- The duration of study for a Bachelor of Applied Sciences degree in all majors is four academic years for students admitted on the basis of a high school diploma, and two years for those admitted to the upgrade program based on the institute and their qualifications.
- The default duration of study is four years, but it is possible to enroll in full-time, intensive study, enabling the student to graduate in three years.
- The academic year is divided into three semesters, and the University Council determines the start and end dates of studies and exam dates according to the university calendar.
The Bachelor of Science in Artificial Intelligence Engineering program aims to prepare engineers capable of designing and developing advanced artificial intelligence systems that meet the needs of Industry 4.0. The program combines theoretical foundations in advanced mathematics, machine learning algorithms, and big data processing with practical applications in robotics, computer vision, and intelligent systems.
The program is characterized by its focus on:
- Deep learning and neural networks.
- Natural Language Processing (NLP) and Human-Machine Interaction.
- Cybersecurity of Intelligent Systems and Ethics in Artificial Intelligence.
The program is taught via interactive learning platforms (such as Moodle and Zoom) with virtual labs (Google Colab, AWS AI Labs), and includes annual applied projects and partnerships with leading companies such as NVIDIA and IBM.
Vision
To be a leading regional center for AI engineering education by 2030.
Mission
Preparing creative engineers capable of leading digital transformation through sustainable AI solutions.
Objectives
– Develop programming and mathematical analysis skills.
– Empower students to build scalable AI systems.
– Promote innovation in the fields of health, energy, and smart cities.values
Technical excellence, teamwork, transparency, and ethical responsibility in the use of AI.
Program Inputs (Admission Requirements):
- Educational qualification: High school diploma (science/information technology) with a minimum of 60%.
- Skills: Programming basics, advanced mathematics, English proficiency (IELTS 5.0).
- Technology: Computer with specifications (i7, RAM 16GB, GPU 4GB).
Program outputs:
- Understanding the theories of artificial intelligence and machine learning.
- Knowledge of AI ethics and privacy laws.
- Develop ML/DL models using (TensorFlow, PyTorch).
- Big data processing (Hadoop, Spark).
- Commitment to AI ethics.
- Working within multidisciplinary teams.
Graduate specifications:
Technical capabilities:
- Building Recommendation Systems, Computer Vision Systems.
- Improve AI models using techniques like Transfer Learning.
Soft skills:
- Agile project management, technical communication with non-experts.
Study Duration
Conditions for success and graduation
1. Each course is evaluated out of 100 points.
2. A student is considered to have passed a course if they obtain a final score greater than or equal to 60% of the course grade.
3. A student is considered to have conditionally passed if they obtain a score of 50 in the course and their overall semester GPA is greater than 2.00 out of 4.00.
4. If a student passes a course and obtains a full mark, the university has the right to verify the student’s level through an oral interview or written exam, and confirm their success in the course or declare them failed if they are not at the required level.
5. If a student fails a course, they must retake it and their exams and pay the full costs.
Graduation Average:
The averages of the courses passed by the student over the five years are added together and divided by the total number of courses, to calculate the overall average.
48 courses - 148 credit hours
First semester (18 credit hours – 30 ECTS)
| Course code | Course name | Course Description | watches | ECTS | Type |
| AIE 101 | Introduction to Distance Education | E-learning and technical project management tools. | 2 | 3 | compulsory |
| AIE 102 | Python Programming for the Basics | Syntax, data structures, and core libraries (NumPy, Pandas). | 4 | 6 | compulsory |
| MATH 101 | Mathematics for Artificial Intelligence | Linear algebra, differentiation and evolution, and probabilities. | 4 | 6 | compulsory |
| PHYS 101 | Computer Physics | Fundamentals of electrical circuits and digital electronics. | 3 | 5 | compulsory |
| ENG 101 | Technical English (1) | Technical terms, coding and code comments. | 3 | 5 | compulsory |
| AIE 103 | Introduction to Artificial Intelligence | History of AI, modern applications, and ethics. | 2 | 3 | compulsory |
Second semester (18 credit hours – 30 ECTS)
| Course code | Course name | Course Description | watches | ECTS | Type |
| AIE 201 | Future Leadership Tools | Technical Project Management and Innovation in AI. | 2 | 3 | compulsory |
| AIE 202 | هياكل البيانات والخوارزميات | Design and analysis of algorithms (search, sorting, trees, graphs). | 4 | 6 | compulsory |
| MATH 201 | Statistics and Mathematical Engineering | Advanced probability, Bayesian statistics, and data modeling. | 4 | 6 | compulsory |
| AIE 203 | Advanced Programming (C++) | Object-oriented programming and performance improvement. | 3 | 5 | compulsory |
| ENG 102 | Technical English (2) | Writing technical reports and presentations. | 3 | 5 | compulsory |
| AIE 204 | Database Basics | SQL, NoSQL, and Database Design for Intelligent Systems. | 2 | 3 | compulsory |
Third semester (18 credit hours – 30 ECTS)
| Course code | Course name | Course Description | watches | ECTS | Type |
