Academic Master's Degree
Master's Degree in Artificial Intelligence
The Master's Degree in Artificial Intelligence at the College of International Justice (CI Justice) is an advanced academic and engineering programme designed specifically to elevate the technical and algorithmic competence of engineers and specialists working...
Duration2 years
Credit hours60
DegreeMaster's Degree
Language of studyArabic
Terms4
About the program
The Master's Degree in Artificial Intelligence at the College of International Justice (CI Justice) is an advanced academic and engineering programme designed specifically to elevate the technical and algorithmic competence of engineers and specialists working in the development of intelligent systems, advanced machine learning, deep learning, and generative AI models in major organizations and vital sectors. The programme focuses on complex intelligent challenges, diving into the analysis and design of deep neural networks, natural language processing (NLP), advanced computer vision, algorithmic governance, and regulations governing the use and development of responsible artificial intelligence.The programme spans four academic semesters (two academic years). Students study 8 advanced courses (5 compulsory and 3 elective) distributed across the first and second semesters, followed by practical application concurrently with completing a Master's Thesis during the third and fourth semesters. The programme focuses on complex algorithmic and engineering issues—beginning with research methodologies and advanced data analysis, moving through deep learning and large language models (LLMs), and culminating in AI governance and ethics. It is distinguished by a teaching methodology relying on research seminars and advanced computing laboratories equipped with graphical processing units (GPUs), fostering critical technical thinking, algorithmic design, and smart policy formulation.
Vision
To be the globally preeminent academic and professional destination in preparing leading artificial intelligence scientists and engineers capable of innovating reliable smart systems, guiding smart digital transformation, and enriching technical research with original contributions serving humanity with high responsibility and ethics.
Mission
To deliver an advanced postgraduate programme that integrates solid mathematical and algorithmic foundations with advanced engineering applications of artificial intelligence, through an interactive educational environment based on model building and supercomputing laboratories, to qualify cadres possessing high competence to design and build safe and responsible smart solutions ethically and legally.
Objectives
- Mathematical and Algorithmic Depth: Equip scholars with an advanced critical understanding of advanced mathematics, statistics, and machine learning/deep learning algorithms.
- Developing Rigorous Research Faculty: Qualify researchers to produce original scientific theses and technical policy papers addressing contemporary AI issues and contributing to algorithmic knowledge development.
- Mastery of Language Models and Computer Vision: Build high competence in designing and training large language models (LLMs), computer vision systems, and big data processing.
- Comprehension of Technical Governance and Ethics: Develop capacities in designing and applying AI governance frameworks, ensuring algorithmic compliance and regulation.
- Handling Advanced Smart Applications: Qualify graduates to develop autonomous systems, agentic AI, and smart applications in vital sectors (such as justice and business).
Educational philosophy
The College of International Justice adopts the philosophy of "Algorithmically-Rigorous & Responsibly-Governed AI." This philosophy transcends superficial programming understanding to reach deep mathematical and engineering analysis of models, deconstructing deep learning mechanics, and evaluating the efficiency and reliability of smart systems.This philosophy is embodied in research seminars requiring students to critically read peer-reviewed papers and strategic technical reports. The programme also strongly focuses on practical application in advanced computing laboratories and training models on supercomputers, while providing a flexible (distance) educational environment catering to the needs of practitioners and engineers and supporting directed self-learning.
Learning outcomes
- Knowledge and Understanding: Comprehending the mathematical and algorithmic frameworks of advanced neural networks, comprehensive awareness of large language model training mechanisms (LLMs), and AI governance.
- Cognitive Skills: The ability to critically analyze smart model performance and detect algorithmic bias, proper characterization of programming architectures, and foresight of future trends in automation science.
- Practical Skills: Mastery of designing, training, and fine-tuning deep learning models, developing generative AI applications, and managing MLOps lifecycles.
Career prospects
Career Horizons: The programme qualifies graduates to hold prestigious leadership and advisory positions such as Deep Learning Engineer, Generative AI Engineer, Lead Researcher in Language Model Systems, and AI Governance and Technical Policy Advisor in major corporations and sovereign bodies.
