M.Tech in Artificial Intelligence & Data Science
A two-year postgraduate programme focused on AI and data science, enabling graduates to design intelligent systems and extract insight from complex, large-scale data.
M.Tech in artificial intelligence & data science overview
The programme develops strong foundations in artificial intelligence, data science and high-performance computing, progressing towards advanced techniques used in real-world AI systems. Students experience:
Strong AI and data foundations
Advanced learning in databases, machine learning, mathematics for AI and core artificial intelligence concepts.
Hands-on work with real data
Practical coursework in big data analytics, computer vision, natural language processing and deep learning.
Domain-focused specialisation
Electives that connect AI with application areas such as bioinformatics, communications and smart industry.
Research and industry exposure
An industry internship, project-based learning and a two-stage dissertation on real-world AI problems.
Programme details
Objective
The M.Tech in Data Science and Artificial Intelligence programme is designed to train graduates from computer science and engineering, electronics and communication engineering, electrical and electronics engineering, or Artificial Intelligence backgrounds to develop advanced expertise in artificial intelligence and data science.
The programme combines theoretical foundations with practical applications to help students build strong capabilities in emerging technologies and analytical methods.
During the first two semesters, students study core subjects such as databases, big data analytics and high-performance computing (HPC). The curriculum also introduces key areas including natural language processing (NLP) and digital image processing, followed by advanced topics such as computational sequence modelling and deep learning. These areas form the foundation for developing professional competence in artificial intelligence.
Students are also introduced to reinforcement learning, with applications in autonomous systems, as well as financial risk management, where data analytics plays an increasingly significant role.
The programme offers the option to specialise in one of the following streams:
- Bioinformatics
- Communications
- Smart industry
Each stream includes a foundational course followed by an advanced course.
Graduates of the programme are expected to develop both the motivation and expertise to pursue careers in the rapidly evolving fields of artificial intelligence and data science, or continue into doctoral research programmes in related areas.
- Total credits: 63
- Prior degree required: B.Tech in CSE, AI, ECE or EEE
Note: The extension of the work visa period for students pursuing a master’s degree in France has been increased from 2 years to 5 years.
Academic structure
Our academic structure is designed to establish robust foundations, followed by increasing specialization in later years.
- Credit structure: Total of 62 credits
- Duration: 2 years / 4 semesters
- Coursework is covered in Semesters 1 and 2, with project, seminars, internship and thesis across Semesters 3 and 4.
| Course | L-T-P | Credits |
|---|---|---|
| Advanced Engineering Mathematics | 3-1-0 | 4 |
| Foundations of Artificial Intelligence | 3-0-2 | 4 |
| Probability & Random Processes | 3-1-0 | 4 |
| Advanced Data Structures & Algorithms | 3-0-2 | 4 |
| AI & Data Science Lab | 0-0-4 | 2 |
| Research Methodology | 2-0-0 | 2 |
| Course | L-T-P | Credits |
|---|---|---|
| Machine Learning | 3-1-0 | 4 |
| Deep Learning | 3-0-2 | 4 |
| Big Data Analytics | 3-0-2 | 4 |
| Statistical Learning & Data Mining | 3-0-0 | 3 |
| Advanced AI Lab | 0-0-4 | 2 |
| Program Elective I | 3-0-0 | 3 |
| Course | L-T-P | Credits |
|---|---|---|
| Program Elective II | 3-0-0 | 3 |
| Program Elective III | 3-0-0 | 3 |
| Seminar | 0-0-2 | 1 |
| Dissertation – Phase I | 0-0-12 | 6 |
| Course | L-T-P | Credits |
|---|---|---|
| Dissertation – Phase II | 0-0-24 | 12 |
FAQs
This programme focuses specifically on AI and Data Science, with all core courses aligned to machine learning, data systems and intelligent applications.
Through applied coursework, modern tools, an industry internship and a final-year dissertation.
Yes. The curriculum begins with foundational courses before progressing to advanced topics and specialisations.
Yes. Advanced electives and a substantial dissertation prepare students for doctoral study and research roles.
Graduates pursue roles such as data scientist, machine learning engineer, AI researcher and related positions across technology and research organisations.
