AI-301
BSC-AIOFFICIAL PROGRAM PAGE
PROGRAM OVERVIEW
Bachelor 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
- Bachelor's
- Credits
- 120
- Duration
- 3 calendar years / 9 terms
- Delivery
- Online
- Languages
- English
- Admissions
- Not currently available
Why this program
Build, evaluate and deploy AI systems — not just use AI tools.
A good fit if
Aspiring AI/ML engineers, data and AI developers, and technical founders.
Study objectives & learning outcomes5
- Programming and algorithms
- Machine learning and deep learning
- NLP, LLMs and multimodal AI
- MLOps and AI infrastructure
- Responsible AI and security
What you’ll build4
- AI product studio
- ML and deep-learning experiments
- MLOps deployment artifact
- Two-part AI capstone
Career directions4
- AI/ML Engineer
- AI Developer
- Junior Data/ML Engineer
- AI Product or Implementation roles
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 program40
The courses below reflect the current program curriculum. Course details may be updated through the university's academic review process.
AI-302
AI-302 — Machine Learning
AI-303
AI-303 — Deep Learning
AI-304
AI-304 — Natural Language Processing & LLMs
AI-305
AI-305 — Computer Vision & Multimodal AI
AI-306
AI-306 — Generative AI Systems
AI-307
AI-307 — MLOps & AI Infrastructure
AI-308
AI-308 — Responsible AI, Governance & Law
AI-309
AI-309 — AI Security & Adversarial ML
AI-310
AI-310 — Applied AI Product Studio
AI-391
AI-391 — Robotics & Autonomous Intelligent Systems
AI-392
AI-392 — Edge AI & Intelligent IoT
AI-399
AI-399 — Advanced AI Elective
AI-498
AI-498 — AI Capstone I
AI-499
AI-499 — AI Capstone II
CSC-111
CSC-111 — Programming I
CSC-112
CSC-112 — Programming II & OOP
CSC-211
CSC-211 — Data Structures & Algorithms
CSC-212
CSC-212 — Databases & Data Management
CSC-213
CSC-213 — Computer Systems & Operating Systems
CSC-214
CSC-214 — Networks & Cloud Fundamentals
CSC-215
CSC-215 — Software Engineering
CSC-216
CSC-216 — Data Engineering
CSC-217
CSC-217 — Security & Privacy Fundamentals
MAT-111
MAT-111 — Calculus for AI
MAT-112
MAT-112 — Linear Algebra for AI
MAT-211
MAT-211 — Probability & Statistics
MAT-212
MAT-212 — Discrete Mathematics
MAT-213
MAT-213 — Optimization for AI
SCI-201
SCI-201 — Applied Physical Science for Computing
UNI-101
UNI-101 — Academic & Professional Communication
UNI-102
UNI-102 — Critical Thinking & Problem Solving
UNI-103
UNI-103 — Quantitative Reasoning
UNI-104
UNI-104 — Digital Literacy & Data Fundamentals
UNI-105
UNI-105 — Ethics, Society & Global Citizenship
UNI-106
UNI-106 — Research & Information Literacy
UNI-201
UNI-201 — Entrepreneurship & Innovation
UNI-202
UNI-202 — Career, Teamwork & Leadership
UNI-203
UNI-203 — Sustainability & Responsible Technology
UNI-204
UNI-204 — Interdisciplinary Applied Project
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.