Lecture
This lecture covers linear models, including hyperplanes, multi-output prediction, logistic regression, decision boundaries, and maximum margin classifiers. It also delves into k-Nearest Neighbors (k-NN) for classification and regression, discussing properties, algorithms, and examples. The curse of dimensionality and approximate k-NN methods are explored, along with practical applications in authorship attribution and image data analysis.