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.
The Machine Learning (ML) Department at MBZUAI is dedicated to imparting a world-class education in ML to our students. From foundational principles to advanced applications, our research-intensive education model will provide our students theoretical concepts to test under supervision from senior AI researchers in the field as they tackle real-world problems and produce meaningful results.
Acting Chair of Machine Learning, Professor of Machine Learning, and Director of Center for Integrative Artificial Intelligence (CIAI)
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Explain the modern machine learning pipeline: data, models, algorithmic principles, and empirics.
Employ data-preprocessing and various exploration and visualization tools.
Identify and differentiate the capabilities and limitations of the different forms of learning algorithms.
Critically analyze, evaluate, and continuously improve the performance of the learning algorithms.
Analyze computational and statistical properties of advanced learning algorithms and their performance.
Apply and deploy ML-relevant programming tools for a variety of complex ML problems.
Problem-solve through independently applying machine learning methods to multiple. often ambiguous, complex problems.
Apply sophisticated skills in initiating, managing, completing, and communicating multiple project reports, highly complex ideas, and critiques on a variety of machine learning methods using innovative and sustainable approaches.
The minimum degree requirements for the Master of Science in Machine Learning is 36 credits, distributed as follows:
Number of Courses | Credit Hours | |
---|---|---|
Core | 4 | 16 |
Electives | 2 | 8 |
Internship | At least one internship of up to six weeks duration must be satisfactorily completed as a graduation requirement | 2 |
Introduction to Research Methods | 1 | 2 |
Research Thesis | 1 | 8 |
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, ML701, and ML703 as mandatory courses.
Course Title | Credit Hours | |
---|---|---|
AI701 |
Foundations of Artificial Intelligence
This course provides the students a comprehensive introduction to artificial intelligence. It builds upon fundamental concepts in machine learning. Students will learn about supervised and unsupervised learning, various learning algorithms, and the basics of the neural network, deep learning, and reinforcement learning. |
4 |
MTH701 |
Mathematical Foundations for Artificial Intelligence
This course provides a comprehensive mathematical foundation for the field of artificial intelligence. It builds upon fundamental concepts in linear algebra, probability theory, statistics, and calculus. Students will learn how these mathematical concepts can be used to solve problems frequently encountered in AI applications. |
4 |
ML701 |
Machine Learning
This course provides a comprehensive introduction to machine learning. It builds upon fundamental concepts in mathematics, specifically probability and statistics, linear algebra, and calculus. Students will learn about supervised and unsupervised learning, various learning algorithms, and basics of learning theory, graphical models, and reinforcement learning. |
4 |
AND
This course provides a comprehensive mathematical foundation for the field of artificial intelligence. It builds upon fundamental concepts in linear algebra, probability theory, statistics, and calculus. Students will learn how these mathematical concepts can be used to solve problems frequently encountered in AI applications. |
||
ML702 |
Advanced Machine Learning
This course focuses on recent advances in machine learning and on developing skills for performing research to advance the state of the art in machine learning. Students will learn concepts in kernel methods, statistical complexity, statistical decision theory, computational complexity of learning algorithms, and reinforcement learning. This course builds upon concepts from Machine Learning (ML701) and assumes familiarity with fundamental concepts in machine learning, optimization, and statistics. |
4 |
OR | ||
ML703 |
Probabilistic and Statistical Inference
Probabilistic and statistical inference is the process of drawing useful conclusions about data populations or scientific truths from uncertain and noisy data. This course will cover different modes of performing inference including statistical modelling, data-oriented strategies, and explicit use of design and randomization in analyses. Furthermore, it will provide an in-depth treatment of the broad theories (frequentists, Bayesian, likelihood) and numerous practical complexities (missing data, observed and unobserved confounding, biases) for performing inference. This course presents the fundamentals of statistical and probabilistic inference and shows how these fundamental concepts are applied in practice. |
4 |
Students will select a minimum of two elective courses, with a total of eight (or more) credit hours. One must be selected from list 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:
Course Title | Credit Hours | |
---|---|---|
AI702 |
Deep Learning
This course provides a comprehensive overview of different concepts and methods related to deep learning. Students will first learn the foundations of deep learning, after which they will be introduced to a series of deep models: convolutional neural networks, autoencoders, recurrent neural network, and deep generative models. Students will work on case studies of deep learning in different fields such as computer vision, medical imaging, natural language processing, etc. |
4 |
CB703 |
Introduction to Single Cell Biology and Bioinformatics
This course provides a broad overview of bioinformatics for single cell omics technologies, a new and fast-growing family of biological assays that enables measuring the molecular contents of individual cells with very high resolution and is key to advancing precision medicine. The course starts with an accessible introduction to basic molecular biology: the cell structure, the central dogma of molecular biology, the flow of biological information in the cell, the different types of molecules in the cell, and how we can measure them. This course then introduces students to the diverse landscape of biological data, including its types and characteristics and explores the foundational principles of single-cell omics bioinformatics, encompassing key methodologies, tools, and computational workflows, with an emphasis on the development of foundation models for single cell omics data. |
