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Clustering Algorithms

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Stanford CS229 Machine Learning | Spring 2026 | Lecture 9: K-Means and GMM (non-EM) Jul 31, 2026 · 1:16:30 This lecture covers unsupervised learning, contrasting K-Means clustering with its probabilistic extension, Gaussian Mixture Models (GMM). The core technique for solving GMM is the Expectation-Maximization (EM) algorithm, which relies on convex relaxation and Jensen's inequality.

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Stanford CS229 Machine Learning | Spring 2026 | Lecture 9: K-Means and GMM (non-EM)

This lecture covers unsupervised learning, contrasting K-Means clustering with its probabilistic extension, Gaussian Mixture Models (GMM). The core technique for solving GMM is the Expectation-Maximization (EM) algorithm, which relies on convex relaxation and Jensen's inequality.

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