Curriculum
Advanced Computer Vision and Video Analytics 4 credit hours · MAI505Reinforcement Learning and Autonomous Control Systems (Elective) 4 credit hours · MAI506Machine Learning Operations (MLOps) (Elective) 4 credit hours · MAI507Advanced Natural Language Processing and Sentiment Analysis (Elective) 4 credit hours · MAI508Artificial Intelligence in Justice and Law (Legal AI) (Elective) 4 credit hours · MAI509AI Model Security and Counter-Attacks (Elective) 4 credit hours · MAI510
Frequently asked questions
How does the mechanism for studying the eight courses work? What is the role of Seminars?
Master's studies at our college rely on directed research methodology and deep analysis of smart theories and models, not rote learning. The student studies 8 courses. Each course is conducted through "research seminars" relying on multiple references and recent international working papers according to scientific research standards. Studying a single course takes a minimum of 4 weeks, and may take longer depending on the student's ability and pace in completing the required analyses. After completion, a competency test is held for the course, and upon passing it, the student begins the next course in the same manner.
Is there flexibility in finishing the first-year courses if I cannot complete them on time?
Yes, the system fully considers the circumstances and professional commitments of the learners, especially AI engineers and researchers. The student has the right to extend the study period for first-year courses for an additional period not exceeding a maximum of a second year, giving them sufficient flexibility to deeply absorb complex theories and advanced mathematics without undue academic pressure.
What is the nature of the "Practical Application" (Virtual Course) training? Does it require on-site attendance?
The practical training in the courses (Practical Application 1 & 2) is a "virtual" course that does not require on-site attendance but aims to refine your leadership and academic skills in knowledge transfer and simplifying complex mathematical and algorithmic concepts. You will be asked to restructure an undergraduate course into 12-14 lectures (distributed over the 3rd and 4th semesters), providing a written summary (Word) and recording a voice-over presentation (PowerPoint) for each lecture (lasting 10-20 minutes). This training places you in the role of a trained technical expert.
When can I start the Master's thesis? What are the requirements for the Jury and approval to defend it?
Thesis preparation (Master's Thesis 1 & 2) effectively begins concurrently with the start of practical training in the third semester, and takes from 9 months (minimum) up to two years (extendable exceptionally for a third year, provided total enrollment does not exceed 4 years). The supervisor submits a semi-annual progress report.To defend: The supervisor must submit a report on the thesis's validity for defense (evaluating your performance out of 140 marks) and propose names for the Jury committee, whose validity period is 6 months. At least 15 days must pass after the committee's approval before the defense. Most importantly: You must obtain at least 70% from both the supervisor's evaluation (out of 140) and the Jury members (in their individual reports out of 100) before being allowed to defend. Post-defense, the committee submits a final collective grade report.
What services support me as a distance learning Master's student at the college throughout my studies?
As a student at the College of International Justice, you will enjoy continuous support including:An Academic Tutor for each module: Evaluates your assignments and practical training (TMAs), provides guidance in using programming environments (PyTorch, TensorFlow), and directs you to accredited research sources and databases.e-Library: Comprehensive access to high-quality artificial intelligence and research databases and resources, and other university libraries.Student Support Teams and Help Centre: To answer general questions and support your study skills via an online help center.IT Support: Providing technical support and free access to Microsoft Office 365.Flexible Learning Environment: Includes discussion forums for collaborative work, a weekly study planner to schedule tasks, a tutoring booking system, and interactive Webinars.
Admission requirements
- Holding a Bachelor's degree (Licence) in Artificial Intelligence, Computer Science, Computer Engineering, Information Technology, Mathematics, or a related field from a recognized university.
- Appropriate language proficiency (programming and handling advanced technical terminology) to comprehend in-depth engineering references, analyze technical reports, and prepare scientific research.
- The ability to commit to the intensive study system based on research seminars, computing labs, directed self-learning, and completing programming tasks on time.
- Applicants from non-technical backgrounds may be required to pass foundational prerequisite courses in programming and mathematics as a condition for admission.
Tuition & fees
Application fee$145.00
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Apply to this programIn-depth Scientific Research Methodology & Algorithmic Analysis
MAI501 4 credit hours Intermediate
•Academic Description: Serving as the cornerstone for building master-level research and analytical capabilities in artificial intelligence, this is the first course students take. It covers foundational philosophical and logical methodologies in technical and algorithmic research, transitioning the student to rigorous practical application using empirical data analysis tools and programmatic hypothesis testing. The student trains intensively in using analytical models to evaluate algorithm performance, analyze system complexity, and apply empirical research techniques in developing smart models. The course also emphasizes practical and academic skills for drafting rigorous technical research papers and algorithmic Policy Papers, starting from selecting an original research topic, precisely formulating the technical problem and hypotheses, and gathering data from experimental environments, culminating in intensive practical training on preparing and drafting Thesis Proposals according to global academic standards. Students learn how to formulate developmental recommendations based on mathematical and empirical analysis. The course is studied through research seminars and programming labs according to scientific research methodology.
