Bachelor of science in Artificial Intelligence

Vision of the Computer Science Department

To become a center of excellence in Computer Science education, research, and globalized technologies

Mission of the BS Artificial Intelligence Programme

To prepare graduates who can analyze, design, and develop effective AI solutions and contribute effectively towards society.

Programme Educational Objectives (PEOs)

 

PEO-1: Utilize knowledge to solve real-world problems by applying theory, principles, and methods of computing in general and artificial intelligence in particular.

PEO-2: Demonstrate social and ethical responsibility in professional life.

PEO-3: Manifest lifelong learning for sustained professional and personal progression.

PEO-4: Practice effective communication and teamwork skills.

Programme Learning Outcomes (PLOs)

PLO1    Academic Education: To prepare graduates as computing professionals.

PLO2    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.

PLO3    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.

PLO4    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.

PLO5    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.

PLO6    Individual and Teamwork: Function effectively as an individual and as a member or leader in diverse teams and in multi-disciplinary settings.

PLO7    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.

PLO8    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.

PLO9    Ethics: Understand and commit to professional ethics, responsibilities, and norms of professional computing practice.

PLO10 Life-long Learning: Recognize the need, and have the ability, to engage in independent learning for continual development as a computing professional.

 

Mapping of PLOs to PEOs

 

No. Programme Learning Outcomes (PLOs) PEOs
PEO-1 PEO-2 PEO-3 PEO-4
1 Academic Education
2 Knowledge for solving Computing Problems
3 Problem Analysis
4 Design/ Development of Solutions
5 Modern Tool Usage
6 Individual and Teamwork

 

7 Communication
8 Computing Professionalism and Society
9 Ethics
10 Lifelong Learning

 

Programme Eligibility Criteria

Minimum 50% marks in Intermediate (HSSC) Examination (Pre-Medical/Pre-Engg.) or equivalent qualification with Mathematics certified by IBCC.

Deficiency: For Pre-Medical students, the following two deficiency courses of mathematics will be taught during the first year.

  • Fundamentals of Mathematics I GSC 103 (3 Credit Hours)
  • Fundamentals of Mathematics II GSC 104 (3 Credit Hours) Curriculum for BS in Artificial Intelligence

The generic structure for computing degree Programme is mapped with the BS(AI) Programme in the following table.

 Generic Structure for Computing Disciplines:

 

Areas Credit Hours Courses
Computing Core 49 14
Domain Core 18 6
Domain Elective 21 7
Mathematics & Supporting Courses 12 4
Elective Supporting Courses 3 1
General Education Requirement 30 12
Total 133 44

 

BS (Artificial Intelligence) Road Map

 

SEMESTER 1
Pre- Req Course Code Course Title Theory Lab CR CR/Sem
None GSC 114 Applied Physics 2 0 2  

 

 

 

16

None GSL 114 Applied Physics Lab 0 1 1
None CSC 114 Introduction to Information &

Communication Technology

2 0 2
None CSL 114 Introduction to Information &

Communication Technology Lab

0 1 1
None ENG 101 Functional English 3 0 3
None CSC 113 Computer Programming 3 0 3
None CSL 113 Computer Programming Lab 0 1 1
None GSC 221 Discrete Mathematics 3 0 3
SEMESTER 2
Pre-

Req

Course

Code

Course Title Theory Lab CR CR/Sem
None GSC 121 Linear Algebra 3 0 3 17
CSC CSC 210 Object Oriented Programming 3 0 3

 

113
CSC

113

CSL 210 Object Oriented Programming

Lab

0 1 1
None CSC 220 Database Management Systems 3 0 3
None CSL 220 Database Management Systems

Lab

0 1 1
None GSC 110 Applied Calculus and Analytical

Geometry

3 0 3
None CEN 122 Digital Design 2 0 2
None CEL 122 Digital Design Lab 0 1 1
SEMESTER 3
Pre-Req Course

Code

Course Title Theory Lab CR CR/Sem
GSC 110 GSC 211 Multivariable Calculus 3 0 3  

 

