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The Master of Technology in Artificial Intelligence (M.Tech CSE - AI) program is an advanced 2 year postgraduate journey designed to create high impact AI specialists, researchers, and technology leaders. The program focuses on developing deep expertise in intelligent systems that learn, reason, and adapt, enabling graduates to drive innovation across industries and research domains.
Candidates must have successfully completed a Bachelor degree in Computer Science and Engineering, Artificial Intelligence, Information Technology, Data Science, or a related discipline from a recognized university, securing a minimum of fifty percent marks. Relaxation is provided for reserved categories as per applicable norms.
Admission is subject to university norms and regulatory guidelines as applicable.
| Fees Structure | Amount |
|---|---|
| 1st Year Annual | 95000/- INR |
| 2nd Year Annual | 95000/- INR |
M.Tech CSE - AI Admission Procedure
At Future University, admission to the Master of Technology (M.Tech) in AI programme is conducted through a transparent, merit-based, and research-oriented selection process designed to identify technically strong, analytically sharp, and innovation-driven engineers.
This programme is meant for those who want to go deeper - into design, optimization, systems thinking, advanced analytics, and applied research.
Admission Pathways for M.Tech CSE - AI
Admissions are offered through one or more of the following routes:
1. Qualifying Examination Merit
• Admission based on marks obtained in the B.E. / B.Tech. degree
• Merit list prepared according to university norms and specialization eligibility
2. National & State Level Entrance Examinations
Candidates may secure admission through recognized entrance examinations such as:
• Graduate Aptitude Test in Engineering (GATE)
• State-level postgraduate engineering entrance examinations (as notified by respective authorities)
Valid scores and ranks are considered as per university guidelines.
3. FUCET – Future University Common Entrance Test
Future University also conducts its own entrance examination:
• FUCET (Future University Common Entrance Test)
• Designed to assess technical knowledge in the relevant discipline, analytical ability, and research aptitude
Future University stands apart through its research driven AI curriculum, strong focus on innovation, and commitment to responsible artificial intelligence development. With modern infrastructure, interdisciplinary research opportunities, and structured placement support, the university prepares students to become future ready AI experts and thought leaders.
• Industry aligned artificial intelligence curriculum
• Strong emphasis on research and innovation
• Advanced AI labs and high performance computing facilities
• Startup and incubation support
To nurture highly skilled artificial intelligence professionals who are innovative, ethically responsible, and capable of advancing intelligent technologies for societal and industrial transformation. The department strives to foster scientific inquiry, research excellence, and lifelong learning, empowering students to contribute meaningfully to the evolution of artificial intelligence at a global level.
• Build advanced expertise in artificial intelligence theories and applications
• Prepare students for leadership roles in AI development, research, and innovation
• Deliver an industry aligned curriculum integrating theory with research driven practical application
• Develop analytical thinking, problem solving ability, and research competence
• Provide hands on exposure to advanced AI tools, platforms, and real world use cases
• Industry aligned AI focused postgraduate curriculum
• World class AI research and computing infrastructure
• Research intensive and innovation driven learning approach
• Expert faculty and AI researchers as mentors
• State of the art artificial intelligence laboratories
• Faculty with strong academic and research experience
• Internship and research project opportunities
• Strong placement support and career guidance
• Machine Learning and Deep Learning
• Neural Networks and Reinforcement Learning
• Natural Language Processing
• Computer Vision and Image Analytics
• Big Data and AI Engineering
• Intelligent Automation and Robotics Foundations
• Ethical AI and Responsible Computing
• Research Methodology and Scientific Writing
• Emerging AI Technologies and Innovation
Where Intelligence Meets Innovation
