical, DBSCAN, and GMM.→Machine Learning & Data ScienceWhen does DBSCAN beat k-means, and how do you evaluate clusters with no labels?→
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