 

 

 

17

None CEN 223 Computer Communication and

Networks

3 0 3
None CEL 223 Computer Communication and

Networks Lab

0 1 1
CSC 113 CSC 221 Data Structures and Algorithms 3 0 3
CSC 113 CSL 221 Data Structures and Algorithms Lab 0 1 1
None AIC 202 Programming for Artificial

Intelligence

2 0 2
None AIL 202 Programming for Artificial

Intelligence Lab

0 1 1
None GSC 122 Probability and Statistics 3 0 3
SEMESTER 4
Pre-Req Course Code Course Title Theory Lab CR CR/Sem
CSC 221 CSC 320 Operating Systems 3 0 3  

 

 

 

16

CSC 221 CSL 320 Operating Systems Lab 0 1 1
 

AIC 201

 

AIC 203

Knowledge Representation &

Reasoning

 

3

 

0

 

3

None AIC 301 Machine Learning 2 0 2
None AIL 301 Machine Learning Lab 0 1 1
AIC 202 AIC 201 Artificial Intelligence 3 0 3
AIC 202 AIL 201 Artificial Intelligence Lab 0 1 1
None ENG 134 Communication Skills 2 0 2

 

 

SEMESTER 5
Pre-Req Course Code Course Title Theory Lab CR CR/Sem
CEN

122

CEN 323 Computer Organization & Assembly

Language

2 0 2  

 

17

CEN 122 CEL 323 Computer Organization & Assembly Language Lab 0 1 1
AIC 301 AIC 401 Deep learning 2 0 2

 

AIC 301 AIL 401 Deep learning Lab 0 1 1
CSC 221 CSC 321 Design and Analysis of Algorithms 3 0 3
None PAK 101 Pakistan Studies 2 0 2
Elective 1 2 1 3
Elective 2 3/2 0/1 3
SEMESTER 6
Pre- Req Course Code Course Title Theory Lab CR CR/Sem
None SEN 220 Software Engineering 3 0 3  

 

 

17

None AIC 304 Computer Vision 2 0 2
None AIL 304 Computer Vision Lab 0 1 1
None ISL 101 Islamic Studies/ Ethics 2 0 2
Social Science Elective(General Education) 3 0 3
Elective 3 2 1 3
Elective 4 2 1 3
SEMESTER 7
Pre- Req Course Code Course Title Theory Lab CR CR/Sem
None FYP 400 Final Year Project I 0 3 3  

 

 

 

17

CSC 320 AIC 302 Parallel & Distributed Computing 2 0 2
CSC

320

AIL 302 Parallel & Distributed Computing

Lab

0 1 1
None ENG 320 Technical Writing & Presentation

Skills

3 0 3
None CSC 307 Professional Practices and Ethics 2 0 2
Supporting Elective (Social Sciences) 3 0 3
Elective 5 2 1 3
SEMESTER 8
Pre-Req Course Code Course Title Theory Lab CR CR/Sem
None FYP 400 Final Year Project II 0 3 3  

 

16

CEN 223 CSC 407 Information Security 3 0 3
None HSS 217 Introduction to Sociology 2 0 2
None HSS 423 Entrepreneurship 2 0 2
Elective 6 3/2 0/1 3
Elective 7 3/2 0/1 3
Total: 133

 

List of Courses

 

 

Computing Core Courses (49 credit hours)

 

Pre-

requisite

Course

Code

Course Title Lec Lab CR
None CSC 113 Computer Programming 3 0 3
None CSL 113 Computer Programming Lab 0 1 1
CSC 113 CSC 210 Object Oriented Programming 3 0 3
CSC 113 CSL 210 Object Oriented Programming Lab 0 1 1
None CSC 220 Database Management Systems 3 0 3
None CSL 220 Database Management Systems Lab 0 1 1
None CEN 122 Digital Design 2 0 2
CEL 122 Digital Design Lab 0 1 1
 

None

 

CEN 223

Computer Communication and

Networks

 

3

 

0

 

3

 