4 |
CS721 |
Computer and Network Security
This course provides an overview of foundational principles and contemporary topics in information security. Students will examine system protection strategies, structural security frameworks, software resilience, and detection of security threats. The course integrates theoretical concepts with practical applications to enhance the understanding of securing complex information systems. |
4 |
CV701 |
Human and Computer Vision
This course provides a comprehensive introduction to the basics of human visual system and color perception, image acquisition and processing, linear and nonlinear image filtering, image features description and extraction, classification, and segmentation strategies. Moreover, students will be introduced to quality assessment methodologies for computer vision and image processing algorithms. |
4 |
CV702 |
Geometry for Computer Vision
The course provides a comprehensive introduction to the concepts, principles and methods of geometry-aware computer vision which helps in describing the shape and structure of the world. In particular, the objective of the course is to introduce the formal tools and techniques that are necessary for estimating depth, motion, disparity, volume, pose and shapes in 3D scenes. |
4 |
CV703 |
Visual Object Recognition and Detection
This course provides a comprehensive overview of different concepts and methods related to visual object recognition and detection. In particular, the students will learn a large family of successful and recent state-of-the-art architectures of deep neural networks to solve the tasks of visual recognition, detection, and tracking. |
4 |
CV707 |
Digital Twins
This course provides a comprehensive introduction to digital twins. Students will learn about digital twin technology, its common applications, and benefits, how to create a digital twin for predictive analytics using sensory data fusion, primary predictive modeling methods and how to implement and interacts with a digital twin using different platforms. |
4 |
DS701 |
Data Mining
This course is an introductory course on data mining, which is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. |
4 |
DS702 |
Big Data Processing
This course is an introductory course on big data processing, which is the process of analyzing and utilizing big data. The course involves methods at the intersection of parallel computing, machine learning, statistics, database systems, etc. |
4 |
DS703 |
Information Retrieval
This course is an introductory course on Information Retrieval (IR). The explosive growth of available digital information (e.g., Web pages, emails, news, Tweets, Wikipedia pages) demands intelligent information agents that can sift through all available information and find out the most valuable and relevant information. Web search engines, such as Google and Bing, are several examples of such tools. This course studies the basic principles and practical algorithms used for information retrieval and text mining. It will cover algorithms, design, and implementation of modern information retrieval systems. Topics include: retrieval system design and implementation, text analysis techniques, retrieval models (e.g., Boolean, vector space, probabilistic, and learning-based methods), search evaluation, retrieval feedback, search log mining, and applications in web information management. |
4 |
DS704 |
Statistical aspect of Machine Learning/Statistical Theory
This course covers the fundamentals of theoretical statistics, which are the foundation for the analysis of the properties of machine learning algorithms. Covered topics include statistical models, statistical inference, maximum likelihood estimation, optimal hypothesis testing, decision theory and Bayesian inference, non-parametric statistics, and Bootstrap, (generalized) linear model and high dimensional statistics. All necessary tools from Probability theory: deviation inequalities, type of convergence, law of large numbers, central limit theorem, properties of the Gaussian distribution (etc) will be introduced whenever needed and their proofs given at the end of each chapter. |
4 |
HC701 |
Medical Imaging: Physics and Analysis
This course provides a graduate-level introduction to the principles and methods of medical imaging, with thorough grounding in the physics of the imaging problems. This course covers the fundamentals of X-ray, CT, MRI, ultrasound, and PET imaging. In addition, the course provides an overview of 3D geometry of medical images and a few classical problems in medical images analysis including classification, segmentation, registration, quantification, reconstruction and radiomics. |
4 |
ML702 |
Advanced Machine Learning
This course focuses on recent advances in machine learning and on developing skills for performing research to advance the state of the art in machine learning. Students will learn concepts in kernel methods, statistical complexity, statistical decision theory, computational complexity of learning algorithms, and reinforcement learning. This course builds upon concepts from Machine Learning (ML701) and assumes familiarity with fundamental concepts in machine learning, optimization, and statistics. |
4 |
ML703 |
Probabilistic and Statistical Inference
Probabilistic and statistical inference is the process of drawing useful conclusions about data populations or scientific truths from uncertain and noisy data. It is the foundation and an essential component of machine learning since machine learning aims to learn and improve from experience (which is represented by data). This course will cover the different modes of performing inference, including statistical modelling, data-oriented strategies, and explicit use of designs and randomization in analyses. Furthermore, it will provide in-depth treatment to the broad theories (frequentists, Bayesian, likelihood) and numerous practical complexities (missing data, observed and unobserved confounding, biases) for performing inference. This course presents the fundamentals of statistical and probabilistic inference and shows how these fundamental concepts are applied in practice. |
