Teaching

This course introduces the fundamental concepts, techniques, and applications of computer graphics, including 2D and 3D object representation, geometric transformations, rendering, clipping algorithms, and animation. Students will explore both theoretical foundations and practical implementations using OpenGL and high-level programming languages.

This course introduces the fundamental concepts, techniques, and applications of Artificial Intelligence (AI) with a strong emphasis on expert systems. Topics include intelligent agents, problem-solving and search, knowledge representation and reasoning, probabilistic reasoning, machine learning techniques (including neural networks), and uncertainty handling. Throughout the course, students will explore how each AI technique contributes to the design, reasoning, and decision-making capabilities of expert systems. Ethical, social, and practical implications of AI-based expert systems are also discussed.

This course introduces fundamental data structures and algorithm analysis techniques essential for efficient problem solving. Topics include arrays, linked lists, stacks, queues, trees, graphs, hashing, sorting, and searching algorithms. Emphasis is placed on time and space efficiency, algorithm design, and comparative analysis of different data structure implementations.

This laboratory course provides hands-on experience in implementing fundamental data structures and algorithms using the C programming language. Students will develop practical skills in arrays, linked lists, stacks, queues, trees, graphs, recursion, and searching and sorting techniques through structured lab exercises and a mini project.

This course introduces the principles, techniques, and applications of digital image processing and computer vision. Students explore image enhancement, segmentation, feature extraction, object recognition, and modern deep learning approaches for visual computing.

This course introduces fundamental concepts of system analysis and design, including system components, development methodologies, feasibility analysis, project management, and system modeling techniques. Students will learn to analyze, design, and implement systems while considering organizational, technical, and ethical aspects.

This course introduces the fundamental concepts, architectures, and techniques of database management systems. It covers data modeling using ER/EER models, relational data model, SQL, normalization, transaction processing, and modern database applications including distributed systems and big data technologies.

This course introduces ethical principles, professional responsibilities, and moral reasoning in engineering and computing. Students explore cyberethics, ethical theories, professional codes of conduct, risk management, and environmental impacts through lectures, discussions, and case studies.