None

 

CEL 223

Computer Communication and

Networks Lab

 

0

 

1

 

1

 

CSC 113

 

CSC 221

 

Data Structures and Algorithms

 

3

 

0

 

3

CSC 113 CSL 221 Data Structures and Algorithms Lab 0 1 1
CSC 221 CSC 320 Operating Systems 3 0 3
CSC 221 CSL 320 Operating Systems Lab 0 1 1
CEN 122 CEN 323 Computer Organization & Assembly

Language

 

2

 

0

 

2

CEN 122 CEL 323 Computer Organization & Assembly Language Lab  

0

 

1

 

1

AIC 202 AIC 201 Artificial Intelligence 3 0 3
AIC 202 AIL 201 Artificial Intelligence Lab 0 1 1
CSC 221 CSC 321 Design and Analysis of Algorithms 3 0 3
None SEN 220 Software Engineering 3 0 3
None FYP 400 FYP I 0 3 3
None FYP 400 FYP II 0 3 3
CEN 223 CSC 407 Information Security 3 0 3

 

Artificial Intelligence Core Courses (18 credit hours)

 

Prerequisite Course Code Course Title Lec Lab CR
 

None

 

AIC 202

Programming for Artificial

Intelligence

 

2

 

0

 

2

 

None

 

AIL 202

Programming for Artificial

Intelligence Lab

 

0

 

1

 

1

None AIC 301 Machine Learning 2 0 2
None AIL 301 Machine Learning Lab 0 1 1
 

AIC 201

 

AIC 203

Knowledge Representation &

Reasoning

 

3

 

0

 

3

AIC 301 AIC 401 Deep Learning 2 0 2

 

AIC 301 AIL 401 Deep Learning Lab 0 1 1
None AIC 304 Computer Vision 2 0 2
None AIL 304 Computer Vision Lab 0 1 1
CSC 320 AIC 302 Parallel & Distributed Computing 2 0 2
CSC 320 AIL 302 Parallel & Distributed Computing Lab 0 1 1

 

List of Courses Artificial Intelligence Electives (21 Credit hours)

Prerequisite Course Code Course Title Lec Lab CR
GSC 122 AIC 305 Advance Statistics 3 0 3
None CSC 315 Theory of Automata 3 0 3
None CSC 452 Data Mining 3 0 3
None AIC 306 Speech Processing 3 0 3
None AIC 402 Reinforcement Learning 3 0 3
None AIC 403 Fuzzy Systems 2 1 3
None AIC 307 Evolutionary Computing 3 0 3
None AIC 308 Agent-Based Modeling 3 0 3
None CEN 459 Robotics 2 1 3
None ITC 412 Introduction to Cyber Security 2 1 3
None AIC 442 Natural Language Processing 2 1 3
None AIC 410 Virtual and Augmented Reality 2 1 3
None AIC 411 HCI & Computer Graphics 2 1 3
None AIC 310 Swarm Intelligence 2 1 3
None CSC 400 Quantum Computing 2 1 3
None AIC 377 Game Artificial Intelligence 2 1 3

 

Mathematics & Supporting Courses (12 credit hours)

 

Prerequisite Course

Code

Course Title Lec Lab CR
None GSC 121 Linear Algebra 3 0 3
GSC 110 GSC 211 Multivariable Calculus 3 0 3
None GSC 122 Probability and Statistics 3 0 3
ENG 320 Technical Writing and Presentation Skills 3 0 3

 

Elective Supporting Courses (3 credit hours)

 

Prerequisite Course

Code

Course Title Lec Lab CR
None MKT 110 Principles of Marketing 3 0 3
None FIN 201 Fundamentals of Finance 3 0 3
None MGT 111 Principles of Management 3 0 3
None MGT 242 Organizational Theory and Behavior 3 0 3

 

General Education Courses (30 credit hours)

 

Prerequisite Course

Code

Course Title Lec Lab CR
None GSC 114 Applied Physics 2 0 2
None GSL 114 Applied Physics Lab 0 1 1
None CSC 114 Introduction to Information &

