Department of Artificial Intelligence and Machine Learning
About the Department
Vision and Mission
Prog. Edu Objectives & Outcomes
About the Department
The department of Artificial Intelligence and Machine Learning has been
established in the year 2022, recognized by AICTE. Artificial Intelligence
specialization will create engineers, who can build solutions, with
The Department conducts co-curricular activities such as Guest Lectures,
Training programs, Workshops, etc. The Department also organizes industrial
to help students to update their knowledge.
An exclusive Training and Placement cell is functioning for providing
training and placement assistance to the students during their course of
students are trained by internal trainers and industry experts with various
partners to improve their employability skills like soft skills, technical,
analytical and logical skills. Apart from training, to enhance the
thinking and managerial skills of young minds, they are encouraged to
participate in various events like paper presentations, technical symposia
Currently, the world is being driven digitally with technology and data
present everywhere. It is because many leading companies in the world today,
Face book, Google and Uber, make machine learning as a central part of their
operations. The machine learning market size has been steadily growing. It
is a type
of data mining that allows computers to learn on their own experience.
Nowadays, it's not an exaggeration to say that each of us encounters
learning multiple times daily. Until the 1960s AI and ML were more or less
synonymous, but thereafter they diverged and now they are rampant in the
industry, mass media and academia.
Data scientists and machine-learning engineers are two of the top-ranked
globally. Professionals with critical expertise in the domain of data
machine learning are highly sought after by tech and finance organizations.
This course dives deep into the foundational as well as advanced concepts
science and machine learning to enable students to devise practical
The department provides state-of-the-art computing facilities to the
enabling them to stay a step ahead. They are exposed to various
as in plant training, internships, and workshops during their course of
Vision and Mission
To be a center of excellence in the field of Artificial Intelligence and Machine
Learning applications through appropriate use and diffusion of emerging
To develop students with strong capabilities in Artificial Intelligence
by continuously enhancing teaching and learning with state-of-the-art
To provide high-quality, value-based education to gain competence in
Artificial Intelligence and Machine learning in terms of research and
To implement engineering solutions for the benefit of society by the
use of AI and ML.
Programme Educational Objectives(PEOs)
PEO 1: To perform well in their professional career by
acquiring enough knowledge in the domain of Artificial Intelligence and
PEO 2: To enable graduates to research, design and
implement AI/ML products and services with effective Communication and
PEO 3: To modernize the students with evolving
and use it for career advancement.
Programme Outcomes (POs)
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
PO 4: 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.
PO 5: 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.
PO 6: 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.
PO 7: Environment and sustainability: Understand the
impact of the professional engineering solutions in societal and
environmental contexts, and demonstrate the knowledge of, and need for
PO 8: Ethics: Apply ethical principles and commit to
professional ethics and responsibilities and norms of the engineering
PO 9: Individual and team work: Function effectively
as an individual, and as a member or leader in diverse teams, and in
PO 10: 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.
Programme Specific Outcomes (PSOs)
PSO1: Develop models in Data Science, Machine learning,
Deep learning and Big data technologies, using AI and modern tools.
PSO2: Create solutions for interdisciplinary AI problems
through acquired programming knowledge in the respective domains fulfilling
with real-time constraints.
Dr.B.Anni Princy M.E, Ph.D.,
Professor and Head of the Department in Artificial Intelligence & Machine Learning
She received M.E., degree in Computer Science & Engineering in the year 2004 and Ph.D
in Computer Science & Engineering in the year 2015 from Sathyabama University,
Chennai,. She has a total teaching experience of 27 years. She has published several
research articles in National and International Conferences and Journals. Her areas
of interest include Software Reliability Engineering, Networking. She has published
three Patents. She is the member of the professional societies IEEE & CSI. She
received fund from AICTE for MODROB.
Name of the Faculty
Dr. B. ANNIPRINCY
PROF & HEAD
Question Bank - Academic Year 2023 - 24 (Odd Semester)