The M.Tech. Artificial Intelligence and Machine Learning programme is a 2-year postgraduate course designed to develop advanced expertise in intelligent systems and data-driven technologies. The programme focuses on core areas such as artificial intelligence, machine learning, deep learning, natural language processing, and computer vision.
The curriculum blends strong theoretical foundations with practical applications, enabling students to design, develop, and deploy intelligent solutions for complex real-world problems. With a strong emphasis on research, innovation, and interdisciplinary learning, students gain hands-on experience through advanced labs, projects, internships, and dissertation work.
Advanced curriculum focused on AI and machine learning technologies
Specialization in deep learning, NLP, computer vision, and intelligent systems
Strong emphasis on research, innovation, and real-world applications
Hands-on learning through projects, labs, and industry collaborations
Access to modern AI tools, platforms, and computing infrastructure
Opportunities for internships, publications, and research projectsp>
Industry-aligned electives in emerging AI domains
Preparation for both industry and research/academic careers
Graduates of the programme can pursue advanced roles across industries such as IT, healthcare, finance, robotics, and research organizations..
Career Roles Include:
AI Engineer
Machine Learning Engineer
Data Scientist
AI Research Scientist /p>
Computer Vision Engineer
NLP Engineer
The programme also provides a strong foundation for pursuing Ph.D. and advanced research in artificial intelligence and machine learning.
The M.Tech. Artificial Intelligence and Machine Learning programme is a 2-year postgraduate program divided into 4 semesters, comprising a total of 68 credits. The curriculum combines strong theoretical foundations with practical exposure through advanced laboratories, projects, internships, seminars, and a research-driven dissertation. Emphasis is placed on innovation, critical thinking, and industry relevance, preparing graduates for both professional careers and higher research pursuits.
Semester 1 – Advanced Foundations: Establishes advanced foundations in computing and artificial intelligence through in-depth study of data structures, algorithms, and computing systems for efficient and scalable solution design. Strengthens mathematical concepts essential for AI, including linear algebra, probability, and optimization. Research methodology equips students with skills for scientific investigation and innovation, while elective courses offer flexibility to specialize in emerging AI and machine learning domains.
Semester 2 – AI & ML Core: Focuses on core and advanced AI technologies, including artificial intelligence, machine learning, deep learning, and specialized areas such as natural language processing and computer vision. Enables students to develop intelligent, data-driven systems for complex real-world applications. Elective courses provide opportunities for specialization in emerging domains, while mini projects and seminars enhance practical skills, research aptitude, and technical communication.
Semester 3 – Advanced Learning & Research: Emphasizes advanced learning and research through specialized electives in AI and machine learning, such as reinforcement learning, AI ethics, and advanced analytics. Provides practical exposure through industry internships or research work, enabling students to apply their knowledge to real-world challenges. Dissertation – Phase I initiates independent research, focusing on problem identification, literature review, and the development of innovative AI-driven solutions.
Semester 4 – Dissertation & Specialization: Focuses on the completion of the dissertation (Phase II), where students implement, validate, and refine their AI/ML models and research outcomes. It culminates in project completion and encourages research publication, enabling students to contribute to scholarly work and demonstrate advanced expertise in artificial intelligence and machine learning.
Candidates must have a Bachelor’s degree (B.E./ B.Tech.) in Computer Science and Engineering or in Computer Science and Engineering allied discipline from a recognized university.
A minimum of 60% aggregate marks (or equivalent CGPA) as per university/AICTE norms.
A valid score in GATE or any relevant national/state/university-level entrance examination.
Candidates from allied branches such as IT, Electronics, or equivalent may also be considered as per university / AICTE norms.
| Students Under Indian Category | ₹ Per Annum |
|---|---|
| Academic Fees | ₹ 3,00,000 |
| Hostel Fees (2 Sharing Includes Room & Mess Charges) |
₹ 1,50,000 |
| Hostel Fees (4 Sharing Includes Room & Mess Charges) |
₹ 1,25,000 |
| Caution Deposit (Refundable at the End of the Course or After Graduation) |
₹ 20,000 |
| Students Under NRI/PIO/FO Category | $ USD Per Annum |
|---|---|
| Academic Fees | $5000 |
| Hostel Fees (Includes Room & Mess Charges) |
$2500 |
| Caution Deposit (Refundable at the End of the Course or After Graduation) |
$400 |
Refund Rules:
Fee refund & cancellation policy will be as per guidelines published by UGC/Statutory authorities.
Note:
Dates and schedules are subject to change.
Kaveri University was established as per the Telangana Private Universities (Establishment & Regulation) Act, 2018 under section 3.
