B.Tech in Computer Science & Engineering with Specialization in Artificial Intelligence & Machine Learning (AI & ML) – LATERAL

• PO1 Engineering Knowledge: Apply the knowledge of mathematics, science, engineering fundamentals, and an engineering specialization to the solution of complex engineering problems.
• PO2 Problem Analysis: Identify, formulate, research literature, and analyze complex engineering problems reaching substantiated conclusions using first principles of mathematics, natural sciences, and engineering sciences.
• PO3 Design/development of Solutions: Design solutions for complex engineering problems and design system components or processes that meet the specified needs with appropriate consideration for the public health and safety, and the cultural, societal, and environmental considerations.
• PO4 Conduct Investigations of Complex Problems: Use research-based knowledge and research methods including design of experiments, analysis and interpretation of data, and synthesis of the information to provide valid conclusions.
• PO5 Modern Tool Usage: 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 the limitations.
• PO6 The Engineer and Society: Apply reasoning informed by the contextual knowledge to assess societal, health, safety, legal and cultural issues and the consequent responsibilities relevant to the professional engineering practice.
• PO7 Environment and Sustainability: Understand the impact of the professional engineering solutions in societal and environmental contexts, and demonstrate the knowledge of, and need for sustainable development.
• PO8 Ethics: Apply ethical principles and commit to professional ethics and responsibilities and norms of the engineering practice.
• PO9 Individual and Team Work: Function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary settings.
• PO10 Communication: Communicate effectively on complex engineering activities with the engineering community and with society at large, such as, being able to comprehend and write effective reports and design documentation, make effective presentations, and give and receive clear instructions.
• PO11 Project Management and Finance: Demonstrate knowledge and understanding of the 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.
• PO12 Life-long Learning: Recognize the need for, and have the preparation and ability to engage in independent and life-long learning in the broadest context of technological change.

Programme Specific Outcomes

• PSO1 The ability to understand, analyze and demonstrate the knowledge of human cognition, Artificial Intelligence, Machine Learning and data engineering in terms of real world problems to meet the challenges of the future.
• PSO2 Investigate social, environment, ethical and economic feasibility of an IT solution to a complex composite problem in terms of long-term impact and sustainability of every intricate application.
• PSO3 Keep pace with fast changing technology like Artificial Intelligence, Machine Learning, Deep learning, NLP, Cloud Computing, Computer vision and Pattern Recognition and adapt to new AI tools, systems and applications and manage challenging IT projects.

Industry-Focused Curriculum – The curriculum integrates cutting-edge AI & ML technologies with core computer science concepts, preparing students for industry demands. The courses are updated on a regular basis jointly by industry veterans and renowned academicians.

• Hands-on Learning – Emphasis on real-world applications through case studies, live projects, internships, and industry collaborations.

• State-of-the-Art Labs – Equipped with modern computing infrastructure and AI research tools for advanced learning.

• Expert Faculty & Industry Collaboration – Faculty with research and industry experience, along with collaborations with AI-driven companies and startups.

• Interdisciplinary Approach – The curriculum blends AI with data science, robotics, cyber security, biotechnology, environmental science, agriculture and many more to develop holistic technical skills.

• Career Opportunities & Placements – Strong placement support with opportunities in AI, ML, data science, automation, and software engineering roles.

• Research & Innovation – Encourages students to work on AI-driven research projects, patents, and publications under expert guidance.

• Entrepreneurship & Startups – Provides mentorship and incubation support for students interested in launching AI-based startups.