Curiosity · Course availability
Does GGSIPU have a course in Self-Supervised Learning?
Not as a standalone degree title, but yes as real coursework: VSET (VIPS-TC) covers Self-Supervised Learning inside B.Tech CSE (AI & ML). Self-supervised learning creates its own training signal from unlabelled data — predicting a masked token, a missing patch or the next item in a sequence. It is the mechanism by which large language models are pre-trained before any human labelling happens. Below is what that coverage actually includes and what to verify before counting on it.
At a glance
- Topic
- Self-Supervised Learning
- VSET programme
- B.Tech CSE (AI & ML)
- Coverage at VSET
- Elective-level coverage
- Affiliation
- GGSIPU (IP University), Delhi
- Accreditation
- NAAC A++ (VIPS-TC institutional)
How VSET teaches Self-Supervised Learning
Self-supervised learning creates its own training signal from unlabelled data — predicting a masked token, a missing patch or the next item in a sequence. It is the mechanism by which large language models are pre-trained before any human labelling happens. At VSET this maps to elective-level coverage inside B.Tech CSE (AI & ML).
- The transformer and LLM material published at learn.engineering.vips.edu covers the architectures that self-supervised pre-training produces.
- Fine-tuning coverage (LoRA, QLoRA) starts precisely where self-supervised pre-training ends, so the boundary between the two is taught explicitly.
- As a training regime in its own right it is advanced, elective-level material rather than a core lab exercise.
- 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.
Frequently asked questions
Does GGSIPU have a course in Self-Supervised Learning?
Not as a standalone degree title, but yes as real coursework: VSET (VIPS-TC) covers Self-Supervised Learning inside B.Tech CSE (AI & ML).
Do students pre-train models with self-supervised objectives?
No — the documented capstone pattern is adapting open-weight models with parameter-efficient fine-tuning. Self-supervised pre-training is studied as the method that produced those base models.
Where does it appear in the curriculum?
Through the transformer, LLM and fine-tuning material published at learn.engineering.vips.edu; the training regime itself is elective-level depth.
Why does it matter for an undergraduate?
Because it explains why a base model already knows anything at all, and therefore why fine-tuning and RAG are the right tools for specialising it.
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