B.Sc Computer Science (Artificial Intelligence)

OVERVIEW OF THE PROGRAMME

The Department of Artificial Intelligence is established in the year 2024-2025. The Department Offers B.Sc. Artificial Intelligence Programme under the faculty of Computer Science. The Curriculum and teaching pedagogy of the department will foster higher order thinking and research skills that will professionally equip students for the dynamic and ever evolving data industry. Well-designed cocurricular activities organized by the department are aimed at the holistic development of students. These activities enrich our students with essential life skills and inspire them to explore themselves.

The Department possesses a holistic environment for the vital transformation of the students while inspiring them to pursue their academic and professional paths. Our aspirants pursue this system by launching their distinct curiosity into insights, enrooting via artificial intelligence information, our graduates predominate their critical thoughts for real time extraction of information and refinements, no matter the input/output constraints, which brings out exemplary attributes for sustainable headways in the data driven domain.

PEO, PO and PSO

Program Educational Objectives (PEO):

The B. Sc. Computer Science (Artificial Intelligence) program describe accomplishments that graduates are expected to attain within five to seven years after graduation

  • PEO1 : Graduates will have Expertise in domain knowledge and get employment in the software industry as well as government departments
  • PEO2 : Graduates will have the potential to work harmoniously as team members and be able to become an entrepreneur and exhibit leadership quality. 
  • PEO3 : Graduates will appreciate human values and ethics and will show continuous improvement in their career through lifelong learning.

Program Outcomes (PO):

On successful completion of the B.Sc. Computer Science (Artificial Intelligence)

  • PO1 : Disciplinary knowledge: Capable to apply the knowledge of mathematics, algorithmic principles and computing fundamentals in the modeling and design of computer based Systems of varying complexity
  • PO2 : Scientific reasoning/Problem analysis: Ability to critically analyze, categorizes, formulate and solve the problems that emerges in the field of computer science
  • PO3 : Problem solving: Able to provide software solutions for complex scientific and business related problems or processes that meet the specified needs with appropriate consideration for the public health and safety and the cultural, societal and environmental considerations
  • PO4 : Environment and sustainability: Understand the impact of software solutions in environmental and societal context and strive for sustainable development
  • PO5 : Modern tool usage: Use contemporary techniques, skills and tools necessary for integrated solutions.
  • PO6 : Ethics: Function effectively with social, cultural and ethical responsibility as an individual or as a team member with positive attitude.
  • PO7 : Cooperation / Team Work: Function effectively as member or leader on multidisciplinary teams to accomplish a common objective
  • PO8 : Communication Skills: An ability to communicate effectively with diverse types of audience and also able to prepare and present technical documents to different groups
  • PO9 : Self-directed and Life-long Learning: Graduates will recognize the need for selfmotivation to engage in lifelong learning to be in par with changing technology.
  • PO10 : Enhance the research culture and uphold the scientific integrity and objectivity.

Program Specific Outcomes (PSO):

After the successfull completion of B.Sc. Computer Science (Artificial Intelligence)  program the students are expected to

  • PSO1 : Demonstrate mastery of Computer Science in the following core knowledge areas of Data Structures and Programming Languages, Databases, Software Engineering and Artificial Intelligence and Machine Learning
  • PSO2 :  Apply the technical and critical thinking skills in the discipline of computer science to find solutions for complex real world problems.
  • PSO3 : Ability to practice as an ethical software engineer/researcher in the evolving discipline of Computer Science and Artificial Intelligence by employing the skills learnt.

Career Opportunities

  • Big Data Engineer
  • Business Intelligence Developer
  • Data Scientist
  • Machine Learning Engineer
  • Research Scientist
  • AI Data Analyst
  • Product Manager
  • AI Engineer
  • Robotics Scientist
  • NLP Engineer
  • UX Developer
  • Researcher
  • Healthcare
  • Education
  • sports
  • Agriculture
  • Construction
  • Banking
  • Marketing
  • Ecommerce

Curriculum Structure


RATHINAM COLLEGE OF ARTS AND SCIENCE (AUTONOMOUS)

Scheme of curriculum for B.Sc. Artificial Intelligence

for the students admitted in the Batch during 2024 – 2025

Board of Studies – Computer Science (UG)

S.No.

