Master of Science in Machine Learning - Artificial Intelligence
Abu Dhabi, United Arab Emirates
DURATION
2 Years
LANGUAGES
English
PACE
Full 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
The scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on patterns and inference instead. These algorithms are based on mathematical models learned automatically from data, thus allowing machines to intelligently interpret and analyze input data to derive useful knowledge and arrive at important conclusions. Machine learning is heavily used for enterprise applications (e.g., business intelligence and analytics), effective web search, robotics, smart cities, and understanding of the human genome.
Alumni Statistics
Admissions
Curriculum
The minimum degree requirements for the Master of Science in Machine Learning is 36 credits, distributed as follows:
| Core courses | Number of courses | Credit hours |
| Core | 4 | 16 |
| Electives | 2 | 8 |
| Research thesis | 1 | 12 |
| Internship | At least one internship of up to six weeks duration must be satisfactorily completed as a graduation requirement | 0 |
Core courses
The Master of Science in Machine Learning is primarily a research-based degree. The purpose of coursework is to equip students with the right skill set, so they can successfully accomplish their research project (thesis). Students are required to take AI701, MTH701 and ML701 as mandatory courses. They can select either ML702 or ML703 along with two electives.
| Code | Course Title | Credit Hours |
| AI701 | Foundations of Artificial Intelligence | 4 |
| MTH701 | Mathematical Foundations of Artificial Intelligence | 4 |
| ML701 | Machine Learning | 4 |
| ML702 | Advanced Machine Learning | 4 |
| ML703 | Probabilistic and Statistical Inference | 4 |
Elective Courses
Students will select a minimum of two elective courses, with a total of eight (or more) credit hours. One must be selected from List A and one must be selected from List A or B based on interest, proposed research thesis, and career aspirations, in consultation with their supervisory panel. The elective courses available for the Master of Science in Machine Learning are listed in the tables below:
List A
| Code | Course Title | Credit Hours |
| ML702 | Advancing Machine Learning | 4 |
| ML703 | Probabilistic and Statistical Inference | 4 |
| ML704 | Machine Learning Paradigms | 4 |
| ML705 | Topics in Advanced Machine Learning | 4 |
| ML706 | Advanced Probabilistic and Statistical Inference | 4 |
List B
| Code | Course Title | Credit Hours |
| AI702 | Deep Learning | 4 |
| CV701 | Human and Computer Vision | 4 |
| CV702 | Geometry for Computer Vision | 4 |
| CV703 | Visual Object Recognition and Detection | 4 |
| CV707 | Digital Twins | 4 |
| DS701 | Data Mining | 4 |
| DS702 | Big Data Processing | 4 |
| HC701 | Medical Imaging: Physics and Analysis | 4 |
| ML707 | Smart City Services and Applications | 4 |
| ML708 | Trustworthy Artificial Intelligence | 4 |
| MTH702 | Optimization | 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 |
| ML699 | Machine Learning Master’s Research Thesis | 12 |
| Research Training | 0 |
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 highly specialized understanding of the modern machine learning pipeline: data, models, algorithmic principles, and empirics
- Achieve advanced skills in data-preprocessing and using various exploration and visualization tools
- Demonstrate critical awareness of the capabilities and limitations of the different forms of learning algorithms
- Obtain advanced capabilities to critically analyze, evaluate, and continuously improve the performance of learning algorithms
- Acquire advanced abilities to analyze computational and statistical properties of advanced learning algorithms and their performance
- Gain expertise in using and deploying machine learning-relevant programming tools for a variety of complex machine learning problems
- Develop advanced problem-solving skills through independently applying machine learning methods to multiple complex problems, and demonstrate expertise in dealing with ambiguity in a problem statement
- Apply sophisticated skills in initiating, managing, and completing multiple project reports and critiques on a variety of machine learning methods, that demonstrate expert understanding, self-evaluation, and advanced skills in communicating highly complex ideas
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.