Support Vector Machines: Dual Formulation for Hard Margin

This lecture covers the dual formulation of Support Vector Machines (SVM) for hard margin classification. It explains the optimization problem to find the hyperplane parameters, the decision function, and the concept of support vectors. The instructor discusses the transition from the primal to the dual formulation, the role of Lagrange multipliers, and the conditions for optimal solutions. By exploring the relationship between the dual and primal solutions, the lecture highlights the significance of support vectors in SVM. It also addresses the choice between primal and dual formulations based on data characteristics, emphasizing the interpretation of alpha values and their impact on classification boundaries.