B.Tech Artificial Intelligence and Data Science - Vision and Mission
VISION
AND MISSION
VISION
- To achieve academic excellence in the domain of Artificial Intelligence and Data Science and produce globally competent professionals to solve futuristic societal challenges
MISSION
- To actively engage in the implementation of innovative intelligent solutions for interdisciplinary
Artificial Intelligence based solutions with ethical standards
- To promote research,
innovation and entrepreneurial skills through industry and academic
collaboration
PROGRAM EDUCATIONAL OBJECTIVES (PEOS)
The graduates of this program after
four to five years will,
PEO 1:
Design and develop solutions for real-world problems based on business and
societal needs, as skilled professionals or entrepreneurs.
PEO 2:
Apply Artificial Intelligence and Data Science knowledge and skills to develop
innovative solutions for multi-disciplinary problems, adhering to ethical
standards
PEO 3:
Engage in constructive research, professional development and life-long
learning to adapt with emerging technologies
Program Outcomes and Program Specific Outcomes (POs and PSOs)
Program
Outcomes as stated by NBA: Engineering Graduates will be able to
- Engineering
knowledge: Apply the knowledge of mathematics, science, engineering
fundamentals, and an engineering specialization to the solution of complex engineering
problems.
- Problem
analysis: Identity, formulate, review research literature, and analyze complex
engineering problems reaching substantiated conclusions using 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 with an understanding of 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 in 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 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 life–long learning in the broadest context of technological change.
PROGRAM SPECIFIC OUTCOMES:
Graduates
of Artificial Intelligence and Data Science at the time of graduation will be
able to
PSO 1: Analyze, design and build sustainable intelligent solutions to solve
challenges imposed by industry and society.
PSO2: Demonstrate
data analysis skills to achieve effective insights and decision making to solve
real-life problems.
PSO3: Apply mathematical and statistical models to solve the computational tasks, and model real-world problems using appropriate AI / ML algorithms.
Category wise distribution of credits
S.
No |
Category |
Credits
per Semester |
Credits
Total |
|||||||
I |
II |
III |
IV |
V |
VI |
VII |
VIII |
|||
1 |
Humanities and Sciences(HS) |
3 |
3 |
1 |
3 |
- |
- |
- |
- |
10 |
2 |
Basic Sciences(BS) |
10 |
4 |
3 |
3 |
4 |
- |
- |
- |
24 |
3 |
Engineering Sciences(ES) |
7 |
15 |
4 |
- |
- |
- |
- |
- |
26 |
4 |
Professional Core(PC) |
- |
- |
16 |
17 |
14 |
10 |
- |
- |
60 |
5 |
Professional Electives(PE) |
- |
- |
- |
- |
3 |
6 |
9 |
- |
18 |
6 |
Open Electives(OE) |
- |
- |
3 |
3 |
3 |
- |
- |
- |
09 |
7 |
Employability Enhancement Course(EEC) |
|||||||||
I. Project work |
- |
- |
- |
2 |
- |
2 |
- |
8 |
12 |
|
II. Mandatory Courses |
- |
- |
- |
- |
- |
- |
2 |
- |
02 |
|
|
III. One Credit Course |
- |
- |
- |
- |
1 |
- |
- |
- |
01 |
Total |
20 |
22 |
27 |
28 |
25 |
18 |
11 |
8 |
162 |