A. Shama Rao Foundation SRINIVAS INSTITUTE OF TECHNOLOGY Mangaluru 574143, Karnataka (NAAC Accredited, Affiliated to VTU, Belgavi and Recognized by the AICTE, New Delhi)
 
CET CODE
E144
COMEDK Code
E138

Department Of Artificial Intelligence and Machine Learning

Overview

Four year B.E. undergraduate course in Artificial Intelligence (AI) & Machine Learning (ML) is designed to inculcate technically sound knowledge in advanced learning systems that are based on algorithms of Artificial Intelligence and Machine Learning. AI is changing the way we work and live. AI is already creating tremendous amounts of value into the software industry and in sectors such as retail, travel, transportation, automotive, materials, manufacturing and so on. Understanding AI and knowing how to create AI powered applications gives you an edge in your career. AI is about teaching the machines to learn, act and think as humans. It is the application of computing to solve problems in an intelligent way using algorithms. AI systems typically demonstrate behaviours associated with human intelligence such as planning, learning, reasoning, problem-solving, knowledge representation, perception, psychology, manipulation, social intelligence and creativity. Machine learning is a subset of AI that uses computer algorithms to analyze data and make intelligent decisions based on what it has learned, without being explicitly programmed. Machine learning algorithms are trained with large sets of data and programmed to learn from examples. Machines do not follow rule-based algorithms. Machine learning is what enables machines to solve problems on their own and make accurate predictions using the provided data.

Vision :
• To be a centre of excellence in Artificial Intelligence and Machine Learning with quality education and research, responsive to the needs of industry and society.

Mission:
• To achieve academic excellence through innovative teaching-learning practice.
• To inculcate the spirit of innovation, creativity and research.
• To enhance employability through skill development and industry-institute interaction.
• To develop professionals with ethical values and social responsibilities.


Program Educational Objectives (PEOs)

Graduates will be,

PEO1: Competent professionals in the field of Artificial Intelligence and Machine Learning to pursue careers in diverse fields and higher education.

PEO2: Proficient in designing innovative solutions to real life problems that are technically sound, economically viable and socially acceptable.

PEO3: Capable of working in teams and adapting to new technologies as per the society needs with ethical values.


Program Specific Outcomes (PSOs)

PSO1: Hardware and hardware-software co-design: Ability to identify, design, simulate, analyse and develop AI and ML models, using modern engineering tools and programming languages.

PSO2: Domain specific skills: Ability to work in the field of AI /ML Engineer, Data Scientist, Business Analyst, Product Analyst, Research Scientist and Robotics Professional.


Program Outcomes (Pos)

Engineering Graduates will be able to:

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, review 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 modelling 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.