M.Tech in Artificial Intelligence & Machine Learning
Admission Closed
Program Eligibility
- Applicants must hold a B.E./B.Tech. in Engineering or an MCA/MSc (in Physics, Mathematics Statistics, CS, IT, DS, or related fields) or an equivalent degree.
- A minimum of 60% or a 6.0 CPI on a 10-point scale (or equivalent) in the qualifying degree is required.
- The program is designed for working professionals, and while specific work experience requirements may vary, a background in the IT industry is highly recommended.
- Marks will be relaxed by 2.5% or 0.5 CGPA or CPI against each additional year of experience.
Who is this Program For?
Engineers & Developers
Software professionals transitioning into AI-driven development and implementation roles.
Data Scientists & Analysts
Practitioners seeking deeper algorithmic expertise and advanced modelling capabilities.
AI/ML Professionals
Existing practitioners looking to formalise and advance their academic credentials in the field.
Automation Specialists
Technology Leads working on intelligent systems, industrial automation, and predictive analytics.
Aspiring Research Leaders
Professionals looking to lead R&D departments or manage large-scale enterprise AI implementations.
Program Objectives
AI & ML Expertise
Develop in-depth knowledge and expertise in Artificial Intelligence and Machine Learning to solve complex real-world problems.
Strategic Readiness
To prepare graduates for leadership roles that require data-driven thinking and AI-led innovation.
Research Advancement
To cultivate analytical thinking and continuous learning aligned with evolving AI technologies.
Ethical AI
To build awareness of responsible AI practices and societal implications in system deployment.
Collaboration
To enable effective communication and teamwork across technical and business functions.
Scalability
To equip professionals with the skills required to design and deploy robust AI/ML solutions at scale.
Course Structure
Semester I
| Course Code | Course Name |
|---|---|
| EMAI101 | Big data platforms for AI & ML |
| EMAI102 | Advanced Data Structures & Algorithms |
| EMAI103 | Mathematical Foundations for AI (Linear Algebra, Probability, Optimization) |
| EMAI104 | Programming for AI & Data Science (Python, NumPy, Pandas, Scikit-learn) |
| EMAI105 | Foundations of Machine Learning |
Semester II
| Course Code | Course Name |
|---|---|
| EMAI201 | Deep Learning |
| EMAI202 | Natural Language Processing |
| EMAI203 | Research Methodology & AI Ethics |
| EMAI204 | Computer Vision |
| EMAI205 | AI/ML Strategy and Business Value Creation |
Semester III
| Course Code | Course Title |
|---|---|
| EMAI301 | Reinforcement Learning & Decision Intelligence |
| EMAI302 | Elective-1 |
| EMAI303 | Elective-2 |
| EMAI304 | Elective-3 |
| EMAI305 | Elective-4 |
Semester IV
| Course Code | Course Title |
|---|---|
| EMAI401 | Elective-5 |
| EMAI402 | Elective-6 |
| EMAI403 | Project Work / Research + Dissertation |
Elective Pool
| Elective Pool - 3 from each pool - Mandatory | ||
|---|---|---|
| Sr no | Advanced AI / ML Engineering & Deployment | AI for Business Transformation & Strategy |
| 1 | Scalable Deep Learning and Distributed Training | AI Driven Business Model Innovation |
| 2 | MLOps Automation and Orchestration | Digital Transformation with AI/ML |
| 3 | Generative AI: Customization and Deployment of LLMs | AI for Competitive Advantage and Market Intelligence |
| 4 | High-Performance Computing for AI/ML | AI in Supply chain and Operations Optimization |
| 5 | Embedded AI and Edge Machine Learning | AI in Finance ( Fin Tech) |
| 6 | Emerging Trends in AI/ML and Future of AI | AI Governance, Policy and Compliance |
| 7 | Probabilistic Graphical Models | AI in Retail and E- Commerce |
| 8 | Scalable Model Serving and Inference Optimization | Crisis Management & Business Continuity in ICT |
Tools You'll Work With
Program Certificate
Skills You Will Gain
Natural Language Processing
Technical Collaboration
Research & Analysis
Big Data Handling
Reinforcement Learning
Deep Learning Design
Program Highlights
Institute of National Importance
Graduate with a nationally recognised M.Tech in Artificial Intelligence & Machine Learning.