4 |
ML707 |
Smart City Services and Applications
This course provides a comprehensive introduction to using AI/ML in smart city services and applications. The course will start by reviewing basic concepts. Students will learn how to apply AI/ML to develop, design and improve smart city services. They will be able to demonstrate an understanding of the smart city concept, applications, requirements, and system design. They will develop capabilities of integrating emerging technologies in smart city components and be able to implement them. In addition, they will gain knowledge in applying security, data analytics, Internet of Things (IoT), communications and networking and work on case studies solutions for smart city infrastructures. |
4 |
ML708 |
Trustworthy Artificial Intelligence
This course provides students with a comprehensive introduction to various trust-related issues in applications of artificial intelligence and machine learning. Students will learn about attacks against computer systems that use machine learning, as well as defense mechanisms to mitigate such attacks. |
4 |
ML709 |
IoT of things, Services and Applications
This course provides a comprehensive introduction to using AI/ML in Internet of Things (IoT) smart systems, services and applications. The course will start by reviewing advanced concepts. Students will learn how to apply AI/ML to develop, design and improve IoT systems and services. They will be able to demonstrate an understanding of IoT concepts, applications, requirements and system design. They will develop capabilities of integrating emerging technologies in smart IoT components and be able to implement them. In addition, they will gain knowledge and skills in applying security, data analytics, AI models, communications and networking and work on case studies solutions for IoT infrastructures. |
4 |
ML710 |
Parallel and Distributed Machine Learning Systems
As Machine Learning (ML) programs increase in data and parameter size, their growing computational and memory requirements demand parallel and distributed execution across multiple network-connected machines. In this course, students will learn the fundamental principles and representations for parallelizing ML programs and learning algorithms. Students will also learn how to design and evaluate (using standard metrics) and compare between complex parallel ML strategies composed out of basic parallel ML “aspects” and evaluate and compare between the architecture of different software systems that use such parallel ML strategies to execute ML programs. Students will also use standard metrics to explain how compilation and resource management affects the performance of parallel ML programs. |
4 |
ML711 |
Intermediate Music AI
What is sound and music from a computer science perspective? How can we use AI and ML to better appreciate, perform, and compose music? When music meets computer science, could computers generate something truly creative by closing the loop of analysis and synthesis? Could computers interact with our humans in real time and offer us some new experience? Let’s explore the possibilities in this course. It is a Music AI course, but most of the content is orthogonal to programming or traditional computer science. If you are a great computer science student or even a great programmer, you will be able to use your special skills in this class to your advantage. On the other hand, if you are a musician with intro-level programming skills, you can get by without writing a lot of difficult programs. Your musical knowledge and intuition will also be of great value. Students will learn the fundamentals of digital audio, basic sound synthesis algorithms, techniques for human-computer music interaction, and most importantly , machine learning algorithms for media generation. In a final project, students will demonstrate their mastery of tools and techniques through a publicly performed music composition. |
4 |
MTH702 |
Optimization
This course provides a graduate-level introduction to the principles and methods of optimization, with a thorough grounding in the mathematical formulation of optimization problems. The course covers fundamentals of convex functions and sets, first order and second order optimization methods, problems with equality and/or inequality constraints, and other advanced problems. |
4 |
NLP701 |
Natural Language Processing
This course provides a comprehensive introduction to natural language processing (NLP). It builds upon fundamental concepts in mathematics, specifically probability and statistics, linear algebra, and calculus, and assumes familiarity with programming. |
4 |
NLP702 |
Advanced Natural Language Processing
This course provides a comprehensive introduction to natural language processing (NLP). It builds upon fundamental concepts in NLP and assumes familiarization with mathematical concepts and programming. |
4 |
NLP703 |
Speech Processing
This course provides a comprehensive introduction to speech processing. It builds upon fundamental concepts in speech processing and assumes familiarization with mathematical and signal processing concepts. |
4 |
ROB701 |
Introduction to Robotics
The course covers the mathematical foundation of robotic systems and introduces students to the fundamental concepts of ROS (Robot Operating System) as one of the most popular and reliable platforms to program modern robots. It also highlights techniques to formally model and study robot kinematics, dynamics, perception, motion control, navigation, and path planning. Students will also learn the interface of different types of sensors, read and analyze their data, and apply it in various robotic applications. |
4 |
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.
