Supervised Learning - Support Vector Machines

Understand the role of Lagrange Multipliers and Karush-Kuhn-Tucker in optimising SVMs.

Grasp the concept of Maximum Margin Classification and its significance in SVMs.

Describe nonlinear SVM classification using inner product kernels and its applications. Explore the use of kernel functions such as polynomial and radial basis function (RBF) to transform data into higher dimensions, enabling the separation of non-linearly separable data points.