NBKR INSTITUTE OF SCIENCE & TECHNOLOGY
(AUTONOMOUS)
The B.Tech. Artificial Intelligence & Machine Learning programme is designed to develop graduates with strong AI foundations, practical problem-solving ability, responsible technology practices and the capacity for continuous learning in a rapidly evolving field.
Graduates are expected to achieve these professional capabilities within a few years of graduation.
To produce graduates with strong foundations in mathematics, statistics, programming, data structures, algorithms and Artificial Intelligence & Machine Learning principles, enabling them to analyze and solve complex real-world problems.
To prepare graduates for successful careers in AI/ML and allied technology domains by developing practical skills in machine learning, deep learning, data-driven systems, generative AI and modern AI engineering practices.
To develop effective communication, teamwork, leadership and professional skills, with a strong commitment to ethical, trustworthy and socially responsible use of Artificial Intelligence.
To motivate graduates to pursue higher education, research, entrepreneurship and continuous professional development, adapting to emerging areas such as intelligent agents, multimodal AI, MLOps and evolving AI technologies.
A graduate of the Artificial Intelligence and Machine Learning Program will demonstrate:
Apply the knowledge of mathematics, science, engineering fundamentals, and an engineering specialization to the solution of complex engineering problems.
Identify, formulate, review research literature, and analyze complex engineering problems reaching substantiated conclusions using first principles of mathematics, natural sciences, and engineering sciences.
Design solutions for complex engineering problems and design system components or processes that meet specified needs with appropriate consideration for public health and safety, and cultural, societal, and environmental considerations.
Use research-based knowledge and research methods including design of experiments, analysis and interpretation of data, and synthesis of information to provide valid conclusions.
Create, select, and apply appropriate techniques, resources, and modern engineering and IT tools including prediction and modeling to complex engineering activities with an understanding of their limitations.
Apply reasoning informed by contextual knowledge to assess societal, health, safety, legal and cultural issues and the consequent responsibilities relevant to professional engineering practice.
Understand the impact of professional engineering solutions in societal and environmental contexts, and demonstrate the knowledge of, and need for, sustainable development.
Apply ethical principles and commit to professional ethics and responsibilities and norms of engineering practice.
Function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary settings.
Communicate effectively on complex engineering activities with the engineering community and with society at large, including effective reports, documentation, presentations and clear instructions.
Demonstrate knowledge and understanding of engineering and management principles and apply these to one's own work, as a member and leader in a team, to manage projects and in multidisciplinary environments.
Recognize the need for, and have the preparation and ability to engage in independent and lifelong learning in the broadest context of technological change.
The Program Outcomes above retain the standard engineering graduate attributes used in the reference NBKRIST CSE page, while the PEOs and PSOs are tailored specifically to the B.Tech. AIML programme.
AI/ML-specific capabilities that distinguish graduates of the programme.
Apply mathematical, statistical, programming and machine learning knowledge to develop, train and evaluate appropriate AI/ML models for real-world problems using relevant data and tools.
Design and implement intelligent applications using deep learning, computer vision, natural language processing, generative AI and intelligent-agent technologies to address complex problems.
Develop, evaluate and deploy reliable AI solutions using appropriate software, data and cloud tools while considering model performance, scalability, security, privacy, fairness and responsible use.