Master of Science in

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 interdisciplinary course is designed for students and professionals with a background in machine learning who seek to understand the fundamentals of molecular biology. The course explores key biological concepts and their applications to machine learning, particularly in the fields of bioinformatics, genomics, and computational biology. By bridging the gap between molecular biology and machine learning, students will gain the knowledge to apply machine learning techniques to biological data and solve complex problems in medicine, genetics, and drug discovery.

Course description: Computational biology has become an important discipline in the intersection of computing, mathematics, biology, and medicine. Biological datasets produced by modern biotechnologies are large and hence they can only be understood by using mathematical modeling and computational techniques. Starting from analysis of genetic sequences, the field has progressed towards analysis and modeling of entire biological systems. A way of abstracting the vast amount of biomedical information is by modeling and analyzing these data by using networks (or graphs). Such approaches have been used to model phenomena in other research domains, apart from computational and systems biology and medicine.

Course description: This interdisciplinary graduate course introduces students to the core concepts, tools, and emerging methods in computational genomics and epigenomics. It is designed for students from biology, computer science, machine learning, and related fields who are interested in applying their skills to cutting-edge questions in biology and medicine. Students will explore how genomic and epigenomic data, such as DNA, RNA, chromatin accessibility, and methylation, are generated and analyzed using statistical and machine learning techniques. The course covers foundational topics such as genome organization, gene regulation, and high-throughput sequencing technologies. It extends to advanced applications including multi-omic data integration, deep learning for genomics, and cancer omics.

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.

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, 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: 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: This graduate course aims to inculcate a deeper understanding of advanced machine learning methods, so the students are capable of researching, developing, and implementing these methods for solving real-world problems. This course will cover advanced topics in statistical machine learning, supervised and unsupervised learning, high-dimensional statistics, deep neural networks, reinforcement learning, and classical learning methods.

Course description: This graduate-level course aims to inculcate a deeper understanding of advanced machine learning methods, so the students are capable of researching, developing, and implementing these methods for solving real-world problems. This course will aim to instill in students a strong grasp of the following topics: large scale training of kernel methods, sparse learning, bilevel optimization, black box optimization, and spiking neuralnNetworks. Additionally, a goal of this course is to enhance students’ teamwork skills by requiring them to participate in group projects.

Course description: This course aims to equip graduate students with the knowledge and skills required to analyze, interpret, and integrate large-scale genomic and epigenomic data using computational approaches. Students will learn the principles of genome organization, gene regulation, and epigenetic mechanisms, and how these are measured through modern sequencing technologies. Emphasis is placed on applying statistical, machine learning, and deep learning methods—including recent advances in foundation models—to uncover patterns in biological data and make functional predictions. Through hands-on practice with real-world datasets, reproducible workflows, and project-based learning, students will gain experience working across multiple omics layers and addressing research questions at the intersection of computation, biology, and precision medicine.

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

Prerequisite courses/: 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: 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 of 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.

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

Course description: This course is an introductory course on information retrieval (IR). The explosive growth of available digital information (e.g., web pages, emails, news, social, 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.

Assumed knowledge: Familiarity with the fundamental concepts of probability theory, linear algebra, and real analysis. A first course in statistics would be a plus.

Prerequisite course/s: ML701 Machine Learning or AI701 Foundations of Artificial Intelligence

Course description: 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.

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 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: 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 optimization class. Basics of linear algebra, calculus, trigonometry, and probability and statistics. Proficiency in Python and PyTorch.

Course description: The course covers advanced topics in continuous optimization, such as stochastic gradient descent and its variants, methods that use more than first-order information, primal-dual methods, and methods for composite problems. Participants will read the current state-of-the-art relevant literature and prepare presentations to the other students. Participants will explore how the presented methods work for optimization problems that arise in various fields of machine learning and test them in real-world optimization formulations to get a deeper understanding of the challenges being discussed. Participants will explore how the presented methods work for optimization problems that arise in various fields of machine learning and test them in real-world optimization formulations to get a deeper understanding of the challenges being discussed.

Assumed knowledge: Good understanding of basic reinforcement learning (RL). Basics of linear algebra, calculus, trigonometry, and probability and statistics. Proficiency in Python and good knowledge of Pytorch library.

Course description: The course covers advanced topics in reinforcement learning (RL). Participants will read the current state-of-the-art relevant literature and prepare presentations to the other students. Participants will explore how the presented methods work in simplified computing environments to get a deeper understanding of the challenges that are being discussed. Topics discussed include exploration, imitation learning, hierarchical RL, multi agent RL in both competitive and collaborative setting. The course will also explore multitask and transfer learning in RL setting.

