Master's in

Robotics

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Mode
Full-time

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Credits
36

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Location
On-campus

Overview

The aims of the Master’s in Robotics are (1) to develop students’ interest in, knowledge and understanding of robotics and autonomous systems, and (2) to prepare them for Ph.D. research in that area and/ or the industry workforce. The program teaches students to apply the research techniques and knowledge they have gained to solve complex problems in the field of Robotics.
Robotics research and the development of intelligent systems continue to be one of the key priorities set by both government and industry. Interdisciplinary in scope, our Master’s in Robotics provides an ideal foundation for what today’s experts in robotics and intelligent systems need to know. Along with the chance to learn from world leaders in their fields from across the globe, our program offers hands-on activities where you will learn by designing, prototyping, and validating intelligent robotic systems. As a graduate of the Master’s in Robotics program, you will take a leading role in the development of integrated robotics technologies and systems, both locally and internationally.

overview

Program learning outcomes

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

  1. Discuss and explain concepts and key components of robotics technologies
  2. Compare and contrast various robot sensors and their perception principles that enable a robot to analyze their environment, reason and take appropriate actions toward the given goal
  3. Analyze and solve problems in spatial transformation, robot locomotion, kinematics, motion control, localization and mapping, navigation, and path planning
  4. Critically appraise current research literature and conduct rigorous and situationally appropriate experiments with state-of-the-art robotic algorithms on a robotic platform
  5. Effectively communicate robotics concepts and design decisions using a range of media/visual mediums
  6. Function effectively in or lead a team, that creates a collaborative and inclusive environment, establishes research goals, plans tasks, and meets desired objectives

Completion requirements

The minimum degree requirements for the Master of Science in Robotics is 36 credits, distributed as follows:

Core Courses Number of Courses Credit Hours
Core 4 16
Electives 2 8
Internship At least one internship of 6 weeks in length and align with the working hours of the host organization. The internship does not have to directly align with the student’s research at MBZUAI. 2
Introduction to Research Methods 1 2
Research Thesis 1 8

Core courses

Master of Science in Robotics 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, ROB701 and ROB702 and ROB703 as mandatory courses.

Code Course Title Credit Hours
AI701 Foundations of Artificial Intelligence

This course provides 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
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
ROB702 Robotic Vision and Intelligence

Robots must be able to sense and learn from experience to achieve autonomy. The most frequently used sensing technique is vision. We will explore both the fundamental techniques used in image processing and computer vision analysis (localize objects, recognize objects, segment images) together with advanced tools that allow robots to estimate the motion of objects, estimate depth or reconstruct 3D scenes from a set of images. To give robots the ability to learn, we will explore Reinforcement Learning (RL). RL is a subfield of ML that is inspired by how humans learn. The RL agent interacts with its environment, observes the impact of its actions, and receives rewards (positive or negative depending on how well it accomplished a given task). We cover both the fundamental and advanced RL algorithms and discuss their advantages and disadvantaged in various robotics settings.

4
ROB703 Robot Localization and Navigation

The course covers different topics and techniques within the context of mapping, localization, and navigation. It highlights SLAM methods using various types of filters, such as Kalman filter, Extended Kalman filter (EKF) and Particle filter. It investigates grid- and graph-based SLAM and data association. It puts in perspective map-based and reactive navigation techniques. To reinforce these concepts and methods, they are applied within ROS (Robot Operating System) through dedicated state-of -the-art ROS packages, such as the tf package, AMCL, and mapping.

4

Elective courses

Students will select a minimum of two elective courses, with a total of eight (or more) credit hours.  The electives 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 Robotics are listed in the table below:

Code Course Title Credit Hours
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
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 the two classic problems in analysis of medical images: segmentation and registration.

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
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 an 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
ML709 IoT Smart Systems, 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
MTH701 Mathematical Foundations of 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
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.

4
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.

4
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.

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
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
ROB799 Master’s Research Thesis

Master's thesis research exposes students to an unsolved research problem, for which 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.

8

Internship

The MBZUAl internship with industry is intended to provide the student with hands-on experience, blending practical experiences with academic learning.

Code Course Title Credit Hours
INT799 M.Sc. Internship (up to six weeks)

M.Sc. Internship (up to six weeks)

2

Admission criteria

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.

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:

 

  • 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: 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.

 

Study plan

A typical study plan is as follows:

Semester 1

AI701 Foundations of Artificial Intelligence
ROB701 Fundamentals of Robotics
One elective from the list

Semester 2

ROB702 Robotics Vision and Intelligence
ROB703 Robot Localization and Navigation
+ One elective from the list

Summer

INT799 Internship

Semester 3

RES799 Introduction to Research Methods
ROB799 Master’s Research Thesis

Semester 4

ROB799 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.

Disclaimer: Subject to change.

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