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2025/07/31 - 06:44 ص06/صفر/1447 - 09:44
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The Faculty of Computers and Information Technology at the University of Tabuk proposes a new Master program of Science in Artificial Intelligence (AI). The program is one of the first specialized AI graduate programs in Saudi Arabia. It is designed to provide students with a comprehensive knowledge in AI. It is a graduate professional program that prepares students to compete for jobs in high-demand in the field of AI industry. The program adopts a project-based method that helps the students to apply the concepts learned in the core courses into a project during their last two semesters.
To achieve the program's objectives, it is significant for the program to enable students to be engaged into extensive courses that provide them with the core concepts, which represent the foundations of AI. In order to increase and expand the student knowledge, a variety of elective courses that are offered to the student in AI. The well-equipped AI Lab and Centre of Industrial Innovative and Robotics will be available for the Master program in AI. The program qualifies individuals to expand their knowledge by providing an internship during a full semester in their study.
The program curriculum will develop deep understanding and acquire knowledge in the state-of-the-art in Artificial Intelligence, Machine Learning, Deep Learning, Robotics, Fuzzy-based Methods and Data Science and the application of these techniques in Autonomous Driving and Compute Vision. The curriculum also contains a project that enables students to apply core course materials to a practical project in an actual environment.
Master of Science in Artificial Intelligence.
To graduate highly skilled AI professionals equipped with the knowledge, skills, and ethics to excel in their careers and contribute to advancements in AI research and community development.
Graduates will be equipped with a deep understanding of core AI concepts, algorithms, and programming techniques.
Graduates will be able to effectively apply AI methodologies and tools to solve real-world problems across diverse domains.
Graduates will be prepared to understand the ethical implications of AI and its impact on society.
Graduates will be ready to contribute to the advancement of AI through innovative research and the development of novel solutions.
Graduates will be equipped with the professional skills and leadership qualities necessary to excel in their AI careers and address community needs.
The graduate will be able to:
● K1: Characterize and explain in-depth theoretical foundations, underlying principles, concepts, functioning tools, techniques, and facts relevant to the artificial intelligence field.
● K2: Define and describe state-of-the-art research, recent developments, and theoretical philosophies in artificial intelligence spheres.
● K3: Recognize and interpret the impact of artificial intelligence processes and practices in automating tasks, revolutionizing industries, and enhancing decision-making processes.
The graduate will be able to:
● S1: Apply current and relevant tools, inquiry methodologies, practices, model training, and operations in the field of artificial intelligence.
● S2: Develop high-level AI-based solutions for handling and solving complex problems.
● S3: Construct and implement essential AI-based solutions, models, applications, and projects while evaluating their outcomes and contributions.
● S4: Design, conduct, and assess the latest advancements in research and projects within the domain of artificial intelligence.
● S5: Utilize techniques, processes, and materials to analyze, interpret, and provide potential solutions for complex AI problems.
● S6: Communicate effectively and skillfully in a range of professional contexts within the realm of artificial intelligence.
The graduate will be able to:
● V1: Demonstrate professional and ethical practices and responsibilities within the domain of artificial intelligence.
● V2: Practice effective, ethical, and professional leadership and communication in all duties, tasks, projects, and teamwork within the field of artificial intelligence.
The Master of Science in Artificial Intelligence program at the University of Tabuk is a course-based program along with a project to be submitted at the end of the program during two semesters.
The students are required to complete a minimum of 47 credit hours by both courses and a project.
With the approval of a supervising professor, qualified students may be admitted to the program. Master of Science in Artificial Intelligence students must complete 12 courses, 2 research projects (Research Project 1 and Research Project 2) and an internship in one of well-known organizations
In particular, students must complete minimum 47 credit hours, including:
At least 36 course credits that include:
33 course credits of core courses.
3 elective course credit.
8 Project credits: Research Project 1 (4 credits) and Research Project 2 (4 credits).
3 course credits for an internship period.
During the first and second semesters of the program time the students will focus on courses. From the third semester students will focus on their research projects.
Students must satisfy the requirements listed in Program Design Section.
Each student must submit an approved project, based on appropriate research experience, in accordance with the regulations of the both the university and the department. The project must be produced under the direction and with the approval of the student's research supervisor, who must be a member of the faculty staff. The handbook(s) of the Deanship of Postgraduate Studies should be consulted for rules, procedures, and deadlines for preparation and submission of the final approved copy of the project.
Each candidate must also pass a project examination to evaluate the student knowledge and contribution of research in terms of a viva examination.
Students must maintain a cumulative Grade Point Average (GPA) of at least 3.75 out of 5 on graduate degree courses.
