Support Vector Machines: Kernel SVM

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Page 3 of 4

Covers feature maps, Representer theorem, kernels, and RKHS in machine learning.

Explains Schur's Lemma on unitary representations and their irreducibility and invariance properties.

Explores Support Vector Machines theory, parameters, uniqueness, and applications in machine learning.

Covers feature selection, kernel regression, and neural networks through exercises.

Covers kernels and images of linear transformations between vector spaces.

Covers exercises on regression functions using RLS, WLS, and LWR.

Explores optimal transport and gradient flows in Rd, emphasizing convergence and the role of Lipschitz and Picard-Lindelöf theorems.

Covers supervised learning, classification, regression, decision boundaries, overfitting, Perceptron, SVM, and logistic regression.

Explores feature expansion, kernel methods, SVM, and nonlinear classification in machine learning.

Delves into the theory and optimization of Support Vector Machines, including Mercer's Theorem and Lagrange Duality.