Master of Science in

Assumed knowledge: Linear algebra, mathematical analysis, algorithms. At least intermediate programming skills are necessary. 

Course description: The course gives an introduction to the main topics of modern machine learning such as classification, regression, clustering, and dimensionality reduction. Each topic is accompanied by a survey of key machine learning algorithms solving the problem and is illustrated with a set of real-world examples. The primary objective of the course is to give a broad overview of major machine learning techniques. Particular attention is paid to the modern Python machine learning libraries which allow solving efficiently the problems mentioned above. 

Assumed knowledge: Basics of linear algebra, calculus, probability and statistics, and basic machine learning concepts. Proficiency in Python. 

Course description: This course covers key concepts and methods in deep learning. Students will begin by learning the foundational principles of deep learning, including the universal approximation theorem, strategies for modeling complex patterns using neural architectures, and the specifics of training deep networks. The course then introduces a range of deep models, including convolutional neural networks, recurrent neural networks, and transformer-based architectures. Students will gain hands-on experience in building and training deep neural networks across various domains such as computer vision, medical imaging, and natural language processing. 

Assumed knowledge: Prior to undertaking an internship, students must have successfully completed 24 credit hours. 

Course description: The MBZUAI Internship (up to six weeks) with industry is intended to provide the student with hands-on experience, blending practical experiences with academic learning. 

Course description: The course teaches research methods applicable to scientific research in general, and AI research in particular. It covers various topics including quantitative, qualitative, and mixed methods, measurement and metrics in experimental research, critical appraisal and peer review, public communication, reproducibility and open science, and ethical issues in AI research. Students will gain knowledge in selecting, evaluating, collecting and sharing data and suitable research methods to address specific research questions. After completing the course, students will have the skills to develop a full research methodology that is rigorous and ethical. 

Assumed knowledge: Basics of linear algebra, calculus, trigonometry, and probability and statistics. Proficiency in Python. 

Course description: The course covers the mathematical foundation of robotic systems and introduces students to the fundamental concepts of Robot Operating System (ROS) 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. 

Assumed knowledge: Basics of linear algebra, calculus, and probability and statistics. Proficiency in Python and ROS/Gazebo. 

Prerequisite course/s: ROB701 Introduction to Robotics  

Course description: 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 Robot Operating System (ROS) through dedicated state-of-the-art ROS packages, such as the tf package, AMCL, and mapping. 

Course description: 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 Ph.D. degree. Further, it enables graduates to pursue an industrial project involving a research component independently. 

Assumed knowledge: Basics of linear algebra, calculus, and probability and statistics, Proficiency in Python, Pytorch, 3D geometry and dynamics.  

Course description: Manipulation is crucial for robots in fields such as logistics, manufacturing, and medical surgery, where they can perform dangerous or repetitive tasks. Effective manipulation is based on model-based control strategies, imitation learning, and reinforcement learning. Model-based control strategies include inverse kinematics for accurate positioning, feedforward control to anticipate disturbances, kinematic differential control to adjust for small, real-time changes, and dynamic control to manage forces and torques. Imitation learning enables robots to learn from human experts but may have limitations in generalization. Reinforcement learning refines skills through trial and error, optimizing behavior over time, but often requiring significant computational resources. Each approach has its strengths and limitations, and a combination of these methods is frequently used to achieve optimal performance in real-world applications. 

Course description: 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 one (1) year. Master’s thesis research helps train graduates to pursue more advanced research in their Ph.D. degree. Further, it enables graduates to pursue an industrial project involving a research component independently. 

Course description: The course teaches research methods applicable to scientific research in general, and AI research in particular. It covers various topics including scientific methods, measurement and metrics in experimental research, critical appraisal and peer review, public communication, and ethical issues in AI research. Students will gain knowledge in selecting, evaluating, and collecting data and suitable research methods 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 methodology that is rigorous, entrepreneurial, and ethical.

