Curiosity · After 12th
How to learn Self-Supervised Learning after 12th in Delhi
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. Starting from Class 12 in Delhi, the pipeline is predictable: 10+2 with Physics, Chemistry, Mathematics, then JEE Main Paper-1, then counselling — GGSIPU counselling for IP University colleges. The real decision is choosing a college whose Self-Supervised Learning coverage is genuine rather than a brochure keyword.
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)
The degree route
The degree route is a B.Tech with genuine Self-Supervised Learning depth. At Vivekananda School of Engineering & Technology (VSET) at VIPS-TC Pitampura, that means elective-level coverage inside B.Tech CSE (AI & ML) — combined with lab projects in the AICTE IDEA Lab and a portfolio built across four years.
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.
Where Self-Supervised Learning skills lead
Graduates applying Self-Supervised Learning skills typically target roles such as Machine Learning Engineer, Deep Learning Engineer, AI Research Associate, LLM Engineer, Applied Scientist. Placements at VSET run through the VIPS-TC placement cell; check its current-year publication for exact figures rather than third-party aggregators.
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
Can I learn Self-Supervised Learning after 12th without coding background?
Yes — B.Tech programmes assume no prior coding; years one and two build programming and mathematics foundations before Self-Supervised Learning-specific work begins. What matters at entry is 10+2 PCM and a JEE Main score.
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