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

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:

BS (Artificial Intelligence) Road Map

List of Courses

Computing Core Courses (49 credit hours)

Artificial Intelligence Core Courses (18 credit hours)

List of Courses Artificial Intelligence Electives (21 Credit hours)

Mathematics & Supporting Courses (12 credit hours)

Elective Supporting Courses (3 credit hours)

General Education Courses (30 credit hours)

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.