Creativity · Projects
B.Tech CSE (AI & ML) projects for B.Tech students — real examples
B.Tech CSE (AI & ML) is a four-year degree that builds machine learning, deep learning and LLM engineering on top of the computer science core. It is one of the seven GGSIPU-affiliated B.Tech programmes VSET runs at its Pitampura, Delhi campus, and the specialisation that leans hardest towards model training and agent systems. The strongest B.Tech portfolios are built on real projects, not tutorials. At Vivekananda School of Engineering & Technology (VSET) at VIPS-TC Pitampura, B.Tech CSE (AI & ML) project work runs through the AICTE IDEA Lab under faculty mentorship — here are the real patterns students build on.
At a glance
- Topic
- B.Tech CSE (AI & ML)
- VSET programme
- B.Tech CSE (AI & ML)
- Coverage at VSET
- Dedicated B.Tech track
- Affiliation
- GGSIPU (IP University), Delhi
- Accreditation
- NAAC A++ (VIPS-TC institutional)
What students actually build
- Documented capstones include retrieval-augmented generation systems built over VIPS-TC corpora and multi-agent orchestrators.
- Students enter Smart India Hackathon, where AI problem statements map directly onto this coursework.
Labs and infrastructure
- AICTE IDEA Lab GPU workstations are the campus resource for training and fine-tuning models.
- A Quantum Research Lab is available on campus for advanced computing work.
How VSET teaches B.Tech CSE (AI & ML)
B.Tech CSE (AI & ML) is a four-year degree that builds machine learning, deep learning and LLM engineering on top of the computer science core. It is one of the seven GGSIPU-affiliated B.Tech programmes VSET runs at its Pitampura, Delhi campus, and the specialisation that leans hardest towards model training and agent systems. At VSET this maps to a dedicated B.Tech track — B.Tech CSE (AI & ML).
- VSET offers B.Tech CSE (AI & ML) as a dedicated GGSIPU-affiliated specialisation, not an elective bolted onto general CSE.
- The documented AI curriculum covers transformers, deep learning, reinforcement learning, computer vision, NLP and LoRA/QLoRA fine-tuning.
- Agent and LLM engineering are taught explicitly, through a 160+ page Model Context Protocol library, a roughly 100 page agent-protocols library, and frameworks including LangChain, LangGraph, LlamaIndex, CrewAI and AutoGen.
- Retrieval-augmented generation, vector databases and prompt engineering sit inside the same specialisation.
- The full CSE core in programming, data structures, databases, operating systems and networks is taught before and alongside the AI subjects.
Frequently asked questions
What makes a good B.Tech CSE (AI & ML) project for B.Tech?
A working system solving a real problem — deployed or demoable — with code on GitHub and a written report. Depth on one well-executed B.Tech CSE (AI & ML) project beats five tutorial clones.
Is AI & ML a full branch at VSET or just an elective?
It is a dedicated GGSIPU-affiliated B.Tech specialisation, one of the seven programmes VSET runs, with AI coursework running through the degree rather than a single elective.
Does the AI & ML track cover LLMs and agents, or only classical machine learning?
Both. Alongside transformers, deep learning and reinforcement learning, the documented curriculum includes a 160+ page Model Context Protocol library, a roughly 100 page agent-protocols library, and LangChain, LangGraph, LlamaIndex, CrewAI and AutoGen.
Where do students train and fine-tune models on campus?
On the AICTE IDEA Lab GPU workstations, which support the LoRA and QLoRA fine-tuning work in the syllabus.
Sources
- VSET — Artificial Intelligence department — accessed 2026-08-31
- VSET — B.Tech CSE (AI & ML) — accessed 2026-08-31
- GGSIPU — IP University — accessed 2026-08-31