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This lecture covers the K-Nearest Neighbors (KNN) classifier, a simple machine learning algorithm that assigns a label to a new point based on the labels of its closest known points. It explains the probabilistic approach of KNN to smooth away noise in labels, the classification process with KNN, the impact of different distance metrics on decision surfaces, and the advantages of KNN such as simplicity and not requiring assumptions about data distribution.