Assumed knowledge: Prior to undertaking an internship, students must have successfully completed 24 credit hours. 

Course description: The MBZUAI Internship (up to six weeks) with industry is intended to provide the student with hands-on experience, blending practical experiences with academic learning. 

Assumed knowledge: Programming in Python and Jupyter Notebooks. Familiarity with command line and GitHub. Basic artificial intelligence (AI)/machine learning (ML) knowledge. No biology knowledge is expected.  

Course description: 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.  

Assumed knowledge: A background in basic machine learning concepts, as well as general computer organization and digital design. 

Course description: This course examines the intersection of artificial intelligence (AI) and computer systems, focusing on how systems enable efficient AI workloads and how AI techniques enhance system performance. The course covers fundamental principles and emerging trends in system design, hardware-software co-design, resource management, and AI-driven system optimization. Through lectures, hands-on labs, and research projects, students will explore techniques for scaling AI applications, improving system efficiency, and integrating AI into traditional computing environments. By engaging with real-world case studies and cutting-edge frameworks, students will develop the skills needed to contribute to the rapidly evolving field of AI systems in both academia and industry. 

Assumed knowledge: General understanding of mathematical principles used in computing; familiarity with how networks function and communicate; and ability to write and understand simple programs in any programming language. 

Course description: 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. 

Assumed knowledge: Basics of linear algebra, calculus, and probability and statistics. Proficiency in Python. 

Course description: 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. 

Assumed knowledge: Hands-on experience with Python and Pytorch. 

Prerequisite course/s: CV701 Human and Computer Vision  

Course description: 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. 

Assumed knowledge: Basics of linear algebra, calculus, and probability and statistics demonstrated through relevant coursework. Proficiency in Python and Pytorch. 

Prerequisite course/s: CV701 Human and Computer Vision 

Course description: 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. 

Assumed knowledge: Basic concepts in programming. 

Course description: 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, and primary predictive modeling methods, and how to implement and interacts with a digital twin using different platforms. 

Assumed knowledge: Basics of linear algebra, calculus, and probability and statistics. Proficiency in Python. 

Course description: An instructor-led lecture and reading session course focused on both seminal and recent topics in computer vision. The course connects recent trends with classical research works in computer vision. The course covers both fundamental topics (e.g., visual recognition, 3D vision) as well as real-world applications of computer vision (e.g., healthcare, embodied AI, remote sensing, and earth observation). The course is conducted in the form of an instructor-led lecture followed by a reading session, where all participants actively participate in in-depth discussion. The detail reading list provided represents one instance of the course offering and may change in subsequent semesters. 

Assumed knowledge: Basics in linear algebra, calculus, computer vision, machine learning, and probability and statistics, as demonstrated through coursework. Proficient in Python programming and PyTorch  

Prerequisite course/s: AI7102 Introduction to Deep Learning  

Course description: This course provides a comprehensive overview of different concepts and methods related to deep learning for visual computing. The course covers different aspects of deep learning for diverse visual computing applications, including image recognition, segmentation, object detection, image synthesis, point cloud processing.  

Assumed knowledge: Discrete mathematics, and probability and statistics. Proficiency in Java or Python. 

Course description: 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. 

Assumed knowledge: Databases. Proficiency in Java or Python. Basic knowledge of calculus, linear algebra, and probability and statistics. 

Course description: 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.

Course description: This course provides a comprehensive introduction to entrepreneurship in the artificial intelligence (AI) era, focusing on transforming technical expertise into viable business ventures. Students will learn to identify market opportunities, validate ideas, develop prototypes, and communicate their vision effectively. The course emphasizes experiential learning through real-world applications, industry engagement, and culminates in a demo day presentation to investors and industry experts. Students will work in teams to develop and pitch AI-driven startup concepts while learning essential entrepreneurial skills, including design thinking, customer validation, storytelling, and fundraising fundamentals. 