The entire work for the Master's degree can be completed within a period of 2.5 calendar year. But, all articles of the unified law organizing the graduate studies in Saudi universities, and those also enumerated in the Regulations and operating procedures for the Postgraduate Study booklet (University of Tabuk), control time limit for acquiring Master's degree.
2 years (4 Semesters + internship): Full-time
Fees
15,5000 SAR per semester. (No fees for Summer Internship)
62,000 SAR per program.
Full Time –2.5 Year Plan
1St Semester
| المقـــــــــــــرر | Total Credits | Credits | | Prerequisite |
المقابل اللغوي | |
| | | Th. | Pr. | | Code/No | Course Title |
| أساسيات الذكاء الاصطناعي | 3 | 3 | - | - | CSC601 | Fundamentals of Artificial Intelligence |
| الرياضيات الحاسوبية | 3 | 3 | - | - | CSC602 | Computational Mathematics |
| البرمجة المتقدمة وإطارات برمجة الذكاء الاصطناعي | 3 | 3 | - | - | CSC605 | Advanced Programming and AI Frameworks |
| طرق بحث | 3 | 3 | - | - | CSC608 | Research Methods |
| المجموع | 12 | 12 | - |
| المقـــــــــــــرر | Total Credits | Credits | | Prerequisite | المقابل اللغوي | |
| | | Th. | Pr. | | Code/No | Course Title |
| تعلم الالة | 3 | 3 | - | CSC602 | CSC603 | Machine Learning |
| الرؤية بالحاسب | 3 | 3 | - | - | CSC606 | Computer Vision |
| الروبوتية والأنظمة المضمنة | 3 | 3 | - | CSC602 | CSC609 | Robotics and Embedded Systems |
| الأنظمة المنطقية الضبابية | 3 | 3 | - | - | CSC607 | Fuzzy Logic Systems |
| المجموع | 12 | 12 |
| المقـــــــــــــرر | Total Credits | Credits | | Prerequisite |
المقابل اللغوي | |
| | | Th. | Pr. | | Code/No | Course Title |
| فترة تدريبية | 3 | - | - | - | CSC699 | Internship |
| المجموع | 3 |
| المقـــــــــــــرر | Total Credits | Credits | | Prerequisite | المقابل اللغوي | |
| | | Th. | Pr. | | Code/No | Course Title |
| الشبكات العصابية والتعلم الذكي | 3 | 3 | - | CSC603 | CSC604 | Neural Networks and Deep Learning |
| الروبوتية المتقدمة | 3 | 3 | - | CSC609 | CSC610 | Advanced Robotics |
| مشروع بحثي 1 | 4 | 4 | - | Completion of 21 credit hours | CSC624 | Research Project 1 |
| المجموع | 10 | 10 |
| المقـــــــــــــرر | Total Credits | Credits | | Prerequisite | المقابل اللغوى | |
| | | Th. | Pr. | | Code/No | Course Title |
| اساسيات علم البيانات | 3 | 3 | - | - | CIS601 | Fundamentals of Data Science |
| مقرر اختياري | 3 | 3 | - | Completion of 30 credit hours | - | Elective Course |
| مشروع بحثي 2 | 4 | 4 | - | CSC624 | CSC625 | Research Project 2 |
| المجموع | 10 | 10 |
| المقـــــــــــــرر | Total Credits | Credits | | Prerequisite | المقابل اللغوي | |
| | | Th. | Pr. | | Code/No | Course Title |
| معالجة اللغات الطبيعية | 3 | 3 | - | Completion of 30 credit hours | CSC612 | Natural Language Processing |
| أنظمة استرجاع المعلومات | 3 | 3 | - | | CIS610 | Information Retrieval |
| تحليل البيانات الضخمة | 3 | 3 | - | | CIS611 | Big Data Analytics |
| تحليل بيانات الرعاية الصحية | 3 | 3 | - | | CSC615 | Data Analytics in Healthcare |
| القيادة الذاتية للمركبات | 3 | 3 | - | | CSC616 | Self-driving Vehicles |
| تنقيب البيانات | 3 | 3 | - | | CSC617 | Data Mining |
| الحوسبة المتوازية | 3 | 3 | - | | CSC618 | Parallel Computing |
| التعلم المعزز | 3 | 3 | - | | CSC619 | Reinforcement Learning |
| الفضاء الذكي وانترنت الأشياء | 3 | 3 | - | | CSC620 | Smart Space and IoT |
| الذكاء الاصطناعي في المجال الطبي | 3 | 3 | - | | CSC621 | AI for Wearable and Healthcare |
| مواضيع مختارة في الذكاء الاصطناعي | 3 | 3 | - | | CSC622 | Selected topics in AI |
| Code | Course Title | Credits | Prerequisite |
| CSC 601 | Fundamentals of Artificial Intelligence | 3 | None |
|
Description This course gives a basic introduction to artificial intelligence (AI) and its applications. Students will study the core concepts and topics of AI including its history, solving problems, algorithmic and learning approaches. Through this course, students will learn how to apply AI methods to solve different problems. This course is an exploration of AI domain and its applications in modern lives. | |||
| Code | Course Title | Credits | Prerequisite |
| CSC 602 |
Computational Mathematics
| 3 | None |
|
Description This course familiarizes students with theories, fundamental conceptions and their basic applications in probability, mathematics statistics, calculus and linear algebra. The course aims at helping students who have a major in computing, science, and other similar fields to develop skills that are useful in solving mathematical problems; for example, computing skills of statistics and probability, calculus and linear algebra that are needed as core subjects to proceed with the machine learning course. | |||
| Code | Course Title | Credits | Prerequisite |
| CSC605 | Advanced Programming and AI Frameworks | 3 | None |
|
