Goals of the Programme:
* Provide a strong foundation in AI/ML theory: Enable students to acquire broad and in-depth knowledge of artificial intelligence and machine learning principles, including foundational mathematics, algorithms, and domain-specific knowledge, so they understand both classical approaches and state-of-the-art techniques in the field.
* Develop advanced technical skills: Equip students with proficient technical abilities to design, build, and deploy AI systems. This includes programming competency, data analysis skills, experience with AI/ML frameworks and tools, software engineering practices for AI, and exposure to technologies like cloud computing and MLOps for model deployment.
* Foster analytical and problem-solving capabilities: Cultivate students’ ability to think critically and solve complex problems using AI techniques. They will learn to identify appropriate AI methods for a given problem, analyze data and model performance rigorously, and innovate in applying computational solutions to real- world challenges.
* Instill ethical and professional values: Ensure students develop a strong sense of ethics, responsibility, and societal awareness in AI practice. The programme emphasizes understanding the ethical implications of AI (such as fairness, accountability, and privacy) and encourages students to be conscientious and professional when collaborating on projects or making decisions as AI practitioners.
* Enhance collaborative and communication skills: Train students to work effectively in teams and communicate their ideas clearly. Students will engage in group projects and presentations to build teamwork, leadership, and communication skills, enabling them to explain complex AI concepts to both technical colleagues and non-specialist stakeholders.
* Prepare graduates for careers or further study: Ready students for immediate employment in AI/ML roles or for postgraduate education. By graduation, students will have a portfolio of project work and practical experience (including a capstone project/internship) that demonstrates their capabilities, positioning them to take on professional positions such as AI engineer, data scientist, machine learning specialist, or to pursue advanced research and studies in the field.
Successful Completion of Higher Education (A' Level 2 pass - E and above)
OR
Attainment of a Level 4 qualification in a related field
OR
Attainment of a Level 4 Foundation Study Program approved for the specific Diploma program
20 years old, completion of a Level 4 qualification (unrelated), and successful completion of an MQA approved University Preperation Program
OR
20 years old, completion of secondary school, 2 years of relevant work experience, and successful completion of an MQA approved University Preparation Program.
Semester 1
Introduction to Python
Object Oriented Programming
Discrete Mathematics & Logic
Calculus and Linear Algebra for AI
Semester 2
Probability and Statistics with Numpy
Data Structures and Algorithms
Database Systems and SQL
Introduction to Artificial Intelligence
Semester 3
Software Engineering for AI Systems
Machine Learning
Deep Learning
Big Data Analytics
Semester 4
Computer Vision
Advanced Algorithms & Optimization
Data Analytics & Visualization
Natural Language Processing
Semester 5
Reinforcement Learning
Robotics & Autonomous Systems
Cloud Computing & MLOps
AI Ethics and Society
Semester 6
Emerging Trends in AI
Advanced Deep Learning
Capstone Project in AI & Machine Learning
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