School of Engineering and Technology
B.Tech (Artificial Intelligence and Machine Learning)
An AI-first programme where the entire curriculum is structured around Artificial Intelligence and Machine Learning from Year I – producing specialists ready for AI engineering, research, and leadership roles.
About the Programme
Unlike B. Tech CSE where AI/ML is a specialisation programme. The mathematical and computing foundations are specifically chosen for AI/ML workloads – linear algebra with ML applications, probability for Bayesian inference, Python from Semester I. By II Year students work on real AI projects with industry partners. The dedicated GPU Lab is equipped with NVIDIA A100/RTX 4090 workstations and cloud compute credits. Graduates enter industry as AI Engineers, MLOps Specialists, NLP Researchers, and Computer Vision Engineers.
Programme Details
Course at a Glance
180
8 Semesters
AI & ML
60
Engineering and Technology
Programme Structure
YEAR I - AI FOUNDATIONS
Mathematics and Python for AI
Linear Algebra for ML · Probability and Statistics · Python for AI · AI Literacy · Digital Logic Design · Neural Networks Introduction · Data Structures · Human Values
YEAR II - CORE AI ENGINEERING
Building the AI toolkit
Machine Learning Fundamentals · Deep Learning · Computer Vision · Natural Language Processing · Database Systems for AI · Cloud Computing · Software Engineering · Statistics and Inference
YEAR III - ADVANCED AI
Specialisation and applications
Generative AI and LLMs · Reinforcement Learning · MLOps and Deployment · Computer Vision Advanced · NLP Advanced · AI Ethics and Governance · 2 Electives · Internship (mandatory)
YEAR IV - RESEARCH AND INDUSTRY
Capstone and professional launch
B. Tech Project in AI (two semesters) · Advanced Electives · Industry Research Seminar · Responsible AI Lab · AI Startup Studio
Themes
GENERATIVE AI AND LLMS
GPT fine-tuning · RAG Systems · Multi-modal AI · AI Agents · Prompt Engineering
COMPUTER VISION
Object Detection · Image Segmentation · 3D Vision · Medical Imaging · Edge AI
NATURAL LANGUAGE PROCESSING
Transformers · Sentiment Analysis · Machine Translation · Information Extraction
MLOps AND AI ENGINEERING
Model Deployment · Monitoring · Drift Detection · Feature Stores · Kubeflow
AI FOR HEALTHCARE AND FINANCE
Clinical NLP · Drug Discovery · Algorithmic Trading · Credit Scoring Models
RESPONSIBLE AI
Fairness · Explainability · Privacy-Preserving ML · AI Governance Frameworks
Curriculum Overview
AI-first curriculum from Day 1
Mathematics, computing, and labs all have explicit AI/ML contexts from Semester I.
GPU lab and cloud compute access
Dedicated NVIDIA GPU lab with A100/RTX 4090. Every student receives cloud compute credits (AWS/GCP) in Year II. MLOps pipeline practice on real infrastructure.
Industry research collaboration
Centre of Excellence in AI in partnership with NVIDIA. Students work on real AI problems. Research publications and patent support.
Responsible AI embedded
AI Ethics, Bias, Privacy, and Governance are core subjects – not electives. Graduates understand not just how to build AI but why it must be built responsibly.
Eligibility Criteria
| Criterion | Requirement |
|---|---|
Qualifying exam | 10+2 (Class XII) from any recognised board |
Mandatory subjects | Physics, Chemistry, Mathematics (PCM) |
Minimum marks | 60% aggregate in PCM (55% for SC / ST / PwD) |
Entrance exam | Valid JEE Main score OR Metro University MET |
Preferred | Students with strong Mathematics background, computing interest, or Olympiad exposure |
Selection | JEE Main / MET → Counselling → Seat allotment |
Frequently Asked Questions
How is this different from B. Tech CSE?
This programme is AI-driven from Day 1 with dedicated GPU labs and an AI-specific curriculum throughout all four years.
Do I need prior AI/ML knowledge?
No prior AI knowledge is not required. The programme builds from mathematics and Python in Year I. Strong Class XII Mathematics is the best preparation.
What companies hire AI/ML graduates?
Amazon, Google, Microsoft, NVIDIA, Flipkart, Paytm, and startups in HealthTech, FinTech, EdTech. Average package: Rs 8-12 LPA. Highest offer: Rs 24 LPA.
Is research possible in this programme?
Yes, students can join faculty research groups. Year IV project can be co-supervised by an industry researcher. Conference publications and patent support are actively provided.
grammes at IITs and NITs.