Master of Science in

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.

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).

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.

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.

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.

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.

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.

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.

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.

The course introduces Bayesian statistical modelling 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 data sets.

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 PhD degree. Further, it enables graduates to pursue an industrial project involving a research component independently.

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.

The MBZUAI internship with industry is intended to provide the student with hands-on experience, blending practical experiences with academic learning.