đź“šAbout

This lecture focuses on the theoretical as well as practical aspects of the Support Vector Machines. It is a supervised learning model associated with learning algorithms that analyze data used for classification and regression analysis. Developed at AT&T Bell Laboratories by Vapnik with colleagues (Boser et al., 1992, Guyon et al., 1993, Vapnik et al., 1997), it presents one of the most robust prediction methods, based on the statistical learning framework or VC theory proposed by Vapnik and Chervonenkis (1974) and Vapnik (1982, 1995).

âŹ˛Outline

00:00:00 Introduction

00:01:11 Support Vector Machines

00:03:55 Supporting Vectors and Hyperplanes

00:07:05 SVM Mathematical Modelling

00:08:58 Hard Margin SVM

00:47:21 Outlier Sensitivity & Linear Separability

00:49:11 Hard Margin SVM on Python

01:13:15 Soft Margin SVM

01:27:09 Soft Margin SVM on Python

01:31:47 Outro