Course Title | Credit Hours | |
---|---|---|
ML699 |
Machine Learning Master’s 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. Master’s thesis research helps train graduates to pursue more advanced research in their Ph.D. degree. Further, it enables graduates to independently pursue an industrial project involving research component. |
8 |
RES799 |
Introduction to Research Methods
This course focuses on teaching students how to develop innovative research-based approaches that can be implemented in an organization. It covers various research designs and methods, including scientific methods, ethical issues in research, measurement, experimental research, survey research, qualitative research, and mixed methods research. Students will gain knowledge in selecting, evaluating, and collecting data to address specific research questions. Additionally, they will learn design thinking skills to connect their research-based topic to practicality. After completing the course, students will have the skills to develop a full research topic that can be innovative, entrepreneurial, and sustainable and can be applied in any organization related to the topic of research. |
2 |
The MBZUAl internship with industry is intended to provide the student with hands-on experience, blending practical experiences with academic learning.
Course Title | Credit Hours | |
---|---|---|
INT799 |
M.Sc. Internship (up to six weeks)
M.Sc. Internship (up to six weeks) |
2 |
MBZUAI accepts applicants from all nationalities who hold a completed Bachelor’s degree in a STEM field such as Computer Science, Electrical Engineering, Computer Engineering, Mathematics, Physics or other relevant Science or Engineering major from a university accredited or recognized by the UAE Ministry of Education (MoE) with a minimum CCGPA of 3.2 (on a 4.0 scale) or equivalent.
Applicants must provide their completed degree certificates and official transcripts when submitting their application. Senior-level students can apply initially with a copy of their official transcript and expected graduation letter and upon admission must submit the official completed degree certificate and transcript. A degree attestation from UAE MoE (for degrees from the UAE) or Certificate of Recognition from UAE MoE (for degrees acquired outside the UAE) should also be furnished within students’ first semester at MBZUAI.
All submitted documents must either be in English, originally, or include legal English translations.
Additionally, official academic documents should be stamped and signed by the university authorities.
Each applicant must show proof of English language ability by providing valid certificate copies of either of the following:
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 (7) days after receiving all requirements.
Submission of GRE scores is optional for all applicants but will be considered a plus during the evaluation.
In a 500- to 1000-word essay, explain why you would like to pursue a graduate degree at MBZUAI and include the following information:
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 current or 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.
All applicants with complete files, including the required number of recommendations, will be invited to participate in an online screening exam to assess their knowledge and skills. Completion of the exam is not mandatory but highly encouraged as it would provide additional information to the evaluation committee. Waiving the exam is only recommended for those students who can provide strong evidence of their research capability, subject matter expertise, and technical skills.
Exam Topics
Math: Calculus, probability theory, linear algebra, trigonometry and optimization
Programming: Knowledge surrounding specific programming concepts and principles such as algorithms, data structures, logic, OOP, and recursion as well as language–specific knowledge of Python
Applicants are highly encouraged to complete the following online courses to further improve their qualifications :
The exam instructions are available here
A select number of applicants may be invited to an interview with faculty as part of the screening process. The time and instructions for this will be communicated to applicants on timely bases.
Only one application per admission cycle must be submitted; multiple submissions are discouraged.
Application portal opens | Regular deadline | Decision notification date | Late deadline |
---|---|---|---|
1st October 2024 (8:00 AM UAE time) |
15th January 2025 (5:00 PM UAE time) |
31st March 2025 (5:00 PM UAE time) |
31st May 2025 (5:00 PM UAE time) |
High-calibre applicants who apply by the ‘Regular Deadline’ and have complete applications (including the required recommendations) will be given full consideration. | The online application portal will remain open until the ‘Late Deadline’. We do not guarantee that these late applications will be given full consideration. |
Detailed information on the application process and scholarships is available here.
A typical study plan is as follows:
SEMESTER 1 AI701 Foundations of Artificial IntelligenceDisclaimer: Subject to change.
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