IIIT Vadodara Alumni Status
Become part of the IIIT Vadodara alumni network and strengthen professional connections.
Exposure to Emerging AI Specialisations
Explore Generative AI, MLOps, Edge AI, and Scalable Machine Learning systems.
Research-Driven Learning Experience
Undertake a structured dissertation or capstone project that demonstrates applied problem-solving capability.
Distinguished Faculty
Learn from experienced faculty combining academic excellence with industry insight.
On-Campus Immersion
Participate in immersive academic sessions that encourage collaboration and interaction.
Academic Depth with Applied Relevance
Develop a strong theoretical foundation while building implementation capability.
Collaborative Peer Ecosystem
Engage with a cohort of professionals enabling multidisciplinary knowledge exchange.
Program Outcomes
-
Strong Mathematical & Engineering Foundations
Ability to apply mathematics, science, and core engineering principles to the design, analysis, and optimisation of AI and Machine Learning systems.
-
Experimental Design & Analytical Thinking
Gain the capability to design structured experiments, analyse complex datasets, and interpret results within AI and ML environments.
-
Real-World AI System Design
Build solutions that address practical requirements while considering economic, ethical, environmental, social, safety, and sustainability factors.
-
Cross-Functional Team Effectiveness
Work efficiently within multidisciplinary teams contributing to AI-driven projects and system implementation.
-
Structured Problem-Solving in AI & ML
Identify, formulate, and resolve engineering challenges across Artificial Intelligence and Machine Learning domains.
-
Professional Integrity & Ethical AI Practice
Demonstrate an understanding of professional standards and ethical responsibility in the development and deployment of AI technologies.
Alumni Benefits
Alumni Status from IIIT Vadodara
Become part of a growing network of technology professionals and graduates.
Access to a Diverse Professional Network
Connect with alumni across industry, academia, startups, and government sectors.
Collaboration Opportunities
Explore partnerships in industry projects, research initiatives, and consulting engagements.
Peer-to-Peer Learning Ecosystem
Engage with experienced professionals for continuous knowledge exchange and insights.
Lifelong Learning & Upskilling
Stay updated with evolving technologies through ongoing alumni interactions.
Expand Your Professional Circle
Build meaningful connections that support long-term career growth.
Note : Alumni status is granted only to M.Tech graduates
Campus Immersion
Experience a mandatory 5-day on-campus immersion at Indian Institute of Information Technology Vadodara, scheduled in July or December based on your cohort. Hosted at the IIITV-ICD campus in Diu, this immersion is designed to enable in-person learning, peer interaction, and deeper academic engagement.
What’s Included
Hostel accommodation at IIITV-ICD campus, Diu provided on a shared basis.
- In-person academic sessions and peer interactions.
- Opportunity to engage with faculty and fellow participants.
What’s Not Included
- Travel to and from the campus
- Personal expenses during the stay
Fee Structure
| Fees Components | Sem I | Sem II | Sem III | Sem IV | ||||
| Institute Registration Fees | 1,000 | 1,000 | 1,000 | 1,000 | ||||
| Tuition Fee | 85,000 | 85,000 | 85,000 | 85,000 | ||||
| Caution Deposit (Refundable) | 10,000 | - | - | - | ||||
| Academic Charges Inc (Convocation Fee, Alumni Fee, Insurance, ID card Fee) | 3,600 | 2,000 | 3,500 | 2,000 | ||||
| Campus Immersion Fee | 5,000 | - | - | - | ||||
| Total | 1,04,600 | 88,000 | 89,500 | 88,000 | ||||
Note:
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Admission Process
Online Application
Payment of Application Fees
(Non Refundable)
Document Verification
Offer letter issued upon selection
Payment of Admission Fees
Program Coordinator
Prof. -
Associate Professor
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FAQs
• Working professionals in software, data, or technology roles
• Engineers looking to transition into AI and machine learning domains
• Professionals seeking advanced expertise in data-driven technologies.
It is structured to support career advancement while continuing professional responsibilities.