Linear Algebra for AI in a B.Tech — syllabus & what you learn

How VSET teaches Linear Algebra for AI

Linear algebra covers vectors, matrices, linear transformations, eigenvalues and decompositions, which is the language neural networks, embeddings and dimensionality reduction are written in. It is core engineering mathematics coursework at VSET and deepens in the Applied Mathematics specialisation. At VSET this maps to documented coursework depth inside B.Tech CSE (Applied Mathematics).

• Linear algebra is part of the core engineering mathematics coursework across VSET's B.Tech programmes.
• VSET offers B.Tech CSE with an Applied Mathematics specialisation, which extends the mathematical treatment.
• The CSE AI and ML specialisation applies this material directly to model training and representation.
• Matrix computation here is the basis for the machine learning coursework students meet later.

Labs and infrastructure

• IDEA Lab GPU workstations let students run the matrix-heavy computations that machine learning coursework involves.
• Campus computing labs support numerical practicals alongside the mathematics coursework.

What students actually build

• Machine learning capstone projects apply matrix methods for representation and dimensionality reduction.
• Image and signal projects on IDEA Lab hardware use the same linear algebra foundation.