AIM-601
MSC-AIOFFICIAL PROGRAM PAGE
PROGRAM OVERVIEW
Master of Science in Artificial Intelligence
Review the program structure, courses and admissions information, then apply if it matches your goals.
Official program information
PROGRAM EXPERIENCE
All programs
Program at a glance
- Level
- Master's
- Credits
- 36
- Duration
- 1 academic year / 3 terms
- Delivery
- Online
- Languages
- English
- Admissions
- Applications open
Why this program
Build the depth to design, evaluate and deploy AI systems responsibly and with confidence.
A good fit if
Computing and data graduates and technical professionals seeking advanced AI depth.
Study objectives & learning outcomes5
- Advanced machine learning
- Foundation models and RAG
- Vision and multimodal AI
- MLOps and observability
- Responsible AI, governance and security
What you’ll build4
- Advanced model study
- RAG / LLM system
- AI infrastructure design
- Applied AI dissertation or project
Career directions4
- AI/ML Engineer
- Applied AI Specialist
- AI Solutions Architect track
- AI Technical Lead track
Career directions are illustrative and do not guarantee employment, professional title, licensure or eligibility in a particular jurisdiction.
How you will study
EAU uses a digital-first learning model designed around applied learning, evidence of competence and meaningful academic support.
- 100% online delivery, with asynchronous learning as the normal mode.
- Live or observed activity is used where the learning outcome itself requires interaction, demonstration or verification.
- Learning combines focused study, guided practice, authentic tasks, feedback and evidence of competence.
- Academic participation is based on meaningful learning activity and outcomes rather than login time alone.
Courses in this program11
The courses below reflect the current program curriculum. Course details may be updated through the university's academic review process.
AIM-602
AIM-602 — Advanced Machine Learning
AIM-603
AIM-603 — Deep Learning & Foundation Models
AIM-604
AIM-604 — Data Engineering for AI
AIM-611
AIM-611 — NLP, LLMs & Retrieval-Augmented Systems
AIM-612
AIM-612 — Computer Vision & Multimodal AI
AIM-613
AIM-613 — MLOps, AI Infrastructure & Observability
AIM-614
AIM-614 — Responsible AI, Governance & Security
AIM-621
AIM-621 — AI Product Experimentation & Value
AIM-622
AIM-622 — Advanced AI Seminar
AIM-699
AIM-699 — Applied AI Project & Dissertation
How your learning is assessed
- Assessment is based on evidence aligned with defined learning outcomes.
- Depending on the discipline, evidence may include projects, cases, laboratories, analysis, presentations, professional artifacts or verified individual work.
- Feedback supports improvement before later or higher-stakes evidence points.
- Capstone, clinical, oral, security-sensitive or Ijazah requirements may include additional competency gates where applicable.
Academic support
- Clear course roadmaps, requirements and assessment expectations.
- Human academic support alongside the digital learning environment.
- Early support where access, participation or academic progress indicates a meaningful barrier.
- Accessibility and reasonable learning arrangements where applicable without reducing essential learning outcomes.
Programme content is reviewed through the university's academic process. Your issued study plan confirms the sequence that applies to your intake.
Review the programme facts, tuition and admissions availability shown on this page before applying.