Nonlinear SVM: Kernels and Dual Optimization

Related lectures (32)

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

Explores kernel tricks in support vector machines for efficient computation in high-dimensional spaces without explicit transformation.

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

Covers the concept of kernel regression and K-nearest neighbors for making data linearly separable.

Explores Kernel Ridge Regression, equivalent formulations, Representer Theorem, Kernel trick, and predicting with kernels.

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

Explores SVM extensions, RVM, Transductive SVM, and support vector clustering in advanced machine learning.

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

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

Explores the challenges and insights of deep learning, focusing on loss landscape, generalization, and feature learning.