Support Vector Machine: Primal Formulation with Hard Margin

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Explores orthogonality, Gram-Schmidt process, dot products, and solution minimization in systems.

Covers the concepts of dot and cross products of vectors and their applications.

Covers vector components, projections, conventions, operations, and mathematical identities.

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

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

Explains how to find the best hyperplane to separate two classes.

Covers trigonometric functions, derivatives, differential equations, and vector properties.

Covers vector calculus, including scalar and vector products, with insights from Dr. Sylvain Bréchet.

Covers the properties of vectors, including commutativity, distributivity, and linearity.

Covers the vector product and its geometric interpretation in a Cartesian coordinate system.