Advanced Deep Learning and Neural Network Engineering
MAI502 4 credit hours Intermediate
•Academic Description: This course represents an advanced foundational and critical study of deep neural network engineering and complex learning techniques. It transcends basic concepts to deeply analyze modern architectural structures: Deep Convolutional Neural Networks (CNNs), Recurrent Neural Networks and Long Short-Term Memory (RNN/LSTM), and Transformer architectures forming the backbone of modern models. It studies deeply mathematical optimization algorithms for networks (Optimizers), hyperparameter tuning, and techniques to prevent overfitting. It also analyzes strategies for training models on supercomputers and distributing processing across graphics processing units (GPUs). The course is studied through research seminars and deep programming labs (PyTorch / TensorFlow).
AI Governance and Ethics
MAI503 4 credit hours Intermediate
•Academic Description: The course provides a precise institutional analysis of the legal, ethical, and regulatory frameworks governing the development and deployment of artificial intelligence systems. It deeply studies issues of algorithmic bias, fairness, and Explainable AI (XAI) concepts. It critically analyzes international regulatory laws (such as the EU AI Act), autonomous systems liability for errors, and the protection of privacy and intellectual property rights for generative model outputs. It also discusses the role of institutions in establishing responsible governance ensuring transparency and societal accountability. The course is studied through research seminars relying on references and regulatory case studies.
Large Language Models and Generative AI (LLMs & GenAI)
MAI504 4 credit hours Intermediate
•Academic Description: This course offers an advanced and detailed study of the architecture and applications of Large Language Models (LLMs) and Generative AI (GenAI) technologies. It analyzes the precise rules for building and training models such as GPT, LLaMA, and Claude, advanced Prompt Engineering techniques, Retrieval-Augmented Generation (RAG), and model Fine-tuning using LoRA / QLoRA techniques. Students also train in engineering and developing autonomous AI agents capable of automating complex multi-step tasks. The course is studied through research seminars and applied programming labs.
Advanced Computer Vision and Video Analytics
MAI505 4 credit hours Intermediate
•Academic Description: This course provides an advanced critical and applied study of computer vision algorithms and digital image/video analysis. It studies advanced visual feature extraction techniques, image segmentation, real-time object detection and tracking, and 3D reconstruction. It transcends traditional classification to focus on complex applications in autonomous systems and guided vehicles, and smart facial recognition and visual behavior systems. It also dedicates significant space to studying image and video generation models (Diffusion Models). The course is studied through research seminars and live computer vision labs.
Reinforcement Learning and Autonomous Control Systems (Elective)
MAI506 4 credit hours Intermediate
•Academic Description: This course provides an in-depth applied study of sequential decision-making processes based on Reinforcement Learning (RL) algorithms. Students deeply study Markov Decision Processes (MDP), Q-Learning, Policy Gradients, and Deep Reinforcement Learning (Deep RL). Researchers train in applying these algorithms to train autonomous robots, guided vehicles, and optimize the performance of complex games and interactive systems. The course is studied through research seminars and interactive simulation labs.
Machine Learning Operations (MLOps) (Elective)
MAI507 4 credit hours Intermediate
•Academic Description: This course addresses the engineering and operational processes for deploying artificial intelligence models in institutional production environments (Machine Learning Operations - MLOps). It extensively studies the model lifecycle from experimentation to actual operation (CI/CD for AI), automating model training pipelines, monitoring model performance in real-time to detect degradation and Model Drift, and managing AI-specific cloud containers (Docker & Kubernetes). The course is studied through research seminars and cloud operational labs.
Advanced Natural Language Processing and Sentiment Analysis (Elective)
MAI508 4 credit hours Intermediate
•Academic Description: This course provides a specialized, advanced study of Natural Language Processing (Advanced NLP) techniques. It examines advanced semantic analysis, deep machine translation, text generation, and Question Answering Systems. It focuses particularly on the morphological and syntactic challenges of processing Arabic and regional languages. It also covers deep sentiment analysis and opinion mining from social media and corporate reports. The course is studied through research seminars relying on references and software applications.
