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

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