Support Vector Machines: Parameters, Solutions, and Boundaries

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Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.

Covers linear models for classification, focusing on logistic regression and support vector machines.

Explores linear models, logistic regression, classification metrics, SVM, and their practical use in data science methods.

Covers linear models for classification, including SVM, decision boundaries, support vectors, and Lagrange duality.

Explores linear models for classification, logistic regression, SVM, k-NN, and curse of dimensionality.

Covers fundamental machine learning concepts including Structure Discovery, Classification, and Regression.

Covers feature expansion, kernels, SVM, and nonlinear classification in machine learning.

Covers linear models, logistic regression, decision boundaries, k-NN, and practical applications in authorship attribution and image data analysis.

Covers solutions to SVM exercises, discussing optimality conditions, decision functions, and parameter impacts.

Explores Support Vector Machines, covering hard-margin, soft-margin, hinge loss, risks comparison, and the quadratic hinge loss.