Description This course introduces students to the dominant programming languages for AI/ML and deep learning such as Python and R. It dives deeply into programming tools, libraries, and frameworks for building research projects. This includes reading and loading datasets, preprocessing data, understanding structure using statistical summaries and data visualization, data manipulation, and cleaning techniques. This course also guides students through learning and implementing popular ML and deep learning frameworks such as TensorFlow, Keras, and PyTorch for symbolic math, used to perform differential programming and linear algebra. Finally, this course will cover the fundamentals of the MATLAB. | |||
| Code | Course Title | Credits | Prerequisite |
| CSC608 | Research Methods | 3 | None |
|
Description The course aims to familiarize students with the fundamental concepts of research and the importance of research and its methodologies, including theory of science and qualitative and quantitative methods. Also, the course aims to give students skills for understanding the structure of a research paper, critical reading of research paper, developing a research proposal for a master's project and writing a research manuscript. Students will use these theoretical concepts of research to begin to critically review literature relevant to the field of artificial intelligent and its applications. | |||
| Code | Course Title | Credits | Prerequisite |
| CSC603 | Machine Learning | 3 | CSC602 |
|
Description This course introduces the fundamental concepts and functioning of machine learning. It covers theoretical foundations as well as essential algorithms for supervised and unsupervised learning. The practical part will focus on the application of machine learning to a range of real-world problems. The topics include linear and logistic regression, naïve Bayes classifier, k-NN, decision tree, random forest, support vector machine, clustering, dimensionality reduction, and perceptron. | |||
| Code | Course Title | Credits | Prerequisite |
| CSC606 | Computer Vision | 3 | None |
|
Description This course introduces the students to computer vision throughout the continuum from image processing to computer vision, which can be broken up into low, mid and high-level processes, including Image Acquisition, Image Enhancement, Image Restoration, Morphological Processing, Image Segmentation, Representation & Description, and Object Recognition. | |||
| Code | Course Title | Credits | Prerequisite |
| CSC609 | Robotics and Embedded Systems | 3 | CSC602 |
|
Description Robotics and Embedded systems course covers the fundamentals of robotics including position, actuators, and robot coordinate system. Manipulator configuration including axis, angles, and frames are also investigated. Moreover, understanding forward and inverse kinematics as well as Denavit-Hartenberg convention. In the Embedded system part of the course, the following topics are covered: CPU architecture, instruction set, program development, and structured assembly processor. | |||
| Code | Course Title | Credits | Prerequisite |
| CSC607 | Fuzzy Logic Systems | 3 | None |
|
Description The goal of the course is to familiarize students with theoretical background besides the mathematical models of fuzzy logic and sets. When it is compared to the traditional logical systems, the fuzzy logical theory is closer to human thinking in spirit than the traditional logical system; the Fuzzy logic try to imitate human thinking thus reason in a way that is approximate rather than precise. This course has elements that assist students in understanding how to build fuzzy information representation and processing; this includes approximate reasoning and fuzzy inference. The knowledge enables the students to design intelligent systems, as well as controllers. The course will equip students with knowledge on the recent innovations of fuzzy logic systems, whose example include computing with words, the Interval Type-2 fuzzy systems, and the general Type-2 Fuzzy logic systems | |||
| Code | Course Title | Credits | Prerequisite |
| CSC604 | Neural Networks and Deep Learning | 3 | CSC603 |
|
Description This course offers a broad introduction to neural networks and deep learning. It also explores the applications and theories relevant to problem solving using deep learning. The course applies deep learning algorithms to real-life problems in diverse domains such as computer vision, natural language processing, sequence modelling, and more. It covers neural networks, backpropagation, optimization (SGD, RMSprop, and Adam), autoencoders, convolutional neural networks, inception, residual networks, RNNs, LSTM, dropout, batch normalization, Xavier initialization, transfer learning, generative adversarial networks, and deep reinforcement learning. | |||
| Code | Course Title | Credits | Prerequisite |
| CSC610 | Advanced Robotics | 3 | CSC609 |
|
Description Advanced Robotics course investigates high-level robotic topics such as velocity kinematics, Jacobian derivation, singularities, performance matrices, trajectory planning, and PID control. In addition, topics like velocity sensing, control theory, path control, dynamics, and automation are also covered. In the practical part of the course, simulation of manipulators and robot programing are investigated.
