Artificial Intelligence & Data Science

About The Department

The Artificial Intelligence and Data Science department were founded in 2020 with one undergraduate program affiliated with Anna University, approved by AICTE. This course aims to provide the core technologies such as artificial intelligence, data mining, and data modelling and give intensive inputs in machine learning and big data analytics. By the end of this course, the students will gain cross-disciplinary skills across fields such as statistics, computer science, machine learning, and logic, data scientists and will have career opportunities in healthcare, business, eCommerce, social networking companies, climatology, biotechnology, genetics, and other essential areas. The central focus of this programme is to equip students with statistical, mathematical reasoning, machine learning, knowledge discovery, and visualization skills.

Introduction to AI

Artificial intelligence (AI) is the wide-ranging branch of computer science concerned with building intelligent machines capable of performing tasks that typically require human intelligence. AI is an interdisciplinary science with multiple approaches, but advancements in machine learning and deep learning are creating a paradigm shift in virtually every tech industry sector.

B.Tech - AI & DS Advantages

Well Researched and Carefully Crafted Curriculum.
Design-Based Learning and Innovative Pedagogies.
State of the Art Infrastructure and Research facilities.
Skill Development & Personality Development.
Internships, Placements & Bright Career Prospects.
Annual Exhibition of AI Prototypes.
Facilities for Innovation, Incubation and Entrepreneurship.
Course delivery through real-world applications.
Design projects in a semester.
Flexibility in choosing specialization streams.

Programme Educational Objectives (PEOs)

Graduates, within four years of graduation, should demonstrate peer-recognized expertise together with the ability to articulate that expertise and use it for contemporary problem-solving in the analysis, design, implementation and evaluation of artificial intelligence/data science systems.
Within four years of graduation, graduates should demonstrate engagement in the engineering profession, locally and globally, by contributing to the ethical, competent, and creative practice of engineering or other professional careers.
Within four years of graduation, graduates should demonstrate sustained learning and adapt to a constantly changing field through graduate work, professional development, and self-study.
Within four years of graduation, graduates should demonstrate leadership and initiative to advance professional and organizational goals ethically, facilitate the achievements of others, and obtain substantive results.
Within four years of graduation, graduates should demonstrate a commitment to teamwork while working with others of diverse cultural and interdisciplinary backgrounds.

Programme Outcomes (POs)

Engineering Graduates will be able to:
Engineering Knowledge: Apply the knowledge of mathematics, science, engineering fundamentals, and an engineering specialization to solve complex engineering problems.
Problem Analysis: Identify, formulate, review 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 the 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 modelling to complex engineering activities to understand the limitations.
The Engineer and Society: Apply to reason 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 the 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 Teamwork: Function effectively as an individual and as a member or leader in diverse teams and multidisciplinary settings.
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.
Project Management and Finance: Demonstrate knowledge and understanding of the engineering and management principles and apply these to one’s work as a member and leader in a team, to manage projects and in multidisciplinary environments.
Life-Long Learning: Recognize the need to prepare and engage in independent and life-long learning in the broadest context of technological change.

Programme Specific Objectives (PSOs)

Professional Skills: Design and analyze optimal artificial intelligence solutions to real-world data analysis/ augmented reality/ virtual reality/ robotics.
Technical Skills: Use modern AI/Data Science open-source software tools and hardware for product development.
Research Skills: Ability to use the acquired knowledge to identify real-world research problems.
Entrepreneurship Skills: Ability to lead a product development company/team.