| AIE 301 | Basic Machine Learning | – Vocabulary : Algorithms (classification, regression, clustering). – Output : Building a predictive model using Scikit-learn. | 4 | 6 | compulsory |
| AIE 302 | digital signal processing | – Vocabulary : Fourier transform, signal filtering. – Outputs : audio/visual signal processing. | 3 | 5 | compulsory |
| AIE 303 | computer networks | – Vocabulary : TCP/IP protocols, network architecture. – Output : Network simulation using Cisco Packet Tracer. | 3 | 5 | compulsory |
| MATH 301 | Applied Linear Algebra | – Vocabulary : Matrices, Eigenvalues. – Output : Applications in image compression. | 3 | 5 | compulsory |
| ENG 301 | Technical English (3) | – Vocabulary : Writing research papers. – Output : Technical report on AI algorithms. | 3 | 5 | compulsory |
| AIE 304 | Artificial Intelligence Lab (1) | – Vocabulary : Practical applications using Python. – Output : Image classification project. | 2 | 4 | compulsory |
Fourth semester (18 credit hours – 30 ECTS)
| Course code | Course name | Course Description | watches | ECTS | Type |
| AIE 401 | Computer vision | – Vocabulary : Image processing, object detection. – Output : Face detection system using OpenCV. | 4 | 6 | compulsory |
| AIE 402 | Natural Language Processing (NLP) | – Vocabulary : Tokenization, language models. – Output : Building a chatbot. | 4 | 6 | compulsory |
| AIE 403 | Data analysis | – Vocabulary : Data cleaning, data visualization. – Output : Data analysis using Tableau. | 3 | 5 | compulsory |
| AIE 404 | Advanced database systems | – Vocabulary : NoSQL databases. – Outputs : Design a database for a recommendation website. | 3 | 5 | compulsory |
| AIE 405 | Artificial Intelligence Ethics | – Vocabulary : algorithmic bias, privacy. – Output : case study. | 2 | 4 | compulsory |
| AIE 406 | Artificial Intelligence Lab (2) | – Vocabulary : Practical applications. – Outputs : NLP project. | 2 | 4 | compulsory |
Fifth semester (18 credit hours – 30 ECTS)
| Course code | Course name | Course Description | watches | ECTS | Type |
| AIE 501 | Deep learning | – Vocabulary : Neural networks, CNN, RNN. – Output : Image recognition model. | 4 | 6 | compulsory |
| AIE 502 | AI systems security | – Vocabulary : Adversarial attacks, protection. – Output : Secure ML model. | 3 | 5 | compulsory |
| AIE 503 | Cloud computing for artificial intelligence | – Vocabulary : AWS, Google Cloud. – Output : Deploy a model to the cloud. | 3 | 5 | compulsory |
| Optional (1) | Choose two subjects from : | ||||
| AIE 504 | Artificial intelligence in robotics | – Vocabulary : Automatic control, ROS. – Outputs : Programming a simple robot. | 3 | 5 | Optional |
| AIE 505 | Big Data | – Vocabulary : Hadoop, Spark. – Output : Big data processing. | 3 | 5 | Optional |
| AIE 506 | Artificial intelligence in medicine | – Vocabulary : Medical X-ray analysis. – Output : Diagnostic form. | 3 | 5 | Optional |
Semester 6 (18 credit hours – 30 ECTS)
| Course code | Course name | Course Description | watches | ECTS | Type |
| AIE 601 | Large Language Models (LLMs) | – Vocabulary : Transformers, GPT. – Output : Text generation model. | 4 | 6 | compulsory |
| AIE 602 | Internet of Things and Artificial Intelligence | – Vocabulary : Edge AI, TinyML. – Output : Smart home system. | 3 | 5 | compulsory |
| Optional (2) | Choose 3 subjects from : | ||||
| AIE 603 | Artificial intelligence in games | – Vocabulary : NPC design. – Output : Simple game. | 3 | 5 | Optional |
| AIE 604 | Artificial Intelligence in Finance | – Vocabulary : Stock forecasting. – Output : Trading model. | 3 | 5 | Optional |
| AIE 605 | emotional intelligence | – Vocabulary : Sentiment analysis. – Output : Comment analysis system. | 3 | 5 | Optional |
| AIE 606 | Artificial Intelligence and Blockchain | – Vocabulary : Smart contracts. – Output : Simple application. | 3 | 5 | Optional |
Chapter Seven (18 credit hours – 30 ECTS)
| Course code | Course name | Course Description | watches | ECTS | Type |
| AIE 701 | Artificial Intelligence Project (1) | – Vocabulary : defining the problem, collecting data. – Outputs : project plan. | 6 | 10 | compulsory |
| Optional (3) | Choose 4 subjects from : | ||||
| AIE 702 | Artificial intelligence in agriculture | – Vocabulary : Crop analysis. – Outputs : Monitoring system. | 3 | 5 | Optional |
| AIE 703 | Artificial Intelligence in Energy | – Vocabulary : Smart grids. – Outputs : Consumption optimization model. | 3 | 5 | Optional |
| AIE 704 | Artificial Intelligence in Education | – Vocabulary : allocation systems. – Outputs : educational system. | 3 | 5 | Optional |
| AIE 705 | Artificial Intelligence in Marketing | – Vocabulary : Customer analysis. – Output : Recommendation form. | 3 | 5 | Optional |
Chapter Eight (18 credit hours – 30 ECTS)
| Course code | Course name | Course Description | watches | ECTS | Type |
| AIE 801 | Graduation project | – Vocabulary : Developing an integrated system. – Outputs : Presentation to a committee. | 8 | 12 | compulsory |
| AIE 802 | Practical training | – Vocabulary : Training in specialized companies. – Outputs : Final report. | 6 | 10 | compulsory |
| AIE 803 | Artificial Intelligence Laws and Ethics | – Vocabulary : GDPR, legal liability. – Outputs : Case study. | 2 | 4 | compulsory |
| AIE 804 | Preparing a professional portfolio | – Vocabulary : GitHub, LinkedIn. – Output : Profile. | 2 | 4 | compulsory |
Additional notes :
- Elective subjects : The student can choose specialized tracks such as:
- Computer Vision Track : (AIE 504, AIE 603).
- Big Data Track : (AIE 505, AIE 606).
- Practical projects : constitute 40% of the final assessment for each course.