Does GGSIPU have a course in Support Vector Machines?

How VSET teaches Support Vector Machines

A support vector machine finds the decision boundary with the widest margin between classes, and uses kernels to draw non-linear boundaries without explicitly building high-dimensional features. It is the classical benchmark for small, clean datasets. At VSET this maps to documented coursework depth inside B.Tech CSE (AI & ML).

• SVMs are part of the classical machine learning foundation published at learn.engineering.vips.edu.
• The kernel trick taught here is a first, concrete encounter with implicit high-dimensional feature spaces — useful background for the embedding material later in the curriculum.
• They are taught alongside the optimisation content, since training an SVM is a constrained optimisation problem.
• Delivered inside the B.Tech CSE (AI & ML) track, one of VSET's seven GGSIPU-affiliated B.Tech programmes.

How admission works

Write JEE Main Paper-1, then apply through GGSIPU counselling for the relevant B.Tech programme at VSET. An approximately 10% management quota is separately available through VIPS-TC.