k-means clustering is a method used to partition observations into clusters based on their proximity to the mean of each cluster. It minimizes within-cluster variances and can be computationally difficult, but efficient heuristic algorithms exist. It is often confused with the k-nearest neighbor classifier, but they have a loose relationship as the nearest centroid classifier can be applied to the cluster centers obtained by k-means.
Stanford University
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This comprehensive course covers various machine learning principles from supervised, unsupervised to reinforcement learning. Topics also touch on neural networks, support vector machines, bias-variance tradeoffs, and many real-world applications. It requires a background in computer science, probability, multivariable calculus, and linear algebra.
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+ 32 more conceptsPrinceton University
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This introductory course focuses on machine learning, probabilistic reasoning, and decision-making in uncertain environments. A blend of theory and practice, the course aims to answer how systems can learn from experience and manage real-world uncertainties.
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+ 21 more conceptsStanford University
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Stanford's CS 221 course teaches foundational principles and practical implementation of AI systems. It covers machine learning, game playing, constraint satisfaction, graphical models, and logic. A rigorous course requiring solid foundational skills in programming, math, and probability.
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