Support Vector Machines Part 1 - Main Ideas

This course explains the main ideas behind support vector machines, covering maximal-margin and soft-margin classifiers, support vector classifiers, polynomial and radial basis function kernels, and the kernel trick. It assumes prior familiarity with the bias-variance tradeoff and cross-validation.

Syllabus

Awesome song and introduction
Basic concepts and Maximal Margin Classifiers
Soft Margins allowing misclassifications
Soft Margin and Support Vector Classifiers
Intuition behind Support Vector Machines
The polynomial kernel function
The radial basis function RBF kernel
The kernel trick
Summary of concepts