Master of Science in

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 independently pursue an industrial project involving research component independently.

Assumed knowledge: Calculus and linear algebra. A calculus-based introduction to probability theory. An introductory course in statistics is recommended. 

Course description: Mathematical statistics is crucial because it forms the backbone of data analysis and enable the development of models to infer, describe, and predict real-world phenomena. It provides the theoretical foundation needed to understand complex data sets, evaluate uncertainty, and make informed decisions. The course is an attempt to provide a reasonable balance between mathematical rigor and statistical practice. It provides a detailed account on the foundations of statistics and the required basics of probability theory but also emphasizes the applicability of statistics to real-world problems.

Assumed knowledge: Calculus and linear algebra. A calculus-based introduction to probability theory, An introductory course in statistics is recommended. 

Course description: Linear models (LM) and generalized linear models (GLM) are central to data science. These models are critical for analyzing complex data sets, enabling accurate predictions and providing insights into underlying patterns and trends. They facilitate decision making and strategy development in various fields such as finance, healthcare, and social sciences. In this course we present a modern introduction to regression, covering the basics of classical inference methods and introducing newer results from sparse estimation (LASSO).  

Assumed knowledge: Familiarity with the fundamental concepts of linear algebra, real analysis, probability theory and statistical inference, and linear model and extension. 

Course description: This course offers a deep dive into advanced techniques in data analysis, focusing on density estimation, clustering, dimension reduction, visualization and practical problem-solving in data science. Students will explore diverse methods for density estimation, including histograms and kernels, and delve into the intricacies of clustering techniques such as K-means, mixture models, hierarchical clustering, and spectral clustering. The course also covers various dimension reduction and visualization methods like principal component analysis (PCA), kernel PCA, t-SNE, and autoencoders. Moreover, students will tackle practical challenges in data science, including outlier detection, handling missing values, and proposing an interpretation of machine learning models. Through practical exercises and real-world examples, students will develop essential skills for analyzing complex datasets and extracting valuable insights. 

Assumed knowledge: Fundamental course on mathematical statistics. 

Course description: This intensive course provides a comprehensive exploration of advanced statistical learning techniques for prediction and forecasting. Through a blend of theoretical understanding and hands-on implementation, students will delve into fundamental concepts such as model evaluation metrics, penalized regressions, support vector machines, ensemble methods, and specialized algorithms for time series data. 

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.

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.

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. 

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: 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 calculus, linear algebra, and programming.  

Course description: The course delves into the world of optimization, linear algebra, and numerical methods, and is specifically tailored to machine learning and data science applications. It aims to bridge the gap between general courses in linear algebra, optimization, and numerical methods and the specific needs of data science practitioners. The emphasis is on understanding and using the underlying mathematical principles, including numerical techniques, rather than naively applying data science solutions.

Assumed knowledge: Fundamental course on mathematical statistics. 

Course description: The course introduces Bayesian statistical modeling and inference, focusing on computational methods such as Markov chain Monte Carlo (MCMC) and other simulation methods to aid in the analysis of both low and high-dimensional datasets.