Overview

The goals of the Master of Science in Computer Science are to train specialists to (1) analyze complex computer science and AI problems, (2) take a scientific, innovative, ethical, and socially responsible approach to conducting and contributing to computer science research, and (3) solve complex problems in the field.

As technological progress accelerates, so does the demand for skilled computer science professionals. The Master of Science in Computer Science is intended for students desiring to substantially advance their knowledge and skill in a field or fields of computer science. You will be supervised and mentored by faculty members with world-class expertise in a variety of areas in computer science, including algorithms, systems, and computational intelligence. This master’s program is ideally suited to students wishing to become senior professionals in the technology industry or to those seeking to prepare for a career in scientific research.

By the end of the program students will be able to:

Analyze real-world problems and apply principles of computer science and other relevant disciplines to meet desired needs

Analyze and prove the properties of data structures, algorithms and/or computing systems using the theoretical underpinnings of Computer Science

Identify and apply mathematical foundations, algorithmic principles, and computer science theory in the modelling and design of computer-based systems

Function effectively as a member or leader of a team engaged in computer science projects and research of varying complexity

Communicate the practical and entrepreneurial feasibility and sustainability of research findings and innovations, orally and in written form, to both specialist and general audiences

Internship At least one internship of up to six weeks duration must be satisfactorily completed as a graduation requirement. Introduction to Research Methods Research Thesis Mathematics for Computer Science

This course covers widely applicable mathematical tools for computer science, including topics from graph theory, probability theory, information theory, and logic. It includes practice in reasoning formally and proving theorems.

CS701 Advanced Algorithms and Data Structures

We study techniques for the design of algorithms (such as dynamic programming) and algorithms for fundamental problems (such as fast Fourier transform FFT). In addition, we explore computational intractability, specifically, the theory of NP-completeness. The key topics covered in the course are: dynamic programming; divide and conquer, including FFT; randomized algorithms, including RSA cryptosystem; graph algorithms; max-flow algorithms; linear programming; and NP-completeness.

CS702 Theory of Computer Science

This course uncovers the science behind computing by studying computation abstractly without involving any specifics of programming languages and/or computing platforms. Specifically, it studies finite automata which capture what can be computed using constant memory, the universal computational model of Turing machines, the inherent limits of what can be solved on a computer (undecidability), the notion of computational tractability, and the P vs NP question. Finally, the course also involves Boolean circuits, cryptography, polynomial hierarchy, rigorous thinking and mathematical proofs.

CS703 Operating Systems

This course discusses the advanced concepts in operating system design and implementation. The operating system provides a convenient and efficient interface between user programs and the hardware of the computer on which they run.

Elective courses

Students will select a minimum of two elective courses, with a total of eight (or more) credit hours. They must be selected from the 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 Computer Science are listed in the table below:

Programming Languages and Implementation

This course aims at uncovering the fundamental principles of programming language design, semantics, and implementation.

CS705 Distributed and Parallel Computing

Parallel and distributed systems are ubiquitous in many applications in our daily life including AI, online games, social networks, web services and healthcare simulations. These systems distribute computation over many computing units because they must sustain massive workloads that cannot fit into a single computer. Designing efficient, easy-to-maintain and correct parallel and distributed systems is challenging. In this course, we specifically study distributed computing, consistency, remote procedure calls, logging, recovery, and MapReduce. Further, we will cover instruction-level parallelism, parallel programming, cache coherence, memory consistency, and synchronization implementation.

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.

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.

NLP701 Natural Language Processing

This course provides a comprehensive introduction to Natural Language Processing. It builds upon fundamental concepts in Mathematics, specifically probability and statistics, linear algebra, and calculus, and assumes familiarity with programming.

NLP702 Advanced Natural Language Processing

This course provides a methodological and an in-depth background on key core Natural Language Processing areas based on deep learning. It builds upon fundamental concepts in Natural Language Processing and assumes familiarization with mathematical and machine learning concepts and programming.

NLP703 Speech Processing

This course provides a comprehensive introduction to Speech Processing. It builds upon fundamental concepts in Speech Processing and assumes familiarity with Mathematical and Signal Processing concepts.

ROB701 Fundamentals of 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.

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.

CS799 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 1 year. Master's thesis research helps train graduates to pursue more advanced research in their PhD degree. Further, it enables graduates to pursue an industrial project involving a research component independently.

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 official 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 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 (7) days after receiving all requirements.

In a 500- to 10 00-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

Experience in completing a diverse range of projects related to artificial intelligence

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 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 : C alculus, p robability t heory, l inear a lgebra, t rigonometry and o ptimization

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

Specialization topics: Knowledge and understanding of the theory of computation, computational complexity, databases, computer architecture and operating systems

Applicants are highly encouraged to complete the following online courses to further improve their qualifications :

https://www.coursera.org/learn/python?specialization=python

https://www.coursera.org/learn/python-data?specialization=python

https://www.coursera.org/learn/linear-algebra-machine-learning?specialization=mathematics-machine-learning

https://www.coursera.org/learn/introductiontoprobability

The exam instructions are available here

A typical study plan is as follows:

Semester 1

CS701 Advanced Algorithms and Data Structures
MTH703 Mathematics for Computer Science
CS702 Theory of Computer Science

Semester 2

CS703 Operating Systems
Two electives from the list

Summer

INT799 Masters Internship

Semester 3

RES799 Introduction to Research Methods
CS799 Master’s Research Thesis

Semester 4

CS799 Master’s research thesis

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.