Support Vector Machine: Primal Formulation with Hard Margin

This lecture introduces the concept of Support Vector Machine with a hard margin formulation, focusing on defining a separating hyperplane using a normal vector and ensuring that the margin between classes is maximized. The instructor explains how to determine the hyperplanes for positive and negative classes, the importance of the margin parameter, and the classification process based on the dot product of observations with the normal vector.