Vision & Mission

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


  1. Engineering knowledge: Apply the knowledge of mathematics, science, engineering fundamentals, and an engineering specialization to the solution of complex engineering problems.
  2. 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.
  3. 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.
  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.
  5. 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.
  6. 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.
  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 sustainable development.
  8. Ethics: Apply ethical principles and commit to professional ethics and responsibilities and norms of the engineering practice.
  9. Individual and teamwork: Function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary settings.
  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.
  11. 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.
  12. 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

 

 

 

 

 

 

 


 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 


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