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MAI633 - Advanced Internet of algorithms and their applications in using YOLO, object tracking, Students work on utilizing AI services MAI691B - Thesis in Artificial practice using hands-on laboratory
Intelligent Things the stochastic image segmentations segmentation, and filtering methods to solve complex problems from Intelligence experiences covering Integration of
Credit Hours: 3 and restoration. Students will than used in robotics. Later modules different domains. Credit Hours: 6 Player vs. Player GUI and Unbeatable
introduce advanced autonomy
Tic Tac Toe using Minimax Algorithm,
explore the geometrical modeling
Pre-requisite: MAI540 techniques such as snakes, active concepts including behavior cloning, MAI606 - Artificial Intelligence Pre-requisite: MAI691A GUI Implementation of Connect
This course uses 8-bit contours, and segmentation, level path-tracking methods such as pure in Education This course is amendatory 4 Game with Difficulty Levels, and
microcontrollers in addition to sets methods, and total variation- pursuit, and the robotics software Credit Hours: 3 requirement for all Masters’ students. Family Tree Representation and
Linux-based 32-bit microprocessors based image segmentation. architecture using ROS. Students Pre-requisite: Graduate Standing The thesis consists of two successive Querying using Prolog. The course
to design and implement advanced Throughout this course the students gain practical experience with robot courses A and B. The purpose of has a project. In this project, students
AI and computer-vision-powered will build their knowledge about the modeling, coordinate frames, sensor This course explores advanced the thesis is to complete individually work teams using PyCharm, MATLAB,
embedded Linux applications. image restoration (PDE-based image data flow, mapping, localization, applications artificial intelligence a research capstone project in the and Prolog to design a AI-based
During this course, students will get restoration, variation-based image and navigation within simulated and machine learning in education. field of electrical engineering to Solution to the Knight Tour Problem
introduced to OpenCV, state charts restoration) and image registration robotic environments. The course Students design and use artificial culminating the student experience and communicate their experience
(with concurrency and composite (global transformation functions, concludes with emerging approaches intelligence tools to enhance and validate them as Masters using presentations and reports.
states) for modeling and design of local transformation functions, and in robotics using foundation models teaching, learning, and assessment. Practitioners. Moreover, the student Students will also analyze the impact
AI-powered IoT applications, as well similarity measures) techniques and multimodal AI, including vision- The course covers different advanced should implement and use their of Artificial Intelligence (AI) on society,
as machine learning and computer for the advanced computational language and vision-language-action use cases including students’ gained experience to complete and the environment, and the economy.
vision implementations running analysis and design of Computer- systems that enable higher-level emotion and cheating behavior publish research work. In the first MAI201PC - Programming for AI
on the Raspberry Pi such as Scikit Assisted Diagnostic (CAD) systems reasoning and decision making in detection, student performance part (ECE691A) the students are
Learn, Yolo, and TensorFlow Lite. for medical problems. The emphasis autonomous robots. Throughout prediction, educational content expected to select and advisor, write Credit Hours: 3
They also learn how to integrate is on understanding the underlying the course, students implement recommendation, authorship a proposal and defend their proposal Pre-requisite:
the Arduino and Raspberry Pi using mathematics in a practical sense and extend real robotic algorithms, attribution, AI-powered educational successfully. While in the second part This course introduces students
the Firmata protocol. Advanced with the hand-on experience of the evaluate system performance, mobile apps, and AR and VR (ECE691B) students are expected
interfacing techniques are students with associated labs and and document their work in in education. The course uses to get to complete their thesis work to concepts, techniques, and
discussed theoretically in lectures homework assignments. technical reports and a final project. Python and MATLAB for hands-on including defending it successfully applications of Machine Learning
(ML). Topics covered include
and implemented practically in the MAI675 - Autonomous Vehicles: The emphasis is on integrating experiences. and submit at least one original
labs and the project. At the end of Drones and Self-Driving Cars perception, control, and intelligent MAI691A - Thesis in Artificial journal paper or multiple quality data structures, training and
testing, performance assessment,
the course, students are required decision making into complete Intelligence conference papers for publication.