Sem

Part

Sub Type

Sub Code

Subject

Credit

Hours

INT

EXT

Total

1

1

1

L1

  

Language – I

4

4

50

50

100

2

1

2

L2

  

English for Communication – I

4

4

50

50

100

3

1

3

Core

  

Core  – I – Problem Solving techniques using C

4

4

50

50

100

4

1

3

Core Practical

  

Core – I Practical – C  Programming Lab

2

4

20

30

50

5

1

3

DSC

DSC

DSC 1

4

4

50

50

100

6

1

3

DSC Practical

  

DSC Practical – 1

2

4

20

30

50

7

1

3

Allied-I

DSA

DSA 1A

4

4

50

50

100

8

1

4

AEC

  

Ability Enhancement Course I

2

2

50

  

50

9

1

6

VAC

  

Value Added Course – I%

2

–

50

  

50
           

28

30

      

1

2

1

L1

  

Language – II

4

4

50

50

100

2

2

2

L2

  

English for Communication – II

4

4

50

50

100

3

2

3

Core

  

Core  – II – Python Programming

4

4

50

50

100

4

2

3

Core Practical

  

Core Practical II – Python Programming Lab

2

4

20

30

50

5

2

3

DSC

DSC

DSC 2C

4

4

50

50

100

6

2

3

DSC Practical

  

DSC Practical – 2C

2

4

20

30

50

7

2

3

Allied-II

DSA

DSA 2A

4

4

50

50

100

8

2

4

AEC

  

Ability Enhancement Course II

2

2

50

  

50

9

2

6

VAC

  

Value Added Course – II %

2

–

50

  

50
           

28

30

      

1

3

3

Core

  

Core III – Java Programming

4

5

50

50

100

2

3

3

Core Practical

  

Core Practical III –  Java Programming Lab

2

4

20

30

50

3

3

3

DSC

  

DSC 3C

4

5

50

50

100

4

3

3

DSC Practical

  

DSC Practical – 3C

2

4

20

30

50

5

3

3

Allied-III

DSA

DSA 3A

4

5

50

50

100

6

3

4

SEC

SEC-I

Skill Enhancement Courses – I

2

5

20

30

50

7

3

4

AEC

  

Ability Enhancement Course III

2

2

50

  

50

8

3

6

VAC

  

Value Added Course – III %

2

–

50

  

50

9

3

6

IDL

  

Inter Department Learning – I#

2

–

50

  

50
           

24

30

      

1

4

3

Core

  

Core IV – Natural Language Processing

4

5

50

50

100

2

4

3

Core Practical

  

Core Practical IV –  Natural Language Processing Lab

2

4

20

30

50

3

4

3

DSC

DSC

DSC 4C

4

5

50

50

100

4

4

3

DSC Practical

  

DSC Practical – 4C

2

4

20

30

50

5

4

3

Allied-IV

DSA

DSA 4A

4

5

50

50

100

6

4

4

SEC

SEC-II

Skill Enhancement Courses – II

2

5

20

30

50

7

4

4

AEC

  

Ability Enhancement Course IV

2

2

50

  

50

8

4

6

VAC

  

Value Added Course – IV %

2

–

50

  

50

9

4

6

IDL

  

Inter Department Learning – II#

2

–

50

  

50
           

24

30

      

1

5

3

Core

  

Core V – Machine Learning techniques

4

4

50

50

100

2

5

3

Core Practical

  

Core Practical V  – Machine Learning Lab

2

4

20

30

50

3

5

3

DSC

DSC

DSC 5C

4

4

50

50

100

4

5

3

DSC Practical

  

DSC Practical – 5C

2

4

20

30

50

5

5

3

DSE

DSE – I

Elective  – I – DSE 1E

4

5

50

50

100

6

5

3

DSE

DSE – II

Elective  – II – DSE 2E

4

5

50

50

100

7

5

4

SEC

SEC-III

Skill Enhancement Courses – III

2

4

20

30

50

8

5

6

VAC

  

Value Added Course – V%

2

–

50

  

50
           

24

30

      

1

6

3

Core

  

Core VI –  Big Data Analytics

4

6

50

50

100

2

6

3

Core Practical

  

Core Practical VI  –  Big Data Analytics using SCALA Lab

2

4

20

30

50

3

6

3

DSE

DSE – III

Elective – III – DSE 3E

4

6

40

60

100

4

6

3

DSE

DSE – IV

Elective – IV – DSE 4E

4

6

50

50

100

5

6

3

Core Course – XI

DSC

Core Project

8

4

80

120

200

6

6

4

SEC

SEC-IV

Skill Enhancement Courses – IV

2

4

20

30

50

7

6

5

EX

  

Extension Activity- EX %

2

–

50

  

50
           

26

30

1900

1950

3850
         

Total credit

154

        

Note :

@   –     No End Semester Examination, only Internal Exam.

