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

Eligibility:

  • 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

Academic Education

Completion of an accredited program (BS AI) of study designed to prepare graduates as computing professionals

2

Knowledge for Solving Computing Problems

Apply knowledge of computing fundamentals, knowledge of a computing specialization, and mathematics, science, and domain knowledge appropriate for the computing specialization to the abstraction and conceptualization of computing models from defined problems and requirements

3

Problem Analysis

Identify, formulate, research literature, and solve complex computing problems reaching substantiated conclusions using fundamental principles of mathematics, computing sciences, and relevant domain disciplines

4

Design/ Development of Solutions

Design and evaluate solutions for complex computing problems, and design and evaluate systems, components, or processes that meet specified needs with appropriate consideration for public health and safety, cultural, societal, and environmental considerations

5

Modern Tool Usage

Create, select, adapt and apply appropriate techniques, resources, and modern computing tools to complex computing activities, with an understanding of the limitations

6

Individual and Team Work

Function effectively as an individual and as a member or leader in diverse teams and in multi-disciplinary settings

7

Communication

Communicate effectively with the computing community and with society at large about complex computing activities by being able to comprehend and write effective reports, design documentation, make effective presentations, and give and understand clear instructions

8

Computing Professionalism and Society

Understand and assess societal, health, safety, legal, and cultural issues within local and global contexts, and the consequential responsibilities relevant to professional computing practice

9

Ethics

Understand and commit to professional ethics, responsibilities, and norms of professional computing practice

10

Life-long Learning

Recognize the need, and have the ability, to engage in independent learning for continual development as a computing professional