to design and build AI embedded Credit Hours: 3 autonomous systems. classification, and regression.
system with optimized latency and Pre-requisite: MAI621 MAI623 - Advanced AI- Credit Hours: 3 MAI635 - Special Topics in Students connect theoretical
concepts learned in the course to
power by applying the knowledge Powered Mobile Application Pre-requisite: 15 Credit Hours Artificial Intelligence
gained throughout the course. They The objective of this course is to Development This course is amendatory Credit Hours: 3 practice using hands-on laboratory
experiences covering Regression
will learn the limitations of embedded provide students with advanced, Pre-requisite: Graduate Standing
systems and the need for machine hands-on experience in autonomous Credit Hours: 3 requirement for all Masters’ students. from scratch using numpy,
The thesis consists of two successive
learning algorithms with small robotics systems that integrate Pre-requisite: Graduate Standing courses A and B. The purpose of This course will include advanced Classification comparison using
sklearn, Image classification using
memory footprints and processing sensing, perception, control, and the thesis is to complete individually topics of contemporary interest in
needs. Lectures and labs will be modern AI techniques. Through a This course provides students with a research capstone project in the selected areas of artificial intelligence Keras and pytorch. The course has
a project. In this project, students
used to ensure that the concepts sequence of guided laboratories and an advanced hands-on experience Engineering. Particular topics vary
of embedded systems for Artificial a major project, students develop of recent advancements in mobile field of electrical engineering to from term to term depending on work teams using PyCharm, Scikit
culminating the student experience
Intelligence are understood. and analyze autonomous vehicle and Internet computing technologies the interests of the students and the Libraries, and PyTorch to design a
capabilities using both classical and their applications. Students and validate them as Masters specialties of the instructor. deep learning image classifier and
MAI642 - Artificial Intelligence robotics methods and contemporary design multiple advanced mobile and Practitioners. Moreover, the student communicate their experience using
in Medicine machine learning approaches. The Progressive Web Applications (PWAs). should implement and use their MAI202PC - Introductory presentations and reports.
Credit Hours: 3 course begins with foundations The course begins by covering gained experience to complete and Artificial Intelligence
publish research work. In the first
in system design and embedded
Pre-requisite: MAI503 interaction, including state-based technologies including Android part (ECE691A) the students are Credit Hours: 3
MLKit, CameraX, and Ionic and use
This course covers the computations modeling, signal communication, them to develop AI-powered features expected to select and advisor, write Pre-requisite:
methods for the medical image and sensor-driven behaviors such in addition to navigation, persistence, a proposal and defend their proposal This course introduces students
analysis. This course covers the as line following. It then progresses and localization. Students also successfully. While in the second part to concepts, techniques, and
theory of stochastic and geometric to perception and control for experience integration of advanced (ECE691B) students are expected applications of Artificial Intelligence
models of medical imaging. The autonomous navigation, including services for machine learning and to get to complete their thesis work (AI). Topics covered include Artificial
students will be introduced to the PID control, computer vision for computer vision by off-loading, including defending it successfully Intelligence, Problem-solving, Deep
basics of the stochastic image lane detection, and integration of external API access, and on-device and submit at least one original Learning, Reinforcement Learning,
modeling techniques including vision with control systems. Students implementation. Students also learn journal paper or multiple quality and Natural Language Processing.
intensity models, spatial interaction further explore modern perception how to externally train and import conference papers for publication._. Students connect theoretical
models, stochastic optimization techniques such as object detection AI models to mobile applications. concepts learned in the course to
Abu Dhabi University | Postgraduate Catalog 2026 - 2027 Abu Dhabi University | Postgraduate Catalog 2026 - 2027