Artificial Intelligence in Justice and Law (Legal AI) (Elective)
MAI509 4 credit hours Intermediate
•Academic Description: This course examines the practical application of artificial intelligence in the justice and legal sector (Legal AI). It analyzes how to deploy natural language processing and large language models in automated judicial analysis, smart legal research in case law, automated contract evaluation and drafting, and predicting judicial rulings based on historical data. The course critically discusses the limits of these technologies and the legal and ethical risks of relying on them entirely in the judiciary. The course is studied through research seminars relying on references and real judicial case studies.
AI Model Security and Counter-Attacks (Elective)
MAI510 4 credit hours Intermediate
•Academic Description: This course provides a precise analysis of smart model security vulnerabilities and cyberattacks targeting artificial intelligence algorithms (AI Security). It deeply studies Data Poisoning, Adversarial Attacks, Model Extraction, and sabotaging generative model outputs. It analyzes robust defense strategies, model robustness enhancement, and protecting training data privacy using Federated Learning. The course is delivered via research seminars and interactive security labs.
Practical Application 1 (Virtual Course - Part 1)
MAI511 5 credit hours Intermediate
•Academic Description: The student is assigned an undergraduate virtual course (e.g., Introduction to AI or Python Programming) as applied practical training. This course aims to enhance the student's abilities in knowledge transfer and advanced presentation skills for complex mathematical and algorithmic concepts in a simplified and rigorous academic manner. The student must design a teaching structure for the first half of this course, dividing it methodically into six or seven concise lectures. For each lecture, the student prepares a comprehensive written summary in Word format, detailing the objectives and scientific content based on rigorous technical analyses and references. This summary is accompanied by a video recording where the student presents the lecture using a presentation program (PowerPoint). The presentation must be scientifically accurate, focused, and objective, lasting between 10 and 20 minutes per lecture. This practical training evaluates the student's competence in strategic technical information summarization, academic and executive presence, and presentation skills essential for AI engineers and scientists.
Master's Thesis 1 (Preparation and Proposal)
MAI512 9 credit hours Intermediate
•Academic Description: This course represents the actual start of work on the master's thesis, commencing concurrently with the practical training. In this section, the student begins the research and preparation phase under direct academic supervision from a specialized professor. The student first focuses on precisely crystallizing an original research problem in the field of artificial intelligence, machine learning, or deep learning. The student gathers references, sources, and recent academic/technical literature (such as research papers published in global conferences like NeurIPS, ICML). Subsequently, the student formulates the theoretical and conceptual framework, and determines the scientific and programming methodology (whether experimental, developmental, or analytical) to be adopted to reach conclusions and test hypotheses. The student submits the research Proposal for approval by the college board. They begin drafting the preliminary chapters or theoretical framework of the thesis, laying a solid and coherent foundation for the in-depth technical research to be completed in the fourth semester.
Practical Application 2 (Virtual Course - Part 2)
MAI513 4 credit hours Intermediate
•Academic Description: The student completes the applied practical training on the virtual course started in the third semester. The student divides the remaining part of the course into another six or seven concise lectures. For each lecture, the student prepares a comprehensive written summary in Word format, accompanied by a video recording where the student presents the lecture using a presentation program (PowerPoint). The recording for a single lecture must not be less than 10 minutes and not more than 20 minutes. This course contributes to consolidating the student's executive presentation skills, successfully completing the requirements for academic knowledge transfer, and confirming their ability to analyze and present complex intelligent theories and models with extreme clarity and professionalism.
Master's Thesis 2 (Completion and Defense Preparation)
MAI514 9 credit hours Intermediate
•Academic Description: This course represents the second and final half of the work on the master's thesis, continuing concurrently with Practical Application 2. In this section, the research and applied work started in the third semester is completed. The student writes the analytical and applied chapters, conducting experiments, training models, and discussing technical results objectively. Crucially in this phase is the student's ability to extract profound smart insights and conclusions, and provide practical recommendations and Algorithmic Frameworks derived and grounded exclusively on the results of their study to support decision-makers and systems engineers, followed by drafting the comprehensive conclusion. The final draft of the thesis is prepared for methodological and scientific review and auditing by the supervisor, in preparation for submission to the discussion and arbitration committee to obtain final approval for awarding the Master's degree in Artificial Intelligence.