Lecture
Classification with GMM and kNN
Description
This lecture covers the concepts of Mixture of Gaussians and Bayes' classification, illustrating how to determine the class of data points by comparing likelihoods under Gauss functions. It also discusses the impact of different numbers of points on classification boundaries and the computational costs of Gaussian Mixture Models. Additionally, it explores the effects of overfitting and underfitting in k-Nearest Neighbors (kNN) classification, emphasizing the importance of model evaluation and selection criteria.
Official source
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