Clustering (Kmeans Vs. Gaussian Mixture Models) Lecture
In this lecture, we will introduce clustering as an unsupervised learning task. We will study in detail the Kmeans clustering algorithm, one of the most popular clustering approaches. Then, we will present a more generalized method for estimating the parameters of underlying distributions in the data, Gaussian Mixture Model (GMM). Finally, we will demonstrate how we can apply the Expectation-Maximization (EM) algorithm to estimate the parameters of GMM.
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Event Expiry Date - Wednesday, November 18, 2020