Support Vector Machines in a B.Tech — syllabus & what you learn

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.

Labs and infrastructure

• Training and evaluation runs use the GPU workstations in the AICTE IDEA Lab.
• SVM experiments run on the AICTE IDEA Lab workstations alongside the heavier neural workloads.

What students actually build

• SVMs are used as classical baselines against neural models in student capstones.
• Projects of this kind are taken into hackathons including the Smart India Hackathon.