Bachelor of Science (Artificial Intelligence)

Program Info
Award of Degree

For the award of BS (Artificial Intelligence) degree, a student must have:

  • Passed courses with a total of at least 132 credit hours, including all those courses that have been specified as core courses
  • Obtained a CGPA of at least 2.00

Offered Campuses

Chiniot-Faisalabad Islamabad Karachi Lahore Peshawar


  • At least 60% marks in SSC (Matric) or an equivalent examination (such as O-levels) AND
  • Should have studied for HSSC or an equivalent qualification, for at least two years AND
  • At least 50% marks in HSSC or an equivalent qualification AND
  • studied Mathematics at the HSSC or equivalent level.

Selection Criteria:

  • 50% weight to marks obtained in Admission Test AND
  • 10% weight to higher percent score of SSC (or an equivalent exam) AND
  • 40% weight to higher percent score of HSSC (or an equivalent exam)
  • Weightage of HSSC marks shall be calculated based on (which ever is applicable) at the time of compilation of merit list
    • HSSC part I and II OR
    • HSSC part I if HSSC part II not available OR
    • IBCC equivalence of A-level OR
    • IBCC equivalence of O-level
Candidates having taken NTS-NAT IE or NAT ICS exam

  • Cut-off marks in the NTS-NAT IE exam to be determined by the University
Tentative Study Plan
Sr. No Course Name Crdt Hrs.
Semester 1
1 Introduction to ICT 0+1
2 Programming Fundamentals 3+1
3 Linear Algebra 3+0
4 Calculus & Analytical Geometry 3+0
5 Pakistan Studies 3+0
6 English Composition & Comprehension 2+1
Sr. No Course Name Crdt Hrs.
Semester 2
1 Object Oriented Programming 3+1
2 Digital Logic Design 3+1
3 Differential Equations 3+0
4 Islamic Studies/ Ethics 3+0
5 Communication & Presentation Skills 2+1
Sr. No Course Name Crdt Hrs.
Semester 3
1 Programming for AI 3+1
2 Data Structures 3+1
3 Discrete Structures 3+0
4 Probability & Statistics 3+0
5 University Elective – I 3+0
Sr. No Course Name Crdt Hrs.
Semester 4
1 Artificial Intelligence 3+1
2 Fundamentals of Software Engineering 3+0
3 Database Systems 3+1
4 Computer Organization and Assembly Language 3+1
Sr. No Course Name Crdt Hrs.
Semester 5
1 Machine Learning 3+1
2 Knowledge Representation & Reasoning 3+0
3 Operating Systems 3+1
4 Design and Analysis of Algorithms 3+0
5 Technical & Business Writing 3+0
Sr. No Course Name Crdt Hrs.
Semester 6
1 Artificial Neural Networks 3+0
2 Computer Networks 3+1
3 Parallel & Distributed Computing 3+0
4 AI Elective I 3+0
5 AI Elective II 3+0
Sr. No Course Name Crdt Hrs.
Semester 7
1 Final Year Project – I 0+3
2 Computer Vision 3+1
3 Fundamentals of Natural Language Processing 3+0
4 AI Elective III 3+0
5 University Elective – II 3+0
Sr. No Course Name Crdt Hrs.
Semester 8
1 Final Year Project – II 0+3
2 Information Security 3+0
3 Professional Practices 3+0
4 University Elective – III 3+0
5 AI Elective IV 3+0

Note: Registration in “Project-I” is allowed provided the student has earned at least 100 credit hours, and his/her CGPA is equal to or greater than the graduating CGPA (2.0).

Program Educational Objectives (PEO)

  1. Knowledge of the fundamentals of Computing and Artificial Intelligence - A graduate who is performing his/her professional roles with understanding of fundamental knowledge of computing and artificial intelligence acquired during his studies.
  2. Ethical and Societal Responsibilities - A graduate who is fulfilling his/her professional responsibilities taking into account ethical and societal concerns with special emphasis on artificial intelligence and its applications.
  3. Communication Skills - A graduate who is effective in oral and written communication of technical and managerial information.
  4. Leadership - A graduate who is effective in a leadership role of a group/team assigned to him/her or in an entrepreneurial environment.
  5. Continuous Improvement - A graduate who keeps on exploring new fields and areas in computing and artificial intelligence for his/her organization or conducts research for academic pursuits.

Program Learning Outcomes (PLOs)

  1. Computing and Artificial Intelligence Knowledge - Apply knowledge of mathematics, natural sciences, computing fundamentals, and a computing specialization to solve complex computing problems using artificial intelligence techniques.
  2. Problem Analysis - Identify, formulate, research literature, and analyze complex computational problems, reaching substantiated conclusions using first principles of mathematics, natural sciences, computing, and artificial intelligence.
  3. Design/Develop Solutions - Design solutions for complex computing problems and design systems, components, and processes that meet specified needs with appropriate consideration for public health and safety, cultural, societal, and environmental considerations.
  4. Investigation & Experimentation - Conduct investigation of complex computing problems using research based knowledge and research based methods.
  5. Modern Tool Usage - Create, select, and apply appropriate techniques, resources and modern computing and artificial intelligence tools, including prediction and modelling for complex computing problems.
  6. Society Responsibility - Apply reasoning informed by contextual knowledge to assess societal, health, safety, legal, and cultural issues relevant to context of complex computing problems.
  7. Environment and Sustainability - Understand and evaluate sustainability and impact of professional computing and artificial intelligence work in solving complex computing problems.
  8. Ethics - Apply ethical principles and commit to professional ethics and responsibilities and norms of computing and artificial intelligence practice.
  9. Individual and Team Work - Function effectively as an individual, and as a member or leader in diverse teams and in multi-disciplinary settings.
  10. Communication - Communicate effectively on complex computing and AI activities with the computing and artificial intelligence community and with society at large.
  11. Project Management and Finance - Demonstrate knowledge and understanding of management principles and economic decision making and apply these to one's own work as a member or a team.
  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 changes.