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 giving 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. 

Course description: 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. 

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: Familiarity with fundamental concepts in Probability, Linear Algebra, Statistics, and Programming. 

Prerequisite course: MTH7101 – Mathematical Foundations of Artificial Intelligence 

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 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 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: 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. 

Course description: 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 (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: 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.

Course description: 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. 

Assumed knowledge: Knowledge of operating systems design and implementation.  

Prerequisite courses:  CS7101 Algorithms and Data Structures 

Course description: 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. 

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

Course description: 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. 

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: Discrete Mathematics, 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 on calculus, linear algebra, 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 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 fundamental concepts in machine learning and programming. 

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 affect the performance of parallel ML programs. 

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

Prerequisite course: 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, probability, and statistics. Programming in Python or similar language. 

Prerequisite course: 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, trigonometry, 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 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. 

Course description: 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 (1) 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 independently.

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.