Support Vector Machines Part 1 - Main Ideas

Overview

This course aims to explain the main ideas behind Support Vector Machines in Machine Learning. By the end of the course, learners will understand concepts such as Maximal Margin Classifiers, Soft Margins, Support Vector Classifiers, the polynomial kernel function, the radial basis function (RBF) kernel, and the kernel trick. The teaching method includes a mix of explanation, intuition building, and real-world examples. This course is intended for individuals familiar with the bias/variance tradeoff, cross-validation, and have a basic understanding of Machine Learning concepts.

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