Communication Technology

2 0 2
None CSL 114 Introduction to Information &

Communication Technology Lab

0 1 1
None ENG 101 Functional English 3 0 3
None GSC 221 Discrete Mathematics 3 0 3
None GSC 110 Applied Calculus and Analytical Geometry 3 0 3
None ENG 134 Communication Skills 2 0 2
None PAK 101 Pakistan Studies 2 0 2
None ISL 101 Islamic Studies/ Ethics 2 0 2
None CSC 307 Professional Practices and Ethics 2 0 2
None HSS 423 Entrepreneurship 2 0 2
None HSS 217 Introduction to Sociology 2 0 2
Social Sciences Electives
None HSS 107 Introduction to Psychology 3 0 3
None HSS 115 Introduction to Media studies 3 0 3
None BES 103 Critical Thinking 3 0 3

 

 

Course Name: Quantum Computing

Credit Hours: 3 (2+1)

Contact Hours: 2+3 Pre-requisites: None Course Code: AIC 400

Content: Introduction to the principles and practice of quantum computing, with an emphasis on the perspective of computer scientists, Introduction to quantum mechanics, Basic principles of quantum mechanics: superposition, entanglement, and measurement, Quantum gates and circuits: Quantum gates and circuits: the Hadamard gate, Pauli gates, and CNOT gate, Basic quantum circuits such as the circuit for Grover’s search algorithm, platforms for developing and exploring quantum computing algorithms, Quantum computing Libraries: Qiskit for mapping computer science datasets in to quantum realm.

Course Name: Virtual and Augmented Reality

Credit Hours: 3 (2+1)

Contact Hours: 2+3 Pre-requisites: None Course Code: AIC 410

Content: Introduction to Virtual Reality and Augmented Reality, Design for AR/VR, History of AR/VR, Basics of Computer Vision and Multimodal Interaction, Business of AR/VR, Basics of Human Perception, Depth Perception and Projection, AR/VR Displays, Graphics Pipeline, Lighting, Shading, and Effects, Scene Graphs and Acceleration, 2D & 3D Tracking, Interaction, Principles and Problems, Algorithms for AR and VR, Human Robot Interaction using AR/VR systems, Eye and Displays (2D),

 

Head Up and Head Mounted Systems in Automotive and Aviation Domains, Virtual Reality System development in Unity

Course Name: Game Artificial Intelligence

Credit Hours: 3 (2+1)

Contact Hours: 2+3 Pre-requisites: None Course Code: AIC 377

Content: Introduction to Game AI, Basic concepts and methods of artificial intelligence and their applications in computer games, Artificial stupidity, intelligent mistakes, models of game AI, data structures, representations, complexity, and constraints, Agent Movement Steering Behaviors, Coordinated Movement, character movement, pathfinding, decision making (behavior trees), Tactical & Strategic AI, Scripting language for game AI, navigation, planning, and learning in the game, AI for human-like characters, Decision Making: State Machines in gaming, Rule-based systems, Decision trees, Optimizing the behavior of NPCs, Procedural Content Generation, GANs, Pattern recognition and agents in the game, Learning algorithms, training agents with find-grained control, Playing games with deep RL, General video game AI (GVGAI), GameAI platforms

Course Name: Swarm Intelligence

Credit Hours: 3 (2+1)

Contact Hours: 2+3 Pre-requisites: None Course Code: AIC 310

Content: Agent-based modeling: Bottom-up modeling method, individual agents, System theory and complex systems, multi-agent systems, Behavioral swarm intelligence: Modeling flocking behavior, Boids model, flocking behavior applications, such as agents queuing and homing, Computational swarm intelligence (CSI): Optimization theory and multi-objective optimization, Particle swarm optimization (PSO), Ant colony optimization (ACO), Bees colony algorithm (BCO), Bats algorithm, Selected applications: Different selected applications where the students can apply the swarm intelligence algorithms to solve real problems, such as: Multi-robot path planning, Task scheduling.