Assumed knowledge: Basic knowledge of linear algebra, probability, and statistical inference. Basics of machine learning. Basics of Python (or Matlab) or Pytorch.

Course description: In the past decades, interesting advances were made in machine learning, philosophy, and statistics for tackling long-standing causality problems, including how to discover causal knowledge from observational data, known as causal discovery, and how to infer the effect of interventions. Furthermore, it has recently been shown that the causal perspective may facilitate understanding and solving various machine learning/artificial intelligence problems such as transfer learning, semi-supervised learning, out-of-distribution prediction, disentanglement, and adversarial vulnerability. This course is concerned with understanding causality, learning causality from observational data, and using causality to tackle a large class of learning problems. The course will include topics like graphical models, causal inference, causal discovery, and counterfactual reasoning. It will also discuss how we can learn causal representations, perform transfer learning, and understand deep generative models.

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 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: Linear algebra, and probability. Proficiency in Python. Basic knowledge of machine learning.

Course description: This course focuses on building foundations and introducing recent advances in machine learning, and on developing skills for performing research to advance the state–of–the–art in machine learning. This course builds upon basic concepts in machine learning and additionally assumes familiarity with fundamental concepts in optimization and math. The course covers foundations and advanced topics in probability, statistical machine learning, supervised and unsupervised learning, deep neural networks, and optimization. Students will be engaged through coursework, assignments, and projects.

Assumed knowledge: This Ph.D.-level course assumes familiarity with core concepts in probability theory, including random variables and basic stochastic processes. Students should have prior exposure to fundamental machine learning methods and neural networks, as well as comfort with Python programming, ideally using frameworks such as PyTorch. Basic understanding of ordinary differential equations (ODEs) and elementary numerical methods is advantageous but not strictly required. Advanced concepts such as measure theory, stochastic differential equations, and optimal transport will be briefly reviewed during the course, though some prior exposure would be beneficial to fully engage with the material.

Prerequisite course/s: ML8101 Foundations of Machine Learning

Course description: This advanced course offers an in-depth exploration of diffusion models, flow matching, and consistency models, essential tools for state-of-the-art generative AI. Beginning with foundational principles, students will gain rigorous understanding of diffusion processes, stochastic differential equations, and discrete Markov chains, ensuring a robust conceptual framework. We will examine classical and modern diffusion-based generative techniques, such as denoising diffusion probabilistic models (DDPM) and score-based generative modeling (SGM), alongside detailed mathematical derivations and convergence analysis. The course progresses to flow matching, elucidating the connections and contrasts with diffusion methods through explicit mathematical formulations, focusing on optimal transport theory, continuous normalizing flows, and numerical solutions of differential equations governing generative processes. We will then dive deeply into consistency models, analyzing their theoretical foundations, fast sampling techniques, and how they bridge diffusion and flow-based approaches. The course incorporates practical implementations and case studies in Python, ensuring students achieve both theoretical depth and applied proficiency. Upon completion, participants will be equipped with comprehensive knowledge of the mathematics, theory, and practical applications behind diffusion and flow-based generative models, preparing them for advanced research or industry innovation.

Assumed knowledge: Good knowledge of calculus, linear algebra, and probability and statistics.

Course description: This course is an advanced course on algorithms for big data that involves the use of randomized methods, such as sketching, to provide dimensionality reduction. It also discussed topics such as subspace embeddings and low rank approximation. The course lies at the intersection of machine learning and statistics.

Assumed knowledge: Basic knowledge of linear algebra, probability and statistics, calculus, and statistical inference.

Course description: The study of probabilistic and statistical inference deals with the process of drawing useful conclusions about data populations or scientific truths from uncertain and noisy data. This course will cover some highly specialized topics related to statistical inference and their application to real-world problems. The main topics covered in this course are classical frequentists and Bayesian inference methods, approximate Bayesian inference and distribution-free uncertainty quantification.

Assumed knowledge: Understanding of machine learning (ML) principles and basic algorithms. Good knowledge of multivariate calculus, linear algebra, optimization, probability, and algorithms. Proficiency in some ML frameworks, e.g., PyTorch and TensorFlow.

