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.

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

Course description: 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.

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

Course description: This course provides a comprehensive overview of different concepts and methods related to deep learning. Students will first learn the foundations of deep learning, after which they will be introduced to a series of deep models: convolutional neural networks, autoencoders, recurrent neural network, and deep generative models. Students will work on case studies of deep learning in different fields such as computer vision, medical imaging, natural language processing, etc.

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.

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. 

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

Prerequisite course/s: CV 701 Human and Computer Vision or equivalent

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 course/s: CV701 Human and Computer Vision or equivalent

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: 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.

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: 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.

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

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.

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.

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.