Master of Science in Computer Vision - Artificial Intelligence
Abu Dhabi, United Arab Emirates
DURATION
2 Years
LANGUAGES
English
PACE
Full time, Part time
APPLICATION DEADLINE
31 Aug 2025*
EARLIEST START DATE
Aug 2025
TUITION FEES
Request tuition fees
STUDY FORMAT
On-Campus
* deadline for international students/deadline for UAE nationals: May 30th, 2025 | Application start date: Oct 1st, 2024
* *No tuition fee, free accommodation + monthly stipend of 2100USD+
Introduction
This scientific field studies how computers can be used to automatically understand and interpret visual imagery. It aims to mimic the astounding capabilities of human visual cortex using machine vision algorithms. It studies how an image is created, the geometry of the 3D world and high-level tasks such as object recognition, object detection, and tracking, image segmentation and action recognition. Computer vision has important applications in augmented/virtual reality, autonomous cars, service robots, biometrics and forensics, remote sensing, and security and surveillance
Alumni Statistics
Admissions
Curriculum
The minimum degree requirements for the master’s of science (M.Sc.) in computer vision is 36 Credits, distributed as follows:
| Core Courses | Number of Courses | Credit Hours |
| Core | 4 | 16 Credit Hours |
| Research Thesis | 1 | 12 Credit Hours |
| Elective Courses | 2 | 8 Credit Hours |
Core courses
The master’s in computer vision is primarily a research-based degree. The purpose of coursework is to equip students with the right skillset, so they can successfully accomplish their research project (thesis). Students are required to take COM701 as a mandatory course, and the other three core courses below as mandatory courses:
| Code | Course Title | Credit Hours |
| AI701 | Foundations of Artificial Intelligence | 4 |
| MTH701 | Mathematical Foundations for Artificial Intelligence | 4 |
| CV701 | Human and Computer Vision | 4 |
| CV702 | Geometry for Computer Vision *OR* | 4 |
| CV703 | Visual Object Recognition and Detection | 4 |
Elective courses
Students will select a minimum of two elective courses, with a total of eight (or more) credit hours (CH) from a list of available elective courses based on interest, proposed research thesis, and career perspectives, in consultation with their supervisory panel. The elective courses available for the master’s of science degree in computer vision are listed in the table below:
| Code | Course Title | Credit Hours |
| MTH702 | Optimization | 4 |
| AI702 | Deep Learning | 4 |
| DS701 | Data Mining | 4 |
| DS702 | Big Data Processing | 4 |
| HC701 | Medical Imaging: Physics and Analysis | 4 |
| ML701 | Machine Learning | 4 |
| ML702 | Advanced Machine Learning | 4 |
| ML703 | Probabilistic and Statistical Inference | 4 |
| NLP701 | Natural Language Processing | 4 |
| NLP702 | Advanced Natural Language Processing | 4 |
| NLP703 | Speech Processing | 4 |
Research thesis
Master’s thesis research exposes students to an unsolved research problem, where they are required to propose new solutions and contribute towards the body of knowledge. Students pursue an independent research study, under the guidance of a supervisory panel, for a period of one year.
| Code | Course Title | Credit Hours |
| CV699 | Computer Vision Master’s Research Thesis | 12 |
Gallery
Rankings
CS Rankings in a Glance
- 18th in the field of AI in CS Rankings globally
- 28th in the field of ML in CS Rankings globally
- 16th in the field of CV in CS Rankings globally
- 19th in the field of NLP in CS Rankings globally
Program Outcome
Upon completion of the program requirements, the graduate will be able to:
- Exhibit comprehensive and highly specialized knowledge of computer vision in-line with the underlying mathematical and computational principles
- Perform critical literature survey and develop new ideas by integrating multidisciplinary knowledge
- Apply advanced problem-solving skills to analyze, design and execute solutions for the existing and new problems in computer vision faced by both industry and academia
- Become highly skilled in initiating, managing, and completing multifaceted computer vision projects, and be able to clearly communicate concepts, complex ideas, and conclusions both orally and in the form of technical reports
- Function independently and in a team to address computer vision problems under complex and unpredictable real-world settings
- Demonstrate a fundamental understanding of computer vision discipline at an advanced level suitable to pursue a Ph.D. degree and contribute to cutting-edge computer vision research to produce new knowledge or take responsibility to lead innovative and impactful computer vision projects in industry
- Manifest the right learning attitude during coursework and research that clearly shows ownership, personal and technical growth, and responsibility
- Understand legal, ethical, environmental, and socio-cultural ramifications of computer vision technologies, and be able to make informed and fair decisions on complex practical issues
Ideal Students
STEM major students with GPA above 3.2/4.0
Career Opportunities
AI is permeating every industry. At recent employer engagement events at MBZUAI, there has been representation from multiples sectors including (but not limited to):
- Aviation, consultancy, education, energy, finance, government entities, healthcare, media, oil and gas, security and defense, research institutes, retail, telecommunications, transportation and logistics, and startups.
Recent job opportunities advertised via the MBZUAI Student Careers Portal include (but not limited to):
- AI solution architect, AI solution engineer, algorithmic engineer, data analyst, data engineer, data scientist, data strategy consultant, full stack software engineer, full stack web developer, predictive analytics researcher, and senior data scientist – consultant.
Other career opportunities could include (but not limited to):
- Applied scientist, analytics engineer, augmented/virtual reality, autonomous cars, biometrics and forensics, chief data officer, data platform leadership, data journalist, data and AI technical sales specialist, growth analytics / engineers, manager: AI and cloud services planning, machine learning engineers, product manager: AI and data analytics, product data scientist, product analyst, remote sensing, research assistants, security and surveillance, senior software engineer, and VP data.