Assumed knowledge: Familiarity with Python programming. Undergraduate course in signal processing or signals and systems. 

Course description: 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. 

Assumed knowledge: Basic concepts in calculus, linear algebra, and programming.

Course description: 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 the basics of learning theory, graphical models, and reinforcement learning.

Assumed knowledge: Basic concepts in calculus, linear algebra and programming, and basic artificial intelligence (AI)/machine learning (ML) knowledge. 

Course description: This course comprehensively introduces 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. 

Assumed knowledge: Basic understanding of machine learning concepts and algorithms. 

Prerequisite course/s: ML701 Machine Learning or  CV701 Human and Computer Vision, or  NLP701 Natural Language Processing 

Course description: This course provides students with a comprehensive introduction to various trust-related issues in artificial intelligence and machine learning applications. Students will learn about attacks against computer systems that use machine learning and defense mechanisms to mitigate such attacks. 

Assumed knowledge: Basic concepts in calculus, linear algebra and programming, and basic artificial intelligence (AI)/machine learning (ML) knowledge. 

Course description: 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. 

Assumed knowledge: Familiarity with fundamental concepts in machine learning and programming. 

Prerequisite course/s: MTH7101 Mathematical Foundations of AI  

Course description: 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. 

Assumed knowledge: Familiarity with fundamental concepts in probability, linear algebra, statistics, and programming. 

Prerequisite course/s: MTH7101 Mathematical Foundations of AI 

Course description: 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 modeling, data-oriented strategies, and explicit use of designs and randomization in analyses. Furthermore, it will provide in-depth treatment to the broad theories (frequentists and Bayesian) and numerous practical complexities for performing inference. This course presents the fundamentals of statistical and probabilistic inference and shows how these fundamental concepts are applied in practice. 

Assumed knowledge: Students enrolling in this course should possess proficiency with high-school–level algebra and trigonometry, along with an introductory treatment of single-variable calculus (limits, basic differentiation, and elementary integration). Familiarity with manipulating simple vectors and matrices—as encountered in a first-year linear algebra or applied mathematics course—is beneficial but not strictly required; key concepts will be refreshed before deeper exploration. No prior coursework in probability or statistics is assumed.  

Course description: This seven-week course equips students with the essential mathematical tools required for modern artificial intelligence and machine learning study. Topics include single- and multivariate calculus (limits, derivatives, gradients, Jacobians, Hessians), linear algebra (vectors, matrices, eigenvalues, decompositions), and probability and statistics (discrete and continuous distributions, Bayes’ theorem, expectation, covariance, law of large numbers, central limit theorem, maximum-likelihood estimation). Emphasis is placed on conceptual understanding, geometric intuition, and hands-on problem solving that link theory to common AI algorithms such as optimization and probabilistic inference. 

Assumed knowledge: Basic concepts in linear algebra, calculus, and probability and statistics. Programming in Python or similar language. 

Course description: 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. 

Assumed knowledge: Basic concepts in linear algebra, calculus, and probability and statistics. Programming in Python or a similar language. At least intermediate knowledge of PyTorch. 

Prerequisite course/s: NLP701 Natural Language Processing  

Course description: This course provides a methodological and an in-depth background on key core natural language processing (NLP) areas based on deep learning. It builds upon fundamental concepts in NLP integrating advances on large language models (LLMs). It assumes familiarity with mathematical and machine learning concepts and programming. 

Assumed knowledge: Basic concepts in linear algebra, calculus, and probability and statistics. Programming in Python or a similar language. 

Prerequisite courses/: NLP701 Natural Language Processing  

Course description: 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. 

Assumed knowledge: Basics of linear algebra, calculus, and probability and statistics. Proficiency in Python and PyTorch. 

Prerequisite course/s: ROB701 Introduction to Robotics 

Course description: 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 accomplishes a given task). We cover both the fundamental and advanced RL algorithms and discuss their advantages and disadvantages in various robotics settings.