Overview
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.
Program learning outcomes
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 variety of machine learning methods, that demonstrate expert understanding, self-evaluation, and advanced skills in communicating highly complex ideas.
Completion requirements
The minimum degree requirements for the Master's of Science in Machine Learning is 35 credits, distributed as follows:
| Core Courses | Number of Courses | Credit Hours |
| Core | 4 | 15 Credit Hours |
| Research Thesis | 1 | 12 Credit Hours |
| Elective Courses | 2 | 8 Credit Hours |
Core courses
MSc 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 COM701, and the other three core courses as a mandatory course.
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 Machine Learning are listed in the table below:
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.
Admission criteria
Bachelor’s degree in a STEM field such as computer science, electrical engineering, computer engineering, mathematics, physics and other relevant science and engineering majors, from a university accredited or recognized by the UAE Ministry of Education (MoE). Students should have a minimum CGPA of 3.2 (on a 4.0 scale) or equivalent.
Applicants must provide their completed degree certificates and transcripts (in English) when submitting their application. Senior-level students can apply initially with a copy of their transcript and expected graduation letter and upon admission must submit the official completed degree certificate and transcript. A degree attestation (for degrees from the UAE) or an equivalency certificate (for degrees acquired outside the UAE) should also be furnished within their first semester at the university.
Each applicant must show proof of English language ability by providing valid certificate copies of either of the following:
- TOEFL iBT with a minimum total score of 90
- IELTS Academic with a minimum overall score of 6.5
- EmSAT English with a minimum score of 1550
TOEFL iBT and IELTS academic certificates are valid for two (2) years from the date of the exam while EmSAT results are valid for eighteen (18) months. Only standard versions (i.e. conducted at physical test centers) of the accepted English language proficiency exams will be considered.
Waiver requests from eligible applicants who are citizens (by passport or nationality) of UK, USA, Australia, and New Zealand who completed their studies from K-12 until bachelor’s degree and master’s degree (if applicable) from those same countries will be processed. They need to submit notarized copies of their documents during the application stage and attested documents upon admission. Waiver decisions will be given within seven days after receiving all requirements.
A general test certificate is optional and submitting one will be considered a plus during the evaluation.
In an 800-word essay, explain why you would like to pursue a graduate degree at MBZUAI and include the following information:
- Motivation for applying to the university
- Personal and academic background and how it makes you suitable for the program you are applying for stand-out achievements, e.g. awards, distinction, etc
- Goals as a prospective student
- Preferred career path and plans after graduation
- Any other details that will support the application
Applicants will be required to nominate referees who can recommend their application. M.Sc. applicants should have a minimum of two (2) referees wherein one was a previous course instructor or faculty/research advisor and the other a previous work supervisor.
To avoid issues and delays in the provision of the recommendation, applicants have to inform their referees of their nomination beforehand and provide the latter's accurate information in the online application portal. Automated notifications will be sent out to the referees upon application submission.
Selected applicants will be invited to participate in an entry exam that will include questions related to the following topics:
- Math: Basic math questions related to calculus, probability theory, linear algebra, and optimization
M.Sc. applicants are recommended to read about these topics especially on how they are related to machine learning. Online research is encouraged, for instance, searching for “linear algebra for machine learning” on YouTube for useful video lectures. In addition, they can find courses on sites like Coursera, Udemy, and many others. Applicants are encouraged to review as many resources as possible. The following website is recommended for mathematics for machine learning: https://mml-book.github.io
- Machine learning: No previous background is needed for M.Sc. applicants.
- Programming
M.Sc. applicants will be asked basic programming questions. Most questions are in Python but the specific language is not a problem since the questions are algorithmic rather than language-specific. Basic understanding about different data structures such as Arrays, Stacks, Queues, etc is important. In addition, it is also important to read about different programming algorithms such as sorting and searching algorithms, and complexity. MSc applicants will be asked questions which require finding the output of a piece of code, finding the problem/error in a short code, and finding the code which performs a specific task.
The exam instructions are available here.
A general admission interview with the Office of Student Affairs will follow.
Study plan
A typical study plan is as follows:
Semester 1
COM701 Research Communication and Dissemination
ML701 Machine Learning
+ 1 Elective
Semester 2
ML702 Advanced Machine Learning
ML703 Probabilistic and Statistical Inference
+ 1 Elective OR
ML699 Master’s Research Thesis
Semester 3
ML699 Master’s Research Thesis
+ 1 Elective (if not taken in Semester 2)
Semester 4
ML699 Master’s Research Thesis
Study Plan
Career prospects
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.
Meet the faculty
Professor Eric Xing
President
and Professor of machine learning
Professor Le Song
Department Chair and Professor,
Machine Learning
Professor Mohsen Guizani
Professor,
Machine Learning
Professor Mérouane Debbah
Professor,
Machine Learning
Professor Najwa Aaraj
Professor
Machine Learning
Dr. Martin Takac
Associate Professor,
Machine Learning
Dr. Kun Zhang
Assistant Professor,
Machine Learning
Dr. Huan Xiong
Assistant Professor,
Machine Learning
Dr. Bin Gu
Assistant Professor,
Machine Learning,
Dr. Qirong Ho
Assistant Professor,
Machine Learning,
Dr. Shangsong Liang
Assistant Professor,
Machine Learning,
Dr. Zhiqiang Xu
Assistant Professor,
Machine Learning,
Dr. Pengtao Xie
Assistant Professor,
Machine Learning,
Disclaimer: Subject to change.