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Explores Transductive Support Vector Machine for semi-supervised clustering, aiming for zero error on labeled points and well-separated unlabeled points.
Introduces linear regression, covering line fitting, training, gradients, and multivariate functions, with practical examples like face completion and age prediction.
Explores learning the kernel solution in convex optimization, focusing on predicting outputs using a linear classifier and addressing possible numerical issues.
