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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 10: GMM (EM), PCA Jul 31, 2026 · 1:20:06 This lecture covers GMM via EM and PCA. It details how EM uses Jensen's inequality to construct a lower bound for optimization, while PCA finds maximal variance directions using Eigen Decomposition of the covariance matrix.

Stanford Online

Stanford CS229 Machine Learning | Spring 2026 | Lecture 10: GMM (EM), PCA

This lecture covers GMM via EM and PCA. It details how EM uses Jensen's inequality to construct a lower bound for optimization, while PCA finds maximal variance directions using Eigen Decomposition of the covariance matrix.

Read article

Stanford Online

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.

Read article