JNTUA M.Tech R25_CSE (AI & ML) Course Structure & Syllabus

The JNTUA M.Tech R25 CSE (Artificial Intelligence & Machine Learning) program is designed to provide advanced knowledge in computing, intelligent systems, and data-driven technologies. The course structure follows the R25 regulations introduced for the academic year 2025–26, focusing on both theoretical foundations and practical applications in AI and ML domains.

The program is typically structured across four semesters in two years. In the first year, students study core subjects such as Mathematical Foundations of Computer Science, Advanced Data Structures, Advanced Algorithms, and Advanced Computer Architecture. These courses strengthen analytical thinking and programming efficiency. Along with core subjects, students choose professional electives like Deep Learning, Natural Language Processing, Cyber Security, Cloud Computing, Generative AI, and Large Language Models, allowing specialization in emerging technologies.

Laboratory courses play a major role in skill development. Students undergo practical training through Advanced Data Structures Lab, Algorithms Lab, and AI/ML-related elective labs, where they implement algorithms, machine learning models, and real-world problem-solving systems. A Mini Project with Seminar is also included to enhance research and presentation skills.

In addition, the syllabus includes Research Methodology and Intellectual Property Rights (IPR), which prepares students for academic research and innovation. Audit courses are also part of the curriculum to improve interdisciplinary knowledge.

In the second year, students focus on advanced electives such as Quantum Computing, Advanced Operating Systems, Prompt Engineering, Digital Forensics, and Open Electives, along with a major project/dissertation, which forms the final evaluation of the program. This project encourages students to work on real-time AI/ML applications in industries or research areas.

Overall, the JNTUA M.Tech R25 CSE (AI & ML) syllabus is structured to balance strong theoretical concepts with hands-on experience, ensuring students are industry-ready for careers in Artificial Intelligence, Machine Learning, Data Science, and Research & Development roles.