M.Sc. in AI & ML Engineering
Artificial Intelligence and Machine Learning are transforming industries worldwide. This program prepares students to design intelligent systems, build scalable AI solutions, and apply advanced machine learning techniques to solve complex real-world problems.
Our university offers a comprehensive range of academic programs designed to inspire innovation, research, and real-world impact. The M.Sc. in AI & ML Engineering program combines theoretical foundations with practical engineering experience to ensure students develop the skills needed in today’s data-driven global environment. From core computing systems to advanced artificial intelligence models, the curriculum emphasizes analytical thinking, hands-on learning, and industry-aligned technologies. With guidance from experienced faculty and access to modern laboratories, students gain opportunities for research, development, and applied AI innovation.
Program Highlight
About Programs
The Master of Science in AI & ML Engineering focuses on building advanced expertise in artificial intelligence technologies, intelligent data systems, and scalable machine learning infrastructure. The program integrates core computing principles with modern AI development frameworks and real-world applications. Students develop practical experience in model development, system deployment, and intelligent data processing through project-based learning and collaborative research.
Master of Science in AI & ML Engineering (24 Months)
Session: 2025 - 2026
Typical Program Structure
A 24-month M.Sc. in AI & ML Engineering program typically follows a structure divided across four academic terms. The curriculum blends advanced coursework with practical labs and applied AI development projects.
- Duration: 24 Months (Full-Time)
- Total Credits: 36 Credits
- Structure: Focused coursework followed by a final AI engineering capstone project completed during the final term.
Core Curriculum Topics
Computing Systems
- Advanced Computing Systems and Architecture
- Distributed and Cloud-Based Systems
Data & Software Development
- Data Management and Database Systems
- Programming and Software Development
- Software Quality, Testing, and Reliability
Secure Engineering & Professional Practice
- Secure Systems and Software Practices
- IT Project Management and Professional Practice
Artificial Intelligence & Machine Learning
- Machine Learning and Intelligent Systems
- Deep Learning and Advanced AI Methods
AI Systems & Data Intelligence
- AI Systems Engineering and Deployment
- Intelligent Data Processing and Optimization
- AI/ML Engineering Capstone Project
Curriculum Overview
Our M.Sc. in AI & ML Engineering curriculum is designed to deliver a balanced and future-focused learning experience that combines advanced theoretical foundations with practical engineering skills. The program integrates core computing systems, intelligent algorithms, data engineering, and modern AI development frameworks. Through hands-on laboratories, applied research projects, and interdisciplinary learning opportunities, students develop strong capabilities in machine learning modeling, data processing, system deployment, and AI solution design for real-world challenges.
Curriculum Breakdown Summary
The curriculum is structured to provide a comprehensive and progressive learning pathway that supports both academic excellence and practical skill development. It is organized into four key components: Core Computing Courses, Artificial Intelligence & Machine Learning, Systems Engineering & Security, and Capstone Project.
| Regular Students | Required Credits |
|---|---|
| Core Courses | 33 Credits |
| Capstone Project | 3 Credits |
| Total | 36 Credits |
Computing Systems
| Cours Code | Course Name | Course Description | Credits |
|---|---|---|---|
| AIML601 | Advanced Computing Systems and Architecture | Explores modern computing architectures, parallel processing techniques, high-performance systems, and infrastructure required for large-scale AI applications. | 3 Credits |
| AIML602 | Data Management and Database Systems | Introduces database architecture, data modeling, query optimization, and scalable data storage systems used in modern analytics and AI environments. | 3 Credits |
Data & Software Development
| Cours Code | Course Name | Course Description | Credits |
|---|---|---|---|
| AIML603 | Programming and Software Development | Focuses on advanced programming principles, object-oriented design, and modern software development frameworks. | 3 Credits |
| AIML604 | Distributed and Cloud-Based Systems | Examines distributed computing models, cloud platforms, and scalable system architectures for enterprise applications. | 3 Credits |
| AIML605 | Software Quality, Testing, and Reliability | Covers software testing strategies, quality assurance frameworks, debugging methods, and reliability engineering for complex systems. | 3 Credits |
Secure Engineering & Professional Practice
| Cours Code | Course Name | Course Description | Credits |
|---|---|---|---|
| AIML606 | Secure Systems and Software Practices | Introduces secure software design principles, system protection techniques, and risk mitigation strategies for modern computing environments. | 3 Credits |
| AIML607 | IT Project Management and Professional Practice | Focuses on project planning, agile development methodologies, teamwork, and professional practices in large-scale technology projects. | 3 Credits |
Artificial Intelligence & Machine Learning
| Cours Code | Course Name | Course Description | Credits |
|---|---|---|---|
| AIML608 | Machine Learning and Intelligent Systems | Covers supervised and unsupervised learning algorithms, model evaluation techniques, and the design of intelligent learning systems. | 3 Credits |
| AIML609 | Deep Learning and Advanced AI Methods | Explores neural networks, deep learning architectures, and advanced AI techniques used in computer vision, NLP, and intelligent systems. | 3 Credits |
AI Systems & Data Intelligence
| Cours Code | Course Name | Course Description | Credits |
|---|---|---|---|
| AIML610 | AI Systems Engineering and Deployment | Focuses on deploying AI models into production environments, system integration, scalability, and monitoring of AI solutions. | 3 Credits |
| AIML611 | Intelligent Data Processing and Optimization | Introduces advanced data processing techniques, feature engineering, and optimization methods for improving AI model performance. | 3 Credits |
| AIML612 | AI/ML Engineering Capstone Project | A project-based course where students design, develop, and deploy a complete AI or machine learning solution to solve a real-world problem. | 3 Credits |
Programs Cost
Our tuition structure is designed to remain transparent, competitive, and accessible for students worldwide. The M.Sc. in Cybersecurity program includes comprehensive academic resources, modern virtual laboratories, expert faculty guidance, and industry-aligned coursework. The program follows a fixed tuition model, allowing students to plan their education expenses with clarity and confidence.
