Master in Applied

This course focuses on teaching students how to develop innovative research-based approaches that can be implemented in an organization. It covers various research designs and methods, including scientific methods, ethical issues in research, measurement, experimental research, survey research, qualitative research, and mixed methods research. Students will gain knowledge in selecting, evaluating, and collecting data 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 topic that can be innovative, entrepreneurial, and sustainable and can be applied in any organization related to the topic of research. 

This course takes an applied approach to data science, focusing on practical techniques and tools for effectively handling, analyzing, and visualizing data to meet industry needs. Key topics include data cleaning and preprocessing, exploratory data analysis (EDA), data visualization, and feature engineering using Python libraries such as Pandas, NumPy, Matplotlib, and Seaborn. The course also covers methodologies for machine learning, including managing large-scale datasets, building models with PyTorch, and deploying data-driven solutions in real-world scenarios. Students will work on projects that mirror real-world challenges in domains like healthcare, finance, retail, and logistics, developing critical thinking and practical skills to create, evaluate, and deploy data-driven models for industry-specific problems.

This course provides an in-depth exploration of software engineering practices tailored for applied machine learning. Students will learn about the integration of software development tools, design principles, and machine learning techniques to build scalable ML applications. The curriculum teaches practical software engineering skills, including software design for ML, database integration, testing and debugging of ML code, distributed training methods, and cloud deployment strategies. Through hands-on labs, students will collaborate to design and implement software that addresses real-world industrial challenges. The course prepares students to meet industry demands by fostering problem-solving skills and practical expertise in modern ML software development. 

This course provides a comprehensive introduction to deep learning, focusing on practical applications and real-world problem-solving using PyTorch. Students will learn fundamental and advanced concepts in deep learning, including neural networks, convolutional networks, and recurrent networks, as well as topics such as transfer learning, autoencoders, and reinforcement learning. Through hands-on labs and projects, students will apply deep learning algorithms to solve challenges in domains such as image processing, natural language processing (NLP), and time-series analysis. The course emphasizes the implementation and deployment of deep learning models, equipping students with the skills to build and optimize models for real-world industrial challenges.  

This course introduces the fundamental principles and practice of generative AI, and how it is used to construct AI applications. Students will learn about different generative AI architectures and their use cases, how generative AI models are pre-trained and fine-tuned, and how to effectively perform inference on generative AI models via prompt engineering techniques. Students will also learn about the ethics and risks involved in using generative AI.  

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. This course aims to instill in students a strong grasp of supervised and unsupervised as well as the variants of learning algorithms. In addition, this course aims to expose students to the basics of the neural network, deep learning, and reinforcement learning. 

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. Specifically, students will be able to build, train, evaluate, and improve appropriate deep learning models for different problems. 

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. The goal of the course is to expand the abilities of students with respect to analyzing, critiquing, designing, and implementing advanced algorithms and their applications in different cases. 

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. This graduate level course will provide a coherent perspective on the different aspects of human and computer vision and give students the ability to understand state-of-the-art computer vision literature and implement components that are essential to many modern machine vision systems. 

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. The aim of this course is to enable students to build state-of-the-art systems for automatic image and video understanding to answer complex questions such as what objects are present in a complex scene and where are they located? Students will be equipped with the skills for designing, implementing, training and evaluating complex neural network architectures to solve