About

Welcome to the Department of Computer Science and Engineering (Artificial Intelligence and Machine Learning) at our esteemed institution. The program offers a four-year Bachelor of Engineering (B.E.) degree with a strong focus on Artificial Intelligence and Machine Learning (AI & ML).

Our curriculum is thoughtfully designed to equip students with advanced knowledge and practical skills in cutting edge technologies, preparing them to address the evolving demands of industry and research. Guided by a team of highly qualified faculty, students are encouraged to develop creativity, analytical thinking, and problem-solving abilities that empower them to excel in this rapidly advancing domain.

We also provide access to research opportunities and smart classrooms that foster experiential learning and innovation, ensuring that our graduates are industry ready and future focused.

Our eminent faculty members are dedicated to delivering high-quality education, equipping students with the skills required to excel in the dynamic field of Artificial Intelligence and Machine Learning. Strategic emphasis on creativity, critical thinking, and problem-solving abilities enables our graduates to provide sustainable solutions to challenges faced by industry and society.

HOD’s Desk

Dr. Suma Sira Jacob, Ph.D.,

Professor and Head, Department of CSE (AI & ML)

The Department of Computer Science and Engineering (Artificial Intelligence and Machine Learning), established in 2022, is committed to nurturing future ready professionals with strong foundations in Artificial Intelligence, Machine Learning, and emerging technologies. Supported by highly qualified and dedicated faculty members, the Department promotes academic excellence, experiential learning, innovation, and industry readiness through a student centric learning ecosystem.

Beyond the curriculum, students gain extensive exposure through prestigious internships, industry interactions, hackathons, coding competitions, and real-world projects. The Department takes pride in remarkable student achievements, including First Prize at the prestigious Collins Aerospace Hackathon with a cash award of ₹1 lakh and a placement offer, and an international podium finish at the Poland Open Hackathon organized by NVIDIA and ACC. Students have also gained valuable industry and research exposure through internships at reputed organizations and premier institutions, including Accenture, IITs, NITs, IIT Guwahati, and IIT Ropar.

Innovation forms an integral part of the Department’s learning culture. SYNAPSE, the annual flagship project showcase, featured 308 student projects across AI tools, computer vision, chatbots, cybersecurity, and IoT-based hardware.

As we continue to grow, our focus remains on building a department where curiosity is nurtured, innovation is celebrated, and every student is empowered to become a confident and capable contributor to the world of Artificial Intelligence and Machine Learning.

Faculty Details

Syllabus

Programme Outcomes (POs)

• PO 1: Engineering Knowledge:
  Apply knowledge of mathematics, natural science, computing, engineering fundamentals and an engineering specialization as specified in WK1 to WK4 respectively to develop to the solution of complex engineering problems.
• PO 2:
  Problem Analysis: Identify, formulate, review research literature and analyze complex engineering problems reaching substantiated conclusions with consideration for sustainable development. (WK1 to WK4)
• PO 3:
  Design/Development of Solutions: Design creative solutions for complex engineering problems and design/develop systems/components/processes to meet identified needs with consideration for the public health and safety, whole-life cost, net zero carbon, culture, society and environment as required. (WK5)
• PO 4 :
  Conduct Investigations of Complex Problems: Conduct investigations of complex engineering problems using research-based knowledge including design of experiments, modelling, analysis & interpretation of data to provide valid conclusions. (WK8).
• PO 5 :
  Engineering Tool Usage: Create, select and apply appropriate techniques, resources and modern engineering & IT tools, including prediction and modelling recognizing their limitations to solve complex engineering problems. (WK2 and WK6)
• PO 6:
  The Engineer and The World: Analyze and evaluate societal and environmental aspects while solving complex engineering problems for its impact on sustainability with reference to economy, health, safety, legal framework, culture and environment. (WK1, WK5, and WK7).
• PO 7:
  Ethics: Apply ethical principles and commit to professional ethics, human values, diversity and inclusion; adhere to national & international laws. (WK9)
• PO 8 :
  Individual and Collaborative Team work: Function effectively as an individual, and as a member or leader in diverse/multi-disciplinary teams.
• PO 9 :
  Communication: Communicate effectively and inclusively within the engineering community and society at large, such as being able to comprehend and write effective reports and design documentation, make effective presentations considering cultural, language, and learning differences
• PO 10 :
  Project Management and Finance: Apply knowledge and understanding of engineering management principles and economic decision-making and apply these to one’s own work, as a member and leader in a team, and to manage projects and in multidisciplinary environments.
• PO 11 :
  Life-Long Learning: Recognize the need for, and have the preparation and ability for i) independent and life-long learning ii) adaptability to new and emerging technologies and iii) critical thinking in the broadest context of technological change. (WK8)

Programme Educational Objectives (PEO’s)

• PEO 1:
  Graduates will be able to demonstrate technical skills and proficiency in modern Intelligent computing practices.
• PEO 2:
  Graduates will be able to navigate scientific and societal modernization through technological innovation and entrepreneurship in the Computational Intelligence
• PEO 3:
  Graduates will be able to equip productively in the field of Artificial Intelligence Engineering through strong technical communication and entrepreneurship skills.

Knowledge and Attitude Profile (WK)

• WK1
  A systematic, theory-based understanding of the natural sciences applicable to the discipline and awareness of relevant social sciences.
• WK2
  Conceptually-based mathematics, numerical analysis, data analysis, statistics and formal aspects of computer and information science to support detailed analysis and modelling applicable to the discipline.
• WK3
  A systematic, theory-based formulation of engineering fundamentals required in the engineering discipline.
• WK4
  Engineering specialist knowledge that provides theoretical frameworks and bodies of knowledge for the accepted practice areas in the engineering discipline; much is at the forefront of the discipline.
• WK5
  Knowledge, including efficient resource use, environmental impacts, whole-life cost, re-use of resources, net zero carbon, and similar concepts, that supports engineering design and operations in a practice area.
• WK6
  Knowledge of engineering practice (technology) in the practice areas in the engineering discipline.
• WK7
  Knowledge of the role of engineering in society and identified issues in engineering practice in the discipline, such as the professional responsibility of an engineer to public safety and sustainable development.
• WK8
  Engagement with selected knowledge in the current research literature of the discipline, awareness of the power of critical thinking and creative approaches to evaluate emerging issues.
• WK9
  Ethics, inclusive behavior and conduct. Knowledge of professional ethics, responsibilities, and norms of engineering practice. Awareness of the need for diversity by reason of ethnicity, gender, age, physical ability etc. with mutual understanding and respect, and of inclusive attitudes.

Programme Specific Outcomes (PSOs)

The Graduates of B.E – COMPUTER SCIENCE AND ENGINEERING (ARTIFICIAL INTELLIGENCE & MACHINE LEARNING) programme will be able to:

• PSO 1
  Apply principles and concepts of Artificial Intelligence and Machine Learning
  models to build Expert systems.
• PSO 2
  Develop computational skills and techniques to solve real time problems in the  field of Deep Learning, Natural Language Processing and Intelligent Automation.