| Semester | Term | Tuition |
|---|---|---|
| Semester 1 | Spring (Year 1) | $4,167 |
| Semester 2 | Summer (Year 1) | $4,167 |
| Semester 3 | Fall (Year 1) | $4,167 |
| Semester 4 | Spring (Year 2) | $4,167 |
| Semester 5 | Summer (Year 2) | $4,167 |
| Semester 6 | Fall (Year 2) | $4,167 |
| Total Program Tuition | $25,000 |
Note: Students may complete the program earlier through the fast-track option by taking additional courses per semester.
Tuition After Scholarship
| Scholarship | Total Tuition | Per Semester (6 Semesters) |
|---|---|---|
| Regular Tuition | $25,000 | $4,167 |
| 40% Scholarship | $15,000 | $2,500 |
| 50% Scholarship | $12,500 | $2,083 |
| 60% Scholarship | $10,000 | $1,667 |
| 70% Scholarship | $7,500 | $1,250 |
| 80% Scholarship | $5,000 | $833 |
Note: Students apply for scholarships at the time of submission of their application, or while continuing the studies, and based on that particular students become eligible for scholarships.
Apply Now
Our admissions process is designed to be simple and accessible for students from different educational backgrounds. Whether you are continuing your academic journey, advancing your professional expertise, or joining as an international student, our team provides full support throughout the application process.
Requirements and Deadlines
Our program costs are designed to remain transparent competitive and accessible for students from diverse backgrounds. Each academic program includes tuition fees, registration charges and essential learning resources ensuring students receive high-quality education and comprehensive academic support Costs may vary based on program type, course load, and mode of study (on-campus, hybrid, or online) We aim to provide exceptional value through modern facilities, expert faculty, and industry aligned curriculum making your investment in education both meaningful and future-focused.
Admissions Requirements
1. Academic Qualifications
- Completed secondary education (HSC/A-Level/Equivalent).
- Minimum GPA/grade requirements as set by the university.
- For graduate programs: a recognized bachelor’s degree with required CGPA.
3. English Language Proficiency
- IELTS: 5.5, TOEFL iBT: 46–50, PTE Academic: 42–45, Duolingo: 85–90
- University-approved English placement test (if applicable)
4. Financial Requirements
- Payment of application fee
- Proof of ability to cover tuition and living costs (for international students)
2. Academic Qualifications
- Completed application form
- Academic transcripts & certificates
- National ID/Passport
- Recent passport-sized photographs
- Proof of English proficiency (if required)
- Recommendation letters (for graduate admissions)
- Statement of Purpose/Personal Essay (selected programs)
Application Deadlines for 2026
| Program Level | Semester | Admission Deadline | Start Date | End Date |
|---|---|---|---|---|
| Graduate | Spring 2026 | December 12, 2025 | January 12, 2026 | April 30, 2026 |
| Graduate | Summer 2026 | April 11, 2026 | May 11, 2026 | August 20, 2026 |
| Graduate | Fall 2026 | August 8, 2026 | September 8, 2026 | December 18, 2026 |
Application Deadlines for 2027
| Program Level | Semester | Admission Deadline | Start Date | End Date |
|---|---|---|---|---|
| Graduate | Spring 2027 | December 11, 2026 | January 11, 2027 | April 30, 2027 |
| Graduate | Summer 2027 | April 10, 2027 | May 10, 2027 | August 20, 2027 |
| Graduate | Fall 2027 | August 7, 2027 | September 7, 2027 |
Requirements and Deadlines
Our program costs are designed to remain transparent competitive and accessible for students from diverse backgrounds. Each academic program includes tuition fees, registration charges and essential learning resources ensuring students receive high-quality education and comprehensive academic.