Support Vector Machines: Soft Margin

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Introduces logistic regression for binary classification and explores multiclass classification using OvA and OvO strategies.

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Covers the concept of multiclass classification and the challenges of linearly separating data with multiple classes.

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

Support Vector Machines: Parameters, Solutions, and Boundaries

Explores SVM parameters, solutions, and decision boundaries, including the uniqueness of solutions and the impact of kernel width.

Support Vector Machines: Linear Separability

Explores linear separability in support vector machines, focusing on hyperplane separation and margin optimization.

Kernel Methods: SVM and Regression

Introduces kernel methods like SVM and regression, covering concepts such as margin, support vector machine, curse of dimensionality, and Gaussian process regression.

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Explores maximizing margins for better classification using support vector machines and the importance of choosing the right parameter.

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