| |||
| Code | Course Title | Credits | Prerequisite |
| CIS601 | Fundamentals of Data Science | 3 | None |
|
Description The Data Science course concentrates on techniques and methods needed to in the Data Science project life-cycle, which includes data collection, data management and data preprocessing, analysis, presentation, as well as operationalization. This class aims at giving students an introduction to all phases of the process of data process and using modern tools and real data; they will gain hands-on experience of the process. The course includes topics such as data formats, cleaning, and loading; data analysis, data governance, data storage in no-relational and relational store; topping up using cluster computing; and data visualization. They will also store and access various data through using suitable data management tools, database, control accessibility of data that is sensitive, and implement conversions of data in different formats. Finally, students will be capable of presenting the results of data science project using reports and visualizations to be used as a foundation of operationalization. | |||
| Code | Course Title | Credits | Prerequisite |
| CSC612 | Natural Language Processing | 3 | completion of 30 credit hours |
|
Description This course provides an introduction to the field of computational linguistics, also called natural language processing (NLP) - the creation of computer programs that can understand and generate natural languages (such as English). We will use natural language understanding as a vehicle to introduce the three major subfields of NLP: syntax (which concerns itself with determining the structure of an utterance), semantics (which concerns itself with determining the explicit truth-functional meaning of a single utterance), and pragmatics (which concerns itself with deriving the context-dependent meaning of an utterance when it is used in a specific discourse context). The course will introduce both linguistic (knowledge-based) and statistical approaches to NLP, illustrate the use of NLP techniques and tools in a variety of application areas, and provide insight into many open research problems. | |||
| Code | Course Title | Credits | Prerequisite |
| CIS610 | Information Retrieval | 3 | completion of 30 credit hours |
|
Description This course studies the basic principles and practical algorithms used for information retrieval and text mining. It covers the tasks of indexing, searching, and recalling data, particularly text or other unstructured forms. The contents also include statistical characteristics of text, several important retrieval models, text categorization, recommendation system, clustering, information extraction, etc. The course emphasizes both the above applications and solid modeling techniques (e.g., probabilistic modeling) that can be extended for other applications. | |||
| Code | Course Title | Credits | Prerequisite |
| CIS611 | Big Data Analytics | 3 | completion of 30 credit hours |
|
Description This course gives an overview of the Big Data phenomenon, focusing thenon extracting value from the Big Data using predictive analytics techniques. It also focuses on big data phenomenon, the main Big Data tools (Hadoop & Spark), the potential use in a corporate environment, the use of predictive analytics on big data.
