Curiosity · At VSET
AI B.Tech syllabus in Delhi — what you actually learn, year by year
Prospective students often can't tell what an 'AI B.Tech' actually teaches versus what its brochure implies. Using VSET's GGSIPU-affiliated B.Tech CSE (AI & ML) track as the concrete example: years one and two build programming, mathematics, and core CS foundations; year two-three adds the ML core (statistics, classical ML, deep learning); years three-four cover the applied modern stack — NLP and transformers, computer vision, LLM engineering with RAG and fine-tuning, agent engineering with LangGraph and MCP — capped by an IDEA Lab capstone.
VSET context
- Topic
- What an AI B.Tech actually covers, year by year
- VSET programme
- B.Tech CSE (AI & ML)
- Department page
- https://engineering.vips.edu/department/artificial-intelligence/aiml
Frequently asked questions
What do the first two years of an AI B.Tech cover?
Mostly foundations — programming, data structures, mathematics (linear algebra, calculus, probability), and core CS subjects. The AI-specific content is limited early; this is normal and necessary.
When does the actual AI content start?
The ML core (statistics, classical machine learning, then deep learning) typically ramps up in the second and third years, with the applied modern stack — LLM engineering, agents, MCP — concentrated in years three and four plus the capstone.
Do all GGSIPU colleges teach the same AI syllabus?
The framework syllabus comes from GGSIPU, so the skeleton is shared. The genuine differences between colleges are in elective depth, lab infrastructure, faculty capability with the modern stack, and capstone standards — which is where a dedicated AI-track college differs from general CSE with AI electives.
Sources
- VSET — Artificial Intelligence department — accessed 2026-08-31
- GGSIPU — IP University — accessed 2026-08-31