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preparation, model development, development environments to refine, a Autonomous Surveillance Robot AIN430 - Machine Learning in AIN482 - Natural Language GUI Implementation of Connect
performance evaluation, and system finalize, and optimize their AI models using ROS and Computer Vision and Medicine Processing 4 Game with Difficulty Levels, and
integration in various AI domains and systems. This process may involve communicate their experience using Family Tree Representation and
such as machine learning, natural rigorous testing, hyperparameter presentations and reports. Credit Hours: 3 Credit Hours: 3 Querying using Prolog. The course
language processing, and computer tuning, troubleshooting, and iterative Prerequisite: CSC201, CEN320 Prerequisite: AIN410 has a project. In this project, students
vision. Students connect theoretical improvements to ensure the project AIN442 - Time Series Analysis This course studies medical imaging This course builds on the concepts work teams using PyCharm, MATLAB,
concepts learned in the program to meets the defined objectives and Credit Hours: 3 and how it is used to build computer- and skills acquired in AIN410 Deep and Prolog to design a AI-based
practice through the development of adheres to ethical and performance Prerequisite: CEN320, AIN310 aided diagnosis systems. It introduces Learning. It introduces students Solution to the Knight Tour Problem
their research proposals. The core of standards. Students will also gain students to functional feature to concepts, techniques, and and communicate their experience
the course is research. In this phase, experience in delivering professional As the big data ecosystem there are extraction, connectivity feature applications of Machine Learning (ML) using presentations and reports.
students work on literature reviews presentations and composing unprecedented amounts of high- extraction, shape feature extraction, and Natural Language Processing Students will also analyze the impact
and develop a proposal for the AI detailed written reports, focusing on quality time series data available. and appearance feature extractions. (NLP). Topics covered include NLP, of Artificial Intelligence (AI) on society,
project they will work on in Capstone the design enhancements, ethical Time series are uniquely interesting Students work on a project to use text vectorization, text, context, and the environment, and the economy.
2. This process involves integrating considerations, and challenges because they can address questions the extracted features with classifiers sentiment analysis . Students connect
various AI engineering skills and overcome during the project’s second of causality trends and the likelihood and develop a automatic diagnosis theoretical concepts learned in the AIN425 - Internet of AI-powered
knowledge acquired throughout the phase. Furthermore, students will of future outcomes. In this course system. course to practice using hands-on Things
program. Students communicate deepen their understanding of the students learn tackle problems laboratory experiences covering Credit Hours: 3
their research process, challenges the societal, environmental, and relating to obtaining cleaning AIN443 - Generative Deep Language Identification using Prerequisite: AIN325
encountered, and project outcomes economic implications of their AI simulating and storing time series Learning SkLearn, Command Line Utility for
using presentations and detailed solutions, facilitating a comprehensive data. The students will explore variety Positive or Negative Text Classification This course builds on topics covered
written reports. Extending the course understanding of their role and of modeling techniques that can be Credit Hours: 3 , and Sentiment Analysis from in AIN325 in porting AI an ML on
over two semesters gives students responsibilities as AI engineers. The used for time series analysis. Python Prerequisite: AIN310 Movie Reviews using SkLearn. The resource limited devices. It continues
time to research the topic, explore two-semester structure enables language will be used int his course This course builds on the concepts course has a project. In this project, to use 8-bit microcontrollers in
innovative AI methodologies, and students to fully realize their projects, to cover various concepts. The course and skills acquired in AIN310 Machine students work teams using PyCharm addition to Linux-based 32-bit
develop a prototype. This leads to providing a unique opportunity to further covers how to apply more Learning and Pattern Recognition. and TensorFlow to design a New microprocessors to design and
a well-rounded learning experience experience the complete life cycle recently developed methods such as It introduces students to concepts, Articles Topics Classification Model implement various AI and computer-
that encapsulates the complexities of a complex AI engineering project, machine learning and neural network techniques, and applications of and communicate their experience vision powered embedded Linux
and ethical considerations of working from initial conceptualization to final to time series data highlighting generative modeling and deep using presentations and reports. applications. During this course,
on large-scale, real-world AI projects. implementation and evaluation. the challenges of data processing learning. Topics covered include Students will also analyze the impact students will get introduced to
and data layout when time series Generative Modeling, Variational Python programming, OpenCV,
AIN452 - Artificial Intelligence EEN366 - Introduction to data is used for fitting models that of Machine Learning (ML) and state charts (with concurrency and
Engineering Design Project II Robotics are not inherently time aware such Autoencoders, Generative Adversarial Natural Language Processing (NLP) composite states) for modeling and
Networks, Text-to-Image Generation,
as decision trees. Furthermore, on society, the environment, and the design of IoT applications, as well
Credit Hours: 2 Credit Hours: 3 and Autoregressive Models. Students economy.
Prerequisite: AIN451 Prerequisite: EEN365 accuracy metrics and performance connect theoretical concepts learned as machine learning and computer
considerations are discussed. Some in the course to practice using hands- AIN305 - Artificial Intelligence vision implementations running on
This course builds on the concepts, This course builds on the concepts case studies from healthcare and on laboratory experiences covering for Engineers the Raspberry Pi such as Scikit Learn,
skills, and progress acquired and and skills acquired in EEN365 finance will be included towards the Generative Modeling Fundamentals, Yolo, and TensorFlow Lite. They also
achieved in AIEN451 Artificial Control Systems. It introduces end. Image Generation with GANs, and Credit Hours: 3 learn how to integrate the Arduino
Intelligence Engineering Design students to concepts, techniques, Text Generation with Autoregressive Prerequisite: CSC201, COE101 and Raspberry Pi using the Firmata
Project I. It continues to engage and applications of Robotics and AIN483 - Audio Processing for AI Models. The course has a project. This course builds on the concepts protocol. Advanced interfacing
students in concepts, techniques, Automation. Topics covered include Applications In this project, students work teams and skills acquired in CSC201 techniques are discussed theoretically
and applications of comprehensive robotics, sensors, actuators, Robotics Credit Hours: 3 using MATLAB and PyCharm to design Computer Programming I and in lectures and implemented
project design and development Operating Systems (ROS), and PID Prerequisite: CEN320, AIN310 a Synthetic Data Generator and COE101 Introductory to Artificial practically in the labs and the project.
within Artificial Intelligence Control. Students connect theoretical communicate their experience using Intelligence. It introduces students At the end of the course, students
Engineering. This course extends concepts learned in the course to This course introduces students presentations and reports. Students to concepts, techniques, and are required to design and build AI
the topics covered in the first practice using hands-on laboratory to audio signal processing and its will also analyze the impact of deep applications of Artificial Intelligence embedded system with optimized
part, including advanced problem experiences covering Robotics applications. Audio processing is learning and generative AI on society, (AI). Topics covered include Artificial latency and power by applying the
definition, AI system design, data Simulation on ROS, Gazebo, and important in many AI and Machine the environment, and the economy. Intelligence, Problem- solving, Deep knowledge gained throughout the
modeling, algorithm development, RViz, Setting up ROS on Raspberry Learning applications. Topics covered Learning, Reinforcement Learning, course.
performance evaluation, and system Pi and Controlling Actuators, and include audio signals, transforms, and Natural Language Processing.
integration. The centerpiece of the Reading Sensor Data and Controlling audio feature extraction, audio signal Students connect theoretical
course remains the design project. the Robot with ROS and OpenCV. classification and segmentation, concepts learned in the course to
Students continue their work from The course has a project. In this denoising, and temporal modeling. practice using hands-on laboratory
Artificial Intelligence Engineering project, students work teams using experiences covering Integration of
Design Project I, utilizing various PyCharm, MATLAB, ROS, Raspberry Player vs. Player GUI and Unbeatable
AI frameworks, software tools, and Pi, Sensors, and Actuators to design Tic Tac Toe using Minimax Algorithm,

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