| |||
| Code | Course Title | Credits | Prerequisite |
| CSC615 | Data Analytics in Healthcare | 3 | completion of 30 credit hours |
|
Description This course introduces key concepts and methods in bioinformatics. Emphasis will be put on efficient algorithms and techniques used in common applications for the analysis of genetic sequences. Topics covered: comparison and alignment of two or more sequences, indexing and searching of sequence databases, motif discovery, searching with sequence patterns, gene prediction as well as mapping and assembly of data from genome sequencing. Necessary basic knowledge of molecular biology will be communicated throughout. | |||
| Code | Course Title | Credits | Prerequisite |
| CSC616 | Self-driving Vehicles | 3 | completion of 30 credit hours |
|
Description Self-driving cars, have rapidly become one of the most transformative technologies to emerge. They depend on Deep Learning algorithms and they create new opportunities in the mobility sector. This course is an introduction to the practice of deep learning through the applied theme of building a self-driving car. It is open to beginners and is designed for those who are new to machine learning, but it can also benefit advanced researchers in the field looking for a practical overview of deep learning methods and their application. | |||
| Code | Course Title | Credits | Prerequisite |
| CSC617 | Data Mining | 3 | completion of 30 credit hours |
|
Description In this course we explore data mining interdisciplinary field brings together techniques from databases, statistics, machine learning, and information retrieval. We will discuss the main data mining methods currently used, including data warehousing and data cleaning, clustering, classification, association rules mining, query flocks, text indexing and searching algorithms, how search engines rank pages, and recent techniques for web mining. Designing algorithms for these tasks is difficult because the input data sets are very large, and the tasks may be very complex. One of the main focuses in the field is the integration of these algorithms with relational databases and the mining of information from semi-structured data. | |||
| Code | Course Title | Credits | Prerequisite |
| CSC618 | Parallel Computing | 3 | completion of 30 credit hours |
|
Description This course discusses several aspects of parallel computing including parallel architectures, parallel algorithms, parallel programming languages and applications. Students will become familiar with different parallel computing approaches, software design, and programming environments. Also, students will learn how to design, analyze, and implement parallel algorithms for several kind of problems. | |||
| Code | Course Title | Credits | Prerequisite |
| CSC619 | Reinforcement Learning | 3 | completion of 30 credit hours |
|
Description This course provides students a solid introduction to the field of reinforcement learning. It also offers the fundamentals and practical applications of reinforcement learning and will cover the latest techniques used to create agents that can solve a variety of complex tasks, with applications ranging from gaming to finance to robotics. The topics include: Markov decision process, Policies, Value Functions & Bellman Equations, Learning and Planning with Tabular Methods, Dynamic programming, Monte Carlo, Temporal difference, SARSA, Q-learning, Function approximation, On-policy methods, Off-policy methods, Eligibility traces, Inverse Reinforcement Learning , Policy gradients, and Actor-Critic (A2C, A3C). | |||
| Code | Course Title | Credits | Prerequisite |
| CSC620 | Smart Space and IoT | 3 | completion of 30 credit hours |
|
Description The course covers what smart space are, exploring the contrasting visions of how they will transform our urban environments and lives, and considers whether smart cities can be sustainable. Then explains the role that latest and emerging smart networking technologies including Cloud Computing, Virtual Networking, big data analytics, 5G Mobile Networks, Mobile App Development, Unmanned Aerial Vehicles (UAVs), and Data and Network Security, which are creating new opportunities for business, education, research and many other aspects of our daily lives. technology can play in transforming cities and considers challenges such as data ownership, privacy and ethics. | |||
| Code | Course Title | Credits | Prerequisite |
| CSC621 | AI for Wearable and Healthcare | 3 | completion of 30 credit hours |
|
Description This course will present the advantages and challenges of telemedicine services. Special focus is placed on how communication, innovative technology, safety and efficiency are addressed through telemedicine. Also, the course covers Wearable technologies which can be innovative solutions for healthcare problems. The big data generated by wearable devices is both a challenge and opportunity for researchers who can apply more artificial intelligence (AI) techniques on these data in the future. | |||
| Code | Course Title | Credits | Prerequisite |
| CSC622 | Selected topics in AI | 3 | completion of 30 credit hours |
|
Description To highlights the up to date issues in Artificial Intelligence field. The main purpose of this course is to highlight and investigate selected "special topics" in Artificial Intelligence that are not covered in the other offered courses. Such topics might be interrelated to one or more AI disciplines. | |||
| Code | Course Title | Credits | Prerequisite |
| CSC624 | Research Project 1 | 4 | completion of 21 credit hours |
|
Description This course provides students with an opportunity to gather the knowledge and skills learned from the program coursework and conduct a research project with industrial applications. Students are expected to conduct a review of research literature and develop a set of hypotheses for a research project in AI. A research project explaining the hypotheses and alternative remedies to the problem must be submitted to the faculty supervisor at the end of the semester. Students are evaluated based on their research project and oral presentation. | |||
| Code | Course Title | Credits | Prerequisite |
| CSC625 | Research Project 2 | 4 | CSC624 |
|
Description The research outlined in the CSC624 proposal must be completed during this course. The final report of the research findings and recommendations should be submitted to the advisor and the results presented. The results should have direct practical applications and / or be available for publication in refereed publications. Students are evaluated based on the submitted research and oral presentation. | |||
| Code | Course Title | Credits | Prerequisite |
| CSC699 | Internship | 3 | completion of 18 credit hours |
|
Description As part of student's academic program and the most valuable step to learn and experience knowledge from a full-time employment, an internship course gives a significant opportunity for students to engage with professional people and gain practical training experiment. Students will be able to apply theoretical concepts to practical or laboratory work.
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You can read more about the program through the following link: Master of Science in Artificial Intelligence