Programme Overview
Launched in 2021 alongside our Centre of Excellence, this is MIT's newest and most selective branch — 60 seats against roughly 900 applications last year. It is a full engineering degree, not a bolt-on specialisation: students take the same mathematics, programming and systems core as Computer Science, then spend years three and four almost entirely on learning systems.
The distinguishing resource is the GPU laboratory. Students train real models on real hardware rather than reading about them, and the department deliberately keeps a bias toward deployment — a model that never leaves a notebook is treated as an unfinished project here.
Mathematics is not optional in this branch. Linear algebra, probability and optimisation are taught as first-class subjects, because everything downstream depends on them.
What You Will Learn
- Linear algebra, probability and optimisation
- Python, NumPy, PyTorch and TensorFlow
- Classical machine learning and statistics
- Deep learning and neural architectures
- Computer vision and image processing
- Natural language processing and LLMs
- Reinforcement learning fundamentals
- MLOps — serving, monitoring, retraining
- Data engineering for model pipelines
- AI ethics, bias and model governance
Curriculum — Year by Year
Common engineering first year with an additional mathematics tutorial specific to this branch, plus the compulsory three-week bridge course.
- Engineering Mathematics I & II
- Engineering Physics & Chemistry
- Programming for Problem Solving (C)
- Basic Electrical Engineering
- Introduction to AI (department seminar)
- Professional Communication
- Environmental Studies
- Programming & Python Labs
The mathematical spine of the degree. Students who struggle here are supported with a weekly problem-solving clinic run by the department.
- Linear Algebra for Machine Learning
- Probability & Statistical Inference
- Data Structures & Algorithms
- Database Management Systems
- Introduction to Machine Learning
- Operating Systems
- Data Visualisation & Exploration
- Mini-project I + summer internship
Everything moves onto the GPU cluster. Students choose a specialisation track and take their first department electives.
- Deep Learning & Neural Networks
- Computer Vision
- Natural Language Processing
- Optimisation Techniques
- Big Data & Distributed Computing
- Reinforcement Learning
- Department Elective I & II
- Mini-project II + second internship
The year models go to production. Semester eight is a six-month industry internship or an approved MIT-Ignite research project.
- MLOps & Model Deployment
- Generative AI & Large Language Models
- AI Ethics, Bias & Governance
- Department Elective III & IV
- Capstone Project (two semesters)
- Six-month industry internship
- Research paper writing seminar
- Open Elective (any department)
Specialisation Tracks
Computer Vision
Detection, segmentation and video analytics, with an ongoing project on crop-disease identification for local farms.
Language & Generative AI
Transformers, retrieval-augmented generation, fine-tuning and evaluation of large language models.
MLOps & Production ML
Feature stores, model serving, drift monitoring and retraining pipelines on containerised infrastructure.
Robotics & Autonomous Systems
Sensor fusion, path planning and control, run jointly with the Mechanical department's robotics lab.
Laboratories You Will Use
- Centre of Excellence AI lab (GPU workstations)
- Data engineering & analytics lab
- Computer vision imaging rig
- Cloud & DevOps sandbox cluster
- Robotics lab (shared with Mechanical)
- Project studio open until 10 PM
Careers & Placement
The first full batch graduated in 2025 — a small cohort of 54, of whom 52 were placed and 9 went on to postgraduate study. The median package of ₹7.1 LPA is the highest of any MIT branch, though the sample is still young and we publish it with that caveat.
- Machine Learning Engineer
- Data Scientist
- Computer Vision Engineer
- MLOps / Platform Engineer
- Data Analyst / BI Engineer
- AI Research Assistant
- Higher study — M.Tech, MS, PhD
- Founder via the MIT-Ignite incubator
Recruiters for this branch: Amazon, Infosys, TCS Research, Fractal Analytics, Mu Sigma, Zoho, Paytm, Josh Technology and a growing set of Noida and Bengaluru startups.
Programme Coordinator
Dr. Imran Qureshi
Ph.D. in Machine Learning from IIIT Allahabad with eleven years of teaching and applied research experience. He runs the Centre of Excellence GPU lab and supervises the crop-disease vision project.