Program Sections

Description PEOs PLOs Semester 1 Semester 2 Semester 3 Semester 4 Semester 5 Semester 6 Semester 7 Semester 8
BS Artifical Intellgence (4 Years)
Description:

This undergraduate program provides a comprehensive foundation in the principles and technologies of Artificial Intelligence. Students gain in-depth knowledge of machine learning, data science, computer vision, natural language processing, robotics, and intelligent systems. The curriculum combines theoretical understanding with practical skills in programming, mathematics, and data analysis. Graduates are prepared to design, develop, and apply AI solutions across various industries, from healthcare and finance to autonomous systems and smart applications.

PEOs (Program Educational Objectives):
PEO No. Program Educational Objective
PEO 1 Foundational Knowledge in Computing and Artificial Intelligence: Graduates will understand and apply core principles of computing and AI, effectively fulfilling their professional roles.
PEO 2 Ethical and Societal Responsibilities: Graduates will approach their professional duties with strong ethical awareness and sensitivity to the societal impacts of AI.
PEO 3 Effective Communication Skills: Graduates will be proficient in clearly and persuasively articulating technical and managerial concepts, both verbally and in writing.
PEO 4 Leadership Abilities: Graduates will demonstrate leadership and management skills in team-based, collaborative, or entrepreneurial environments.
PEO 5 Commitment to Continuous Improvement: Graduates will remain committed to lifelong learning, staying current with advancements in computing and AI, and contributing to research or organizational development.
PLOs (Program Learning Outcomes):
PLO No. Program Learning Outcome
PLO 1 Academic Education: To prepare graduates as computing professionals.
PLO 2 Knowledge for Solving Computing Problems: Successful completion of an accredited BS in AI program, equipping graduates with essential skills and knowledge for computing professions.
PLO 3 Problem Analysis: Identify, formulate, research literature, and solve complex computing problems using principles of mathematics, computing sciences, and domain knowledge.
PLO 4 Design/Development of Solutions: Design and evaluate solutions to complex computing problems, considering public health, safety, cultural, societal, and environmental aspects.
PLO 5 Modern Tool Usage: Create, select, adapt, and apply modern computing tools and techniques with awareness of their limitations.
PLO 6 Individual and Team Work: Function effectively as an individual or leader in diverse teams and multidisciplinary environments.
PLO 7 Communication: Communicate effectively with the computing community and society through reports, documentation, presentations, and instructions.
PLO 8 Computing Professionalism and Society: Understand and assess societal, health, safety, legal, and cultural issues in local and global contexts, recognizing professional responsibilities.
PLO 9 Ethics: Understand and commit to professional ethics, responsibilities, and norms of computing practice.
PLO 10 Life-long Learning: Recognize the need and possess the ability for independent, lifelong learning for continual professional development.
Semester 1:
Course Code Title of the Course Cr. Hrs (Cont. Hrs) Pre-Requisite Domain
