Support Vector Machines: Theory and Applications

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Page 2 of 4

Introduces Support Vector Machines, covering Hinge Loss, hyperplane separation, and non-linear classification using kernels.

Covers kernel methods, kernel regression, RBF kernel, and SVM for classification.

Explores Support Vector Machines, maximizing margin for robust classification and the transition to soft SVM for non-linearly separable data.

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

Covers linear models for multi-class classification, focusing on logistic regression and evaluation metrics.

Covers the fundamentals of neural networks, focusing on RBF kernels and SVM.

Covers the perceptron model and backpropagation algorithm in neural networks.

Covers the history and applications of SVM, as well as the construction of linear classifiers and the concept of classifier margin.

Covers the basics of Support Vector Machines, including linear separability, hyperplanes, margins, and non-linear SVM with kernels.

Explores the Gaussian Discriminant Rule for classification using Gaussian Mixture Models and discusses drawing boundaries and model complexity.