Lectures related to Support Vector Machines: Maximizing Margin

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

Covers non-convex optimization techniques and their applications in machine learning.

Explores convexifying nonconvex problems through SVM and dimensionality reduction techniques.

Explores Support Vector Machines, covering hard-margin, soft-margin, hinge loss, risks comparison, and the quadratic hinge loss.

Covers faster gradient descent methods and projected gradient descent for constrained optimization in machine learning.