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Overview
Vision
To be a globally recognized center of excellence in the field of Artificial Intelligence and Data Science that produces innovative visionaries and research experts capable of addressing complex real-world challenges and contributing to the development of the nation.
Mission
- To provide cutting-edge education in the field of Artificial Intelligence and Data Science that is rooted in ethical and moral values.
- To establish strong partnerships with industries and research organizations in the field of Artificial Intelligence and Data Science, and to excel in the emerging areas of research by creating innovative solutions.
- To instill a sense of social responsibility in our students and inspire them to use their knowledge and skills for the furtherance of society.
- To inspire and guide our students to become entrepreneurs in the field of Artificial Intelligence and Data Science, and to develop an entrepreneurial mindset that fosters innovation and creativity.
Outcomes
Programme Educational Objectives (PEOs):
- Utilize their proficiencies in the fundamental knowledge of basic sciences, mathematics, Artificial Intelligence, data science, and statistics to build systems that require management and analysis of large volumes of data.
- Advance their technical skills to pursue pioneering research in the field of AI and Data Science and create disruptive and sustainable solutions for the welfare of ecosystems.
- Think logically, pursue lifelong learning, and collaborate with an ethical attitude in a multidisciplinary team.
- Design and model AI-based solutions to critical problem domains in the real world. Exhibit innovative thoughts and creative ideas for an effective contribution towards economy building.
Program Outcomes (POs):
- Engineering knowledge: Apply the knowledge of mathematics, science, engineering fundamentals, and an engineering specialization to the solution of complex engineering problems.
- Problem analysis: Identity, formulates, research literature, and analyze complex engineering problems reaching substantiated conclusions using the first principles of mathematics, natural sciences, and engineering sciences.
- 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 public health and safety, and cultural, societal, and environmental considerations.
- 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.
- 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.
- 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.
- Environment and sustainability: Understand the impact of professional engineering solutions in societal and environmental contexts, and demonstrate the knowledge of, and need for sustainable development.
- Ethics: Apply ethical principles and commit to professional ethics and responsibilities and norms of the engineering practice.
- Individual and team work: Function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary settings.
- Communication: Communicate effectively on complex engineering activities with the engineering community and with the 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.
- 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.
- Life-long learning: Recognize the need for, and have the preparation and ability to engage in independent and lifelong learning in the broadest context of technological change.
Programme Specific Outcomes (PSOs):
- Evolve AI-based efficient domain-specific processes for effective decision-making in several domains such as business and governance domains.
- Arrive at actionable Foresight, Insight, and hindsight from data for solving business and engineering problems
- Create, select, and apply the theoretical knowledge of AI and Data Analytics along with practical industrial tools and techniques to manage and solve wicked societal problems
- Develop data analytics and data visualization skills, skills pertaining to knowledge acquisition, knowledge representation, and knowledge engineering, and hence be capable of coordinating complex projects.
- Able to carry out fundamental research to cater to the critical needs of society through cutting-edge technologies of AI
Laboratory
Artificial Intelligence Laboratory
In this lab, the Students implement game-playing techniques. To implement CSP techniques, search strategies. To develop systems with logical reasoning. To develop systems with probabilistic reasoning.
Machine Learning Laboratory
To understand the data sets and apply suitable algorithms for selecting the appropriate features for analysis. To learn to implement supervised machine learning algorithms on standard datasets and evaluate the performance. To experiment with the unsupervised machine learning algorithms on standard datasets and evaluate the performance. To build the graph-based learning models for standard data sets.
Deep Learning Laboratory
To understand the tools and techniques to implement deep neural networks. To apply different deep learning architectures for solving problems. To implement generative models for suitable applications. To learn to build and validate different models
Database Design and Management Laboratory
To understand data definitions and data manipulation commands. To learn the use of nested and join queries. To understand functions, procedures, and procedural extensions of databases. To be familiar with the use of a front-end tool. To understand the design and implementation of typical database applications
Data Science and Analytics Laboratory
To develop data analytic code in Python. To be able to use Python libraries for handling data. To develop analytical applications using Python. To perform data visualization using plots