#    –     No Internal Examination, only End Semester Exam.

 

Discipline Specific Core

S.No

Course Code

Course

Pre-request

Offering Department

Mandatory

1

 

Data Structures

–

Computer Science

Yes

2

 

Operating System

–

Computer Application

Yes

3

 

Deep Learning

–

BCA

 

4

 

Artificial Intelligence and Knowledge Representation

–

  

 Yes

5

 

Software Engineering

–

  

 

6

 

Data Mining and warehouse

–

 

 

7

 

Ethical Hacking

–

Computer Science

Yes

8

 

Robotic Process Automation

–

 

Yes 

9

 

Data Structures Lab

DSC 1C S.NO1

Computer Science

 

10

 

Operating System Lab

DSC 1C S.NO2

Computer Application

 

11

 

Data Mining and warehouse Lab

DSC 1C S.NO3

BCA

 

12

 

Artificial Intelligence Lab

DSC 1C S.NO4

  

 

13

 

Software Engineering Lab

DSC 1C S.NO5

  

 
   

Data Mining Lab

DSC 1C S.NO6

 

  

16

 

Ethical Hacking Lab

DSC 1C S.NO8

Computer Science

 

17

  

Robotic Process Automation Lab

DSC 1C S.NO9

 

  

18

 

Professional Skills Lab

 

 

 

Allied

S.No

Course Code

Course

Pre-request

Offering Department

Mandatory

1

 

Mathematics for Computer Science

–

Maths

 

2

 

Statistics of computer science

–

Maths

Yes

3

 

Entrepreneurial Development

–

Commerce

 

4

 

Bayesian statistics

–

Maths

Yes

Skill Based Subject

S.No

Course Code

Course

Pre-request

Offering Department

Mandatory

1

  

Linux & Shell Programming

–

Computer Science

  

2

  

Predictive analysis of R Programming

–

Computer Technology

Yes

3

  

Internet Of Things

–

Information Technology

Yes

4

  

Information Security & Cyber Law

–

Computer Science

Yes

5

  

Wireless sensor network

–

Information Technology

  

6

  

Signal Processing

–

Computer Science

  

7

  

Programming with scala

–

Information Technology

  

8

  

Capstone Project Work (Based on AI & Machine Learning)

  

Information Technology

  

Discipline Specific Elective

S.No

Course Code

Course

Pre-request

Offering Department

Mandatory

1

  

Business Data Analytics

–

–

  

2

  

Social Network Analysis

–

–

  

3

  

Software Agents

–

–

  

4

  

Artificial Neural Networks and Fuzzy System

–

–

  

5

  

Web Application Security

–

–

  

6

  

Embedded Systems

–

–

  

7

  

Principles of Secure Coding

–

–

  

8

  

Open source software

–

–

  

Ability Enhancement Course

S.No

Course Code

Course

Pre-request

Offering Department

Mandatory

1

 

Environmental Studies

–

General

Yes

2

 

Women Studies

–

Commerce II

 

3

 

Constitution of India

–

Commerce I

 

4

 

Human Rights

–

General

Yes

5

 

Yoga

–

Tamil

 

6

 

NCC

–

Viscom

 

7

 

Communicative English

–

English

 

8

 

Quantitative Apptitude

–

Mathematics

 
           

Value Added Course

 

S.No

Course Code

Course

Pre-request

Offering Department

Mandatory

 

1

 

Fundamental of Office Automation

–

  

Yes

 

2

 

Advance Excel

–

  

 

 

3

 

Fundamental of Multimedia -I

–

  

 

 

4

 

Fundamental of Multimedia -II

–

  

Yes

 

5

 

Video Editing

–

  

 

 

6

 

Fundamental of Visual Effects

–