GE-101 Application of Information & Communication Technologies 3(2-1), 5 – GER
CS-101 Programming Fundamentals 4(3-1), 6 – Comp. Core
MTH-102 Calculus & Analytical Geometry 3(3-0), 3 – GER
ENG-103 Functional English 3(3-0), 3 – GER
GE-104 QR1* (Discrete Structures) 3(3-0), 3 – GER
PS-105 Ideology & Constitution of Pakistan 2(2-0), 2 – GER
— Pre-Calculus (for FSc Pre-Medical students) NC – –
Total   18 (16-2)
Semester 2:
Sr. # Course Code Title of the Course Cr. Hrs (Cont. Hrs) Pre-Requisite Domain
1 CS-102 Object Oriented Programming 4(2-1), 5 CS-101 Comp. Core
2 CS-103 Digital Logic Design 3(2-1), 5 – Comp. Core
3 ENG-106 Expository Writing 3(3-0), 3 ENG-103 GER
4 IS-107 Islamic Studies / Ethics 2(2-0), 2 – GER
5 MTH-304 Multivariable Calculus 3(3-0), 3 MTH-102 Math & Sup. Course
6 CS-104 Artificial Intelligence 3(2-1), 5 – Comp. Core
Total —   18 (15-3)
Semester 3:
Sr. # Course Code Title of the Course Cr. Hrs (Cont. Hrs) Pre-Requisite Domain
1 CS-205 Data Structures 4(3-1), 6 CS-102 Comp. Core
2 CS-206 Computer Networks 3(2-1), 5 – Comp. Core
3 CS-207 Software Engineering 3(3-0), 3 – Comp. Core
4 EW-201 Technical & Business Writing 3(3-0), 3 – Math & Sup. Course
5 PHY-208 Applied Physics 3(2-1), 5 – GER
6 MTH-202 Linear Algebra 3(3-0), 3 MTH-102 Math & Sup. Course
Total —   19 (16-3)
Semester 4:
Sr. # Course Code Title of the Course Cr. Hrs (Cont. Hrs) Pre-Requisite Domain
1 CS-208 Operating Systems 3(2-1), 5 – Comp. Core
2 CS-209 Database Systems 4(3-1), 6 – Comp. Core
3 AI-202 Machine Learning 3(2-1), 5 – Dom. Core
4 CS-210 Analysis of Algorithms 3(3-0), 3 CS-204 Comp. Core
5 MTH-203 Probability & Statistics 3(3-0), 3 – Math & Sup. Course
Total —   16 (13-3)
Semester 5:
Sr. # Course Code Title of the Course Cr. Hrs (Cont. Hrs) Pre-Requisite Domain
1 AI-303 Artificial Neural Networks & Deep Learning 3(2-1), 5 – Dom. Core
2 AIE-301 Elective 1: Advanced Statistics 3(3-0), 3 – Dom. Elective
3 AIE-302 Elective 2: Natural Language Processing 3(2-1), 5 – Dom. Elective
4 AIE-303 Elective 3: Speech Processing 3(2-1), 5 – Dom. Elective
5 AI-304 Programming for AI 3(3-0), 3 – Dom. Core
6 MGT-309 Introduction to Management 3(3-0), 3 – GER
Total –   18 (14-4)
Semester 6:
Sr. # Course Code Title of the Course Cr. Hrs (Cont. Hrs) Pre-Requisite Domain
1 AI-305 Knowledge Representation & Reasoning 3(2-1), 5 – Dom. Core
2 AIE-304 Elective 4: Data Mining 3(2-1), 5 – Dom. Elective
3 AI-306 Computer Vision 3(2-1), 5 – Dom. Core
4 CS-311 Computer Organization & Assembly Language 3(2-1), 5 CS-103 Comp. Core
5 CS-312 Information Security 3(2-1), 5 – Comp. Core
6 MGT-310 Entrepreneurship 3(3-0), 3 – GER
Total –   18 (14-4)
Semester 7:
Sr. # Course Code Title of the Course Cr. Hrs (Cont. Hrs) Pre-Requisite Domain
1 CS-413 Final Year Project – I 2(0-2), 6 – Comp. Core
2 AIE-405 Elective 5: Reinforcement Learning 3(2-1), 5 – Dom. Elective
3 AIE-406 Elective 6: Theory of Automata 3(2-1), 5 – Dom. Elective
4 MGT-411 Civics & Community Engagement 2(2-0), 2 – GER
5 AIE-407 Elective 7: HCI & Computer Graphics 3(3-0), 3 – Dom. Elective
Total –   14 (8-6)
Semester 8:
Sr. # Course Code Title of the Course Cr. Hrs (Cont. Hrs) Pre-Requisite Domain
1 MGT-401 Introduction to Digital Marketing 3(3-0), 3 – Elec. Sup. Course
2 CS-414 Final Year Project – II 4(0-4), 12 CS-413 Comp. Core
3 AI-407 Parallel & Distributed Computing 3(2-1), 5 – Dom. Core
4 GE-412 Professional Practices 3(3-0), 3 – GER
Total –   13 (9-4)