Course description: This graduate course explores a modern branch of machine learning: collaborative learning (CL). In CL, models are trained across multiple devices or organizations without requiring centralized data collection, with an emphasis on efficiency, robustness, and privacy preservation. CL encompasses approaches such as federated learning, split learning, and decentralized training. It integrates ideas from supervised and unsupervised learning, distributed and edge computing, optimization, communication compression, privacy preservation, and systems design. The field is rapidly evolving, with early production frameworks (e.g., TensorFlow Federated, Flower) and active research addressing both theoretical foundations and practical challenges. This course familiarizes students with key developments and practices including:
– Optimization methods for collaborative learning
– Techniques to mitigate communication and computation bottlenecks
– Handling data and systems heterogeneity
– Client and participant selection strategies
– Ensuring robustness, fairness, and personalization
– Preserving privacy in collaborative settings

Evaluation is based primarily on students’ paper presentations and a final project chosen by each student, encouraging hands-on engagement with cutting-edge research and applications.

Assumed knowledge: Linear algebra, matrix analysis, probability, and statistics.

Prerequisite course/s: MTH7101 Mathematical Foundations of AI

Course description: 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.

Course description: The course is designed to comprehensively understand various mathematical concepts and their applications for theoretical computer science. The lectures will cover topics such as asymptotics, the Central Limit Theorem, Chernoff bounds, mathematical problem solving, computational models, spectral graph theory, linear programming, semidefinite programming, error correcting codes, de-randomization, expander graphs, constraint satisfaction problems, treewidth, analysis of Boolean functions, communication complexity, information theory, LP hierarchies and proof complexity, quantum computation, cryptography, hardness assumptions, and the sketch of the PCP Theorem.

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, 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/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, probability, and statistics. Programming in Python or similar language.

Prerequisite course/s: 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: Understanding of calculus, algebra, and probability and statistics. Programming in Python or a similar programming language. Understanding of current natural language processing (NLP) methods.

Prerequisite course/s: NLP805 Natural Language Processing – Ph.D.

Course description: This course focuses on recent topics in natural language processing (NLP) and on developing skills for performing research to advance the state-of-the-art in NLP.

Assumed knowledge: Understanding of calculus, algebra, and probability and statistics. Programming in Python or a similar programming language.

Prerequisite course/s: NLP807 Speech Processing – Ph.D.

Course description: This course explores the cutting-edge techniques and methodologies in the field of speech processing. The course covers advanced topics such as automatic speech recognition, language modeling and decoding, speech synthesis, speaker identification, speech diarization, paralinguistic analysis, speech translation and summarization, multilinguality and low-resource languages, and spoken dialog systems. Students will delve into modern models and frameworks for the different speech tasks. The course emphasizes both theoretical understanding and practical implementation, fostering skills necessary for innovative research and development in speech technologies.

Assumed knowledge:

• Foundational understanding of linear algebra, calculus, probability, and statistics.
• Proficiency in Python, familiarity with standard libraries for data handling and machine learning.
• Fundamentals of machine learning, deep learning, including model training, fine-tuning, and evaluation.

Course description: The course will cover a range of applications empowered by AI – from writing assistants to dialogue-based intelligent tutoring systems – across a range of subject domains, including but not limited to language learning and STEM subjects. We will cover topics surrounding content and feedback generation using generative AI, adaptation and personalization of AI-driven educational systems, multi-modal interactive approaches (including not only text-based but also speech and visual systems), agentic AI approaches to educational applications, generative AI model alignment with educational, age- and subject-specific aspects, and novel human-computer interaction opportunities in this domain. In addition to such novel opportunities, the course will delve into emerging challenges, focusing on ethical issues, societal impact and real-world integration of this technology, and evaluation.

Assumed knowledge:

• Foundational understanding of linear algebra, calculus, probability, and statistics.
• Proficiency in Python, familiarity with standard libraries for data handling and machine learning.
• Fundamentals of machine learning, deep learning, including model training, fine-tuning, and evaluation.
• Core NLP concepts such as tokenization, embeddings, language modeling, common training objectives, and task types: classification, sequence tagging, and sequence-to-sequence.
• Strong critical thinking skills and the ability to read and analyze research papers.

Course description: This course explores the design and development of intelligent agent systems powered by large language models (LLMs). Students will gain a foundational understanding of agent architecture, reasoning mechanisms, and multi-agent collaboration. Through weekly lectures and paper discussions, the course delves into practical applications such as simulating fake news environments, autonomous software engineering, role-playing agents, and computational social science. Core topics include agent workflow automation, trustworthiness and safety of LLMs, and knowledge extraction risks. In addition to lectures, students will engage in a mini-project to design and present their own agent-based system. The course bridges theory with real-world applications, aiming to equip students with both conceptual knowledge and hands-on skills for building advanced LLM-driven agents.