Curiosity · After 12th
How to learn Semi-Supervised Learning after 12th in Delhi
Semi-supervised learning trains on a small labelled set plus a much larger unlabelled one, using techniques such as pseudo-labelling and consistency regularisation. It is the practical middle ground when annotation budgets are tight. 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 Semi-Supervised Learning coverage is genuine rather than a brochure keyword.
At a glance
- Topic
- Semi-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 Semi-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 Semi-Supervised Learning
Semi-supervised learning trains on a small labelled set plus a much larger unlabelled one, using techniques such as pseudo-labelling and consistency regularisation. It is the practical middle ground when annotation budgets are tight. At VSET this maps to elective-level coverage inside B.Tech CSE (AI & ML).
- Semi-supervised methods extend the supervised and unsupervised machine learning material published at learn.engineering.vips.edu.
- The curriculum's coverage of both paradigms provides the base a semi-supervised approach combines; the technique itself is advanced, elective-level work.
- It typically appears in project settings where labelled data for a student's own dataset is scarce.
- Delivered inside the B.Tech CSE (AI & ML) track, one of VSET's seven GGSIPU-affiliated B.Tech programmes.
Where Semi-Supervised Learning skills lead
Graduates applying Semi-Supervised Learning skills typically target roles such as Machine Learning Engineer, Data Scientist, Applied ML Researcher, AI Engineer, Research Associate. 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 Semi-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 Semi-Supervised Learning-specific work begins. What matters at entry is 10+2 PCM and a JEE Main score.
Is semi-supervised learning a named topic in the syllabus?
It is best described as elective-level: VSET's published curriculum covers the supervised and unsupervised foundations it builds on, and the technique itself is usually taken up in project work.
When would a student actually use it?
When a capstone dataset has to be labelled by hand — a small labelled core plus a large unlabelled remainder is the normal situation in student projects.
What compute is needed?
The AICTE IDEA Lab's GPU workstations handle the repeated training rounds these methods require.
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