Page 300 - Postgraduate Catalog 2026-27

300
theoretical and hands-on practical
knowledge necessary to analyze and
understand different aspects related
to modern AI systems. Topics covered
include intelligent agents’ design,
search and optimization algorithms,
machine learning (ML), deep learning
(DL), natural language processing for publication.
(NLP), and recommender systems
(RecSys). The course utilizes variety
of learning and assessment tools
including practical labs, case-study
analysis, and research paper writing.
By completing this course, the
students will be able to analyze the
requirements for real-life AI systems,
design them using the Rational
Agent Design Framework, implement
them by applying advanced learning,
search, or optimization algorithms,
and evaluate them using different
evaluation tools and techniques
presented in the course.
Thesis Requirements
CSE 591A & B – Master’s Thesis in
Cybersecurity
Credit Hours: 9
Pre-requisite: 15 Credits
Thesis is a comprehensive integrated
project that brings together
knowledge, skills, and competencies
developed during the program.
Thesis requirements: (a) the
thesis should exhibit elements of
creativity, initiative and independent
thinking; (b) involve both knowledge
gained through coursework and
skills acquired during the conduct
of the M.Sc. thesis research; (c)
demonstrate the ability to carry out
a major piece of work according to
sound scientific and engineering
principles; (d) organize work in a
comprehensive and well-structured
report, and (e) demonstrate the
ability to defend assumptions,
methodology, and significance and
impact of work. The thesis consists
of two successive courses A and
B. In the first part (CSE591A) the
students are expected to select an
Abu Dhabi University | Postgraduate Catalog 2026 - 2027
301
advisor, write a proposal and defend
their proposal successfully. While in PhD in
the second part (CSE591B) students
are expected to complete their Intelligent
thesis work including defending it
successfully and submitting at least Systems
one original conference/journal paper
Engineering
Core Courses
DEN701 – Advanced Probability
and Stochastic Processes
Credit Hours: 3
Prerequisite: Graduate status
This course provides an advanced
and rigorous study of probability
theory and stochastic processes with
emphasis on analytical modeling
and engineering applications.
The course covers foundational
concepts, including set theory,
random experiments, conditional
probability, independence, and
Bayes’ rule, followed by discrete,
continuous, and mixed random
variables and their associated
distributions. Students study joint
and multiple random variables,
moment generating functions, and
key limit theorems such as the Law
of Large Numbers and the Central
Limit Theorem. The course also
introduces random processes,
including stationary processes,
Poisson processes, Markov chains,
and Brownian motion, with methods
for analyzing mean and correlation
functions. Emphasis is placed on
probabilistic reasoning, mathematical
rigor, and interpretation of results
to support modeling, analysis, and
decision-making under uncertainty in
engineering and scientific contexts.
DEN702 – Advanced Research
Communication
Credit Hours: 3
Prerequisite: Graduate status
This course teaches advanced written
and oral communication skills to
graduate students. It covers the as signal reconstruction, energy Through presentations, discussions,
fundamentals of research including optimization, and system analysis. and examination preparation,
methodologies, and ethics. The The students communicate their students strengthen their ability to
curriculum integrates modern digital experience using presentations and analyze research problems, evaluate
tools like LaTeX and Grammarly to research article-level reports. methodologies, communicate
enhance writing and presentation effectively in academic and
skills. The course also covers data DEN795 – Doctoral Seminar professional settings, and contribute
collection methodologies and Credit Hours: 3 to interdisciplinary engineering
statistical evaluation techniques. Key Prerequisite: DEN702 research communities.
projects include a detailed literature This course serves as the doctoral
review, developing an original
research paper, and preparing a seminar for the PhD in Intelligent Elective Courses
Systems Engineering program and
grant application to allow students to supports students in advancing
communicate complex information
effectively. Additionally, students will their dissertation research while DEN790 – Advanced Deep
developing essential scholarly and
learn to utilize academic databases Learning Applications
for research and publication, adopt professional skills. The seminar Credit Hours: 3
provides a structured environment in
academic writing techniques, and which doctoral students present their Prerequisite: Graduate status
apply proper citation methods.
The course culminates in practical research progress, engage in critical This course introduces advanced
discussions, and receive feedback
presentations, both oral and poster, deep learning concepts and
allowing students to demonstrate from peers and faculty. Through applications, guiding students
these activities, students refine their
their research findings publicly. from foundational neural network
Through these activities, students will research questions, strengthen principles to modern deep learning
gain essential skills in presenting their analytical thinking, and improve their systems. The course begins with
work in academic and professional ability to communicate complex a review of the mathematical
settings. technical ideas. foundations of deep learning,
The course is delivered in an intensive followed by data-driven approaches
DEN703 – Advanced Analysis seminar format with meetings to image classification using linear
and Computing held every two weeks. Sessions classifiers and fully connected neural
Credit Hours: 3 include research presentations, networks. Students then study
Prerequisite: Graduate status peer discussions, and professional optimization, backpropagation, and
stability considerations. Convolutional
development on conducting and
This course provides an in-depth communicating research. Early Neural Networks (CNNs) are
study of advanced analytical and sessions introduce key expectations covered in depth, with emphasis on
computational techniques utilized of doctoral research and provide modern architectures and design
in professional and research guidance on topics such as the use principles for image classification,
environments. It emphasizes both of artificial intelligence in research, as well as practical implementation
theoretical foundations and practical research design, and scholarly through hands-on programming
implementation through MATLAB, communication. workshops. Sequential modeling
covering advanced topics such as concepts are introduced through
linear algebra, numerical differ- A central component of the course Recurrent Neural Networks (RNNs),
entiation and integration, matrix is preparation for the program’s highlighting their role in temporal
operations, and Fourier Transform comprehensive examination. and structured data processing.
techniques. The course progresses to Students review core subject areas The course advances to attention
complex methods, including solving within their program, demonstrate mechanisms and transformer
nonlinear equations, optimization their understanding through written architectures, with applications
techniques, and differential and oral examinations, and develop in computer vision such as object
equations, encom-passing both the ability to synthesize advanced detection, image segmentation,
ordinary and partial differential knowledge across multiple domains. and visual representation analysis
equations. The course has a review The seminar, therefore, functions using transformer-based models.
paper, which involves a critical both as a platform for research Students gain experience with vision
analysis of ad-vanced computational exchange and as a milestone course transformers through practical labs
methods, and a project, which applies supporting doctoral progression and structured problem-solving
the covered techniques to solve toward candidacy. exercises. A dedicated module
complex real-world problems such focuses on Large Language Models
Abu Dhabi University | Postgraduate Catalog 2026 - 2027