Page 294 - Postgraduate Catalog 2026-27

294 295

MEC 563 - Advanced include linear algebra, numerical ITE504 - Data Science and Big techniques. Advanced unsupervised tool use and multi-step reasoning,
Thermodynamics Master of differentiation and integration, Data Analytics and supervised (classification and through hands-on design and
Credit Hours: 3 Science in and advanced matrix operations. It Credit Hours: 3 regression) models are discussed in implementation using modern
extends to more complex topics such
depth. The course trains students on
frameworks. The course concludes
Pre-requisite: Graduate Status as Fourier Transform techniques, Pre-requisite: Graduate-Standing using Python and MATLAB machine with generative and multimodal
This course of Advanced Artificial nonlinear equations, optimization This course in Data Science and learning libraries and toolboxes deep learning applications, including
Thermodynamics presents in-depth methods, and differential equation Big Data Analytics introduces for implementing advanced AI generative models, 3D and vision–
theories of thermodynamics. A Intelligence solving, including both ordinary students to the essential techniques and machine learning systems language intelligence, and real-world
review study of the fundamental and partial differential equations. for managing, processing, and applications. deployment scenarios such as object
concepts and laws of classical The practical application of these analyzing vast and complex data MAI590 - Advanced Deep tracking, detection, deep diagnostics,
thermodynamics is presented . The MAI502 - Advanced Research techniques is demonstrated sets from various sources, including Learning Applications and federated learning. Assessment
course also includes: the application Communication through targeted labs and projects, social media, web applications, is based on proctored assignments,
of fundamental thermodynamics emphasizing real-world scenarios and IoT devices. It starts with Credit Hours: 3 a course project, a review paper,
laws to thermal systems; second-law Credit Hours: 3 such as image interpolation, energy the fundamentals of Big Data, Pre-requisite: MAI540 and a final presentation, enabling
analysis, and the concept of exergy Pre-requisite: Graduate-Standing optimization, and system analysis. covering the 5-Vs characteristics students to develop both practical
and its usefulness in optimizing The curriculum culminates with and addressing challenges in data This course introduces advanced implementation skills and critical
thermal systems; introduction to This course teaches advanced written a focus on probability, random acquisition, storage with HDFS and deep learning concepts and analysis abilities using TensorFlow
chemical thermodynamics, and and oral communication skills to variables, statistical analysis, and the NoSQL, and preprocessing. Students applications, guiding students and Keras.
phase and chemical equilibrium; graduate students through a series Central Limit Theorem, preparing will learn to implement Big Data from foundational neural network MAI621 - Computer Vision and
thermodynamics of combustion of structured assessments. Students students for advanced problem- processing with Hadoop and Apache principles to modern deep learning Image Processing
will first develop a conference-style
systems, heat transfer associated solving in research and professional Spark, explore cloud computing systems. The course begins with
with combustion reactions, and research paper in pairs, focusing on practice. platforms such as AWS, Azure, and a review of the mathematical Credit Hours: 3
equilibrium composition of the clarity, structure, and adherence to MAI605 - Artificial Intelligence GCP, and apply machine learning foundations of deep learning, Pre-requisite: MAI503
products of combustion. academic standards. Individually, to large data sets. The course followed by data-driven approaches
they will complete a review paper Ethics and the Society emphasizes practical skills in data to image classification using linear In this course students are
synthesizing up to 50 peer-reviewed Credit Hours: 3 visualization, real-time analytics, and classifiers and fully connected neural introduced to computer vision
sources to strengthen their ability Pre-requisite: Graduate-Standing the application of Big Data in fields networks. Students then study and image processing techniques,
to analyze and summarize existing like smart grids and bioinformatics. optimization, backpropagation, and focusing on both foundational and
literature. To build professional This course surveys relevant Through hands-on assignments and stability considerations. Convolutional advanced topics. The areas of study
communication skills, each student philosophical discussions and projects, students will design and Neural Networks (CNNs) are include digital image acquisition,
will design a scientific poster and questions about the fundamental develop effective Big Data solutions, covered in depth, with emphasis on representation, and color processing;
deliver a recorded oral presentation, differences between humans and preparing them for advanced roles in modern architectures and design 2-D and 3-D image transforms and
demonstrating their capacity to machines, and debates over the Big Data analytics. principles for image classification, point operations; image filtering
convey research findings effectively. moral status of AI. It offers context as well as practical implementation techniques for edge detection and
Finally, in pairs, students will prepare through the exploration of AI MAI540 - Advanced AI and through hands-on programming morphological operations; feature
a grant proposal, showcasing their technology and its approaches, Machine Learning workshops. Sequential modeling detection, image registration,
ability to articulate project objectives, focusing on machine learning and Credit Hours: 3 concepts are introduced through and contour analysis; and image
feasibility, and expected outcomes. data science. The course then uses Recurrent Neural Networks (RNNs), matching, transformations, and
Together, these assessments this context to discuss important Pre-requisite: MAI503 highlighting their role in temporal advanced local features like SIFT and
provide a comprehensive foundation ethical issues, including privacy This course builds on statistical and structured data processing. MSER. Students will use MATLAB
in academic writing, research concerns, responsibility and the inference, probability, differential The course advances to attention to implement these techniques in
dissemination, and professional delegation of decision-making, calculus, and linear algebra concepts mechanisms and transformer lab exercises and projects, applying
presentation. transparency, and bias. The course to equip students with advanced architectures, with applications their knowledge to develop solutions
also provides students with the knowledge and skills of artificial in computer vision such as object for real- world challenges such as
MAI503 - Advanced Analysis and opportunity to discuss the future detection, image segmentation, background segmentation and object
Computing of work in an AI economy and the intelligence and machine learning and visual representation analysis tracking. The course emphasizes
Credit Hours: 3 challenges for policymakers in concepts and algorithms. During using transformer-based models. practical applications in image
this course, students design and
Pre-requisite: Graduate-Standing adopting AI. Students will learn to construct an end-to-end artificial Students gain experience with vision analysis, encouraging students
analyze these issues critically and will intelligence and machine learning transformers through practical labs to work on collaborative projects,
This course covers advanced work on a team project to develop project and demonstrate mastery and structured problem-solving produce technical reports, and
analytical and computing tools an AI strategy for a hypothetical of AI methods including their exercises. A dedicated module engage in research assignments to
and techniques used in modern business, enhancing their skills in mathematical model formulations focuses on Large Language Models demonstrate their understanding.
professional practice. Students technical reporting. and search and optimization (LLMs), covering their architecture,
learn both the theory and practical techniques. Additionally, this training paradigms, and system-level
application of the covered topics course will cover different types considerations. Students explore
through MATLAB. These topics of advanced feature extraction agentic AI workflows, including

Abu Dhabi University | Postgraduate Catalog 2026 - 2027 Abu Dhabi University | Postgraduate Catalog 2026 - 2027