0103612

Advanced Research Methods

This course is designed to equip postgraduate engineering students with advanced research skills and methodologies necessary for conducting high-quality research in engineering disciplines. The course covers a wide range of topics, from research design and data analysis to ethics and effective communication of research findings. This course will introduce students to more complex study designs and higher-level critical appraisal. Several research methods will be explored in depth with consideration of both quantitative, qualitative and mixed methods designs. During the course, the students will be guided and supported to develop the skills required by professional researchers to disseminate research plans and findings in a range of contexts. The course will equip the students with knowledge and skills to evaluate the utility of different research designs to address specific research questions.

0114610

Mathematics and Statistics for AI

This course is designed to equip graduate students with the foundational mathematical and statistical concepts essential for advanced studies in artificial intelligence and machine learning. Topics will include linear algebra, calculus, probability theory, and statistical inference, emphasizing their applications in AI algorithms. Through a blend of theoretical concepts and practical exercises, students will develop skills to analyze data and model complex systems effectively. This course serves as a prerequisite for advanced AI and machine learning courses, ensuring students have the necessary skills for success in these areas.   

0114611

Advanced Data Mining

The course covers the theory and methods of data mining. It aims to equip students with the necessary skills and knowledge to develop models using data mining techniques that include classification, association, outlier, prediction, clustering and reasoning. Web mining, text mining, and pattern mining approaches are also covered. The course goes through the full cycle of data mining starting from collecting the data all the way to evaluating and interpretation the results. In addition, contemporary methods for web data mining are presented such as web data warehousing, web personalization and recommender systems.

0114612

Machine & Deep Learning

This course covers the theory and practice of machine learning. It explores topics such as feature engineering and data pre-processing. Several popular supervised and unsupervised learning algorithms are covered: linear regression, decision trees, k-nearest neighbor, Bayesian learning, support vector machines, neural networks and k-means. In addition, this course provides a comprehensive introduction to Artificial Neural Networks (ANNs) and Deep Learning (DL). Students will explore foundational concepts, architectures, and algorithms that underpin modern neural networks and deep learning systems. The course emphasizes both theoretical understanding and practical implementation, with hands-on exercises using Python and popular deep learning libraries such as TensorFlow and PyTorch.

0114613

Advanced Artificial Intelligence

The course covers the theory and methods of data mining. It aims to equip students with the necessary skills and knowledge to develop models using data mining techniques that include classification, association, outlier, prediction, clustering and reasoning. Web mining, text mining, and pattern mining approaches are also covered. The course goes through the full cycle of data mining starting from collecting the data all the way to evaluating and interpretation the results. In addition, contemporary methods for web data mining are presented such as web data warehousing, web personalization and recommender systems.

0114614

Robotics

This course introduces the fundamental principles of robotics, including kinematics, dynamics, perception, and control. It explores robotic architectures, machine learning integration, and AI-driven decision-making for autonomous robots. Topics cover robotic sensing, actuation, motion planning, and reinforcement learning applied to robotic applications. The course includes theoretical foundations as well as practical implementation through simulations and real-world case studies.

0114690

Master's Thesis (1)

This course enables the student to demonstrate the understanding and possession of knowledge and skills acquired during the course of the program. This course will be performed by each student individually by choosing a real-world problem in any domain related to the program. The student will be expected to conduct the study using theoretical research methodologies and experimentation. The work will be presented in the form of a thesis which will detail the identification of a real-world problem, implementation of research methodologies, a detailed literature review and implementation of stat-of-the-art AI design and implementation methodologies.

0114691

Master’s Thesis (2)

This course enables the student to demonstrate the understanding and possession of knowledge and skills acquired during the course of the program. This course will be performed by each student individually by choosing a real-world problem in any domain related to the program. The student will be expected to conduct the study using theoretical research methodologies and experimentation. The work will be presented in the form of a thesis which will detail the identification of a real-world problem, implementation of research methodologies, a detailed literature review and implementation of stat-of-the-art AI design and implementation methodologies.

0114615

Natural Language Processing

This course is designed to introduce students to the fundamental concepts and ideas in natural language processing (NLP), and to get them up to speed with current research in the area. It covers syntactic, semantic and discourse processing models, emphasizing machine learning or corpus-based methods and algorithms. It also covers applications of these methods and models in syntactic parsing, information extraction, statistical machine translation, dialogue systems, and summarization. Research papers of high impact published recently in the literature will be provided as reading assignments.

0114616

Computer Vision

This course explores fundamental and advanced topics in computer vision, including image processing, feature extraction, object detection, and deep learning for vision applications. Students will learn about edge detection, segmentation, convolutional neural networks (CNNs), and real-world applications such as facial recognition and autonomous systems.

0114617

Internet of Things

This course provides an in-depth exploration of the Internet of Things (IoT) at a master’s level, covering advanced architectures, security challenges, data analytics, and real-world applications. The focus is on designing and implementing scalable IoT solutions while addressing security and ethical concerns. Case studies and projects emphasize IoT’s role in smart cities, healthcare, industrial automation, and AI integration.

0114618

AI in Healthcare

This course explores the intersection of artificial intelligence (AI) and healthcare, covering machine learning applications in medical diagnosis, predictive analytics, medical imaging, drug discovery, and personalized medicine. Students will analyze ethical and regulatory challenges and work on research-driven projects in AI-enabled healthcare solutions.

0114619

Speech Recognition

This course provides an in-depth study of speech recognition technologies, covering fundamental theories, signal processing techniques, feature extraction, deep learning models, and practical applications. Students will explore automatic speech recognition (ASR) architectures, natural language processing (NLP) integration, and real-world deployments in virtual assistants, healthcare, and security

0114620

Smart Cities

This course explores the application of AI and data-driven technologies in building smart cities. Topics include IoT-enabled urban infrastructure, intelligent transportation, energy management, smart governance, and AI-driven urban analytics. Students will engage in research-based projects and case studies to evaluate emerging smart city innovations and their impact on sustainability and urban living.