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

B.Sc. Computer Science (Artificial Intelligence) Curriculum Structure – Regulation – 2024
 
S.No. Sem Part Sub Type Course Code Course Name Credit Hours INT EXT Total
1 1 1 L1   Language – I 3 5 50 50 100
2 1 2 L2   English – I 3 5 50 50 100
3 1 3 Core    Core Course – I Theory
Problem Solving Techniques in C 		4 5 50 50 100
4 1 3 Core     Core Course – II Theory / Practical
Programming Lab in C 		4 4 50 50 100
5 1 3 Allied   Allied-I
Mathematics for Computer Science 		4 5 50 50 100
6 1 4 SEC   Skill Enhancement Courses – I Database Management System / Practical – Database Management system Lab 4 4 50 50 100
7 1 4 AEC   Ability Enhancement Course I Environmental Studies or Universal Human Values & Professional Ethics 2 2 50 0 50
            24 30 350 300 650
                     
1 2 1 L1   Language – II 3 5 50 50 100
2 2 2 L2   English – II 3 5 50 50 100
3 2 3 Core    Core Course – III Theory
Python Programming  		4 5 50 50 100
4 2 3 Core     Core Course – IV Theory / Practical
Python Programming Lab 		4 4 50 50 100
5 2 3 Elective   Elective – I Entrepreneurship Development 4 4 50 50 100
6 2 3 Allied   Allied-II
Discrete Mathematics 		4 5 50 50 100
7 2 4 AEC   Ability Enhancement Course II Design Thinking 2 2 50 0 50
8 2 5 Ext   Extension Activity – I (NASA) 1 0 25 0 25
            25 30 375 300 675
                     
1 3 1 L1   Language – III 3 4 50 50 100
2 3 2 L2   English – III 3 4 50 50 100
3 3 3 Core    Core Course – V Theory
Artificial Intelligence 		4 6 50 50 100
4 3 3 Core     Core Course – VI Theory / Practical
Artificial Intelligence Lab 		4 4 50 50 100
5 3 3 Allied   Allied-III
Numerical Methods 		4 5 50 50 100
6 3 4 SEC   Skill Enhancement Courses – II Practical / Training
Internet of Things 		4 5 50 50 100
7 3 4 AEC   Ability Enhancement Course III Soft Skill-1 2 2 50 0 50
8 3 3 ITR   Internship / Industrial Training (Summer vacation at the end of II semester activity) 2 0 50 0 50
9 3 5 Ext   Extension Activity – II (NASA) 1 0 25 0 25
            27 30 425 300 725
                     
1 4 1 L1   Language – IV 3 4 50 50 100
2 4 2 L2   English – IV 3 4 50 50 100
3 4 3 Core    Core Course – VII Theory
Big Data Analytics 		4 6 50 50 100
4 4 3 Core     Core Course – VIII Theory / Practical
Big Data Analytics Lab 		4 4 50 50 100
5 4 3 Allied   Allied-IV
Statistical Methods and its application 		4 5 50 50 100
8 4 3 Elective    Elective – II
Option 1: AI in Cloud Computing
Option 2: Deep Learning
Option 3: AI and Expert System 		4 5 50 50 100
7 4 4 AEC   Ability Enhancement Course IV Soft Skill-2 2 2 50 0 50
8 4 5 Ext   Extension Activity – III (NASA) 1 0 25 0 25
            25 30 375 300 675
                     
1 5 3 Core    Core Course – IX Theory
Artificial Neural Networks 		4 6 50 50 100
2 5 3 Core     Core Course – X Theory / Practical
Artificial Neural Networks Lab 		4 6 50 50 100
3 5 3 Elective    Elective – III
Option 1: Network Security and Cryptography
Option 2: AI in Cyber Security
Option 3: Data Communication and Networking 		4 6 50 50 100
  5 3 PRJ   Project 0 6 0 0 0
4 5 4 SEC   Skill Enhancement Courses – III Practical / Training
Power BI 		4 6 50 50 100
5 5 3 ITR   Internship / Industrial Training
(Summer vacation at the end of IV semester activity) 		2 0 50 0 50
6 5 5 Ext   Extension Activity – IV (NASA) 1 0 25 0 25
            19 30 275 200 475
                     
1 6 3 Core    Core Course – XI Theory
Computing Intelligence 		4 6 50 50 100
2 6 3 Core     Core Course – XII Theory / Practical
Computing Intelligence Lab 		4 4 50 50 100
3 6 3 Elective    Elective – IV
Option 1: Deep Learning 
Option 2: Data Mining and Warehousing
Option 3: Mobile Computing 		4 6 50 50 100
4 6 3 PRJ   Core Project 8 8 100 100 200
5 6 4 SEC   Skill Enhancement Courses – IV Practical / Training
R Programming 		4 6 50 50 100
            24 30 300 300 600
          Total credit 144 180 2100 1700 3800
                     
                   
                   
                     
Additional Credits
S.No Sem Part Sub Type Course Code Course Name Credit Hours INT EXT Total
1 2 6 VAC   VAC – Microsoft CoE Course / NPTEL 2 2 50 0 50
3 4 6 IDC   VAC – Microsoft CoE Course / NPTEL 2 2 50 0 50
4 5 6 VAC   VAC – Microsoft CoE Course / NPTEL 2 2 50 0 50
                     
                     
Certificate on Minor Discipline
S. No Sem Part Sub Type Course Code Course Name Credit Hours INT EXT Total
1 2 6 MD   Course – I 5 2 0 100 100
2 3 6 MD   Course – II 5 2 0 100 100
3 4 6 MD   Course – III 5 2 0 100 100
4 5 6 MD   Course – IV 5 2 0 100 100
                     
                     
                     
Core – Theory
S. No. Sem Pre-requisite Course Code Course Name Offering Department Type Theory / Practical
1 1     Problem Solving Techniques in C CS- Industry Training Theory
2 2     Python Programming CS- Industry Training Theory
3 3     Artificial Intelligence CS- Industry Training Theory
4 4     Big Data Analytics CS- Industry Training Theory
5 5     Artificial Neural Networks CS- Industry Training Theory
6 6     Computing Intelligence CS- Industry Training Theory
                   
Core – Theory / Practical
S.No. Sem Pre-requesite Course Code Course Name Offering Department Type Theory / Practical
1 1     Programming Lab in C CS-Industry Training Practical
2 2     Python Programming Lab CS-Industry Training Practical
3 3     Artificial Intelligence Lab CS-Industry Training Practical
4 4     Big Data Analytics Lab CS-Industry Training Practical
5 5     Artificial Neural Networks Lab CS-Industry Training Practical
6 6     Computing Intelligence Lab CS-Industry Training Practical
                     
Allied
S.No. Sem Pre-requesite Course Code Course Name Offering Department Type Theory / Practical
1 1     Mathematics for Computer Science Mathematics Theory
2 2     Discrete Mathematics Mathematics Theory
3 3     Numerical Methods Mathematics Theory
4 4     Statistical Methods and its application Mathematics  
          Skill Enhancement Course          
S.No. Sem Pre-requesite Course Code Course Name Offering Department Type Practical / Training
1 1     Database Management System Computer Science Training
2 3     Internet of Things Computer Science Training
3 5     Power BI Computer Science Training
4 6     R Programming Computer Science Training
5            
                 
               
Elective
S.No. Sem Pre-requesite Course Code Course Name Offering Department Type Practical / Training
1 4       AI in Cloud Computing  Computer Science Theory
2 4       Deep Learning Computer Science Theory
3 4       AI and Expert System Computer Science Theory
4 5       Network Security and Cryptography  Computer Science Theory
5 5       AI in Cyber Security Computer Science Theory
6 5       Data Communication and Networking Computer Science Theory
7 6       Deep Learning Computer Science Theory
8 6       Data Mining and Warehousing Computer Science Theory
9 6       Mobile Computing Computer Science Theory