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📚About

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).

⏲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

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