Engineering knowledge: To apply the knowledge of Deep learning algorithms to observe large volumes of unstructured data including text, photos and videos.

Ethics: To apply various computational thinking algorithms and commit to professional ethics and responsibilities for creating modern expert systems.

Individual and Team: To function effectively as an individual, and as a member or leader in diverse teams, and multidisciplinary settings.

Modern tool usage: To 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 limitation.

Innovative: Drives scientific and societal advancement through technological innovation and entrepreneurship.

Inspiring and Collaborative: To become a leader and a responsible citizen whose strengths come from an ability to draw on and contribute to diverse teams, expertise, and experiences.

Broadly Educated and Versatile: Able to draw upon foundational knowledge, learn, adapt and successfully bring to bear analytical and computational approaches to changing societal and technological challenges.

Cultural and Global Awareness: Recognize the applicability of computing and evaluate its impact on individuals, organizations, and global society.

Problem Solving: Identify problems and formulate solutions for systems and organizations while reconciling conflicting objectives and finding compromises.

Technical Expertise: Apply knowledge of computing and mathematics within technical domains.

Professional Practice: Evaluate and use appropriate methods and professional standards in computing practice.

Practical knowledge: Practical knowledge, which is an important part of technical education, can be achieved by a well-equipped Computer Laboratory. The Computer Laboratory is a basic component of the Institute’s infrastructure, providing a wide range of support to the students and faculties involved in research and other academic activities. The Laboratories provides the hands-on need of today’s industry requirements and what they learn in the classrooms. Our curriculum includes a wide variety of subjects to enhance the practical knowledge of the students, which are listed below:

• AI techniques
• Deep Learning algorithms
• Design and analysis of algorithm
• Object-oriented programming Language
• Computer networks
• Database management system
• Operating system
• Machine Learning Algorithms
• Embedded systems
• Web technology
• Cloud Computing
• Big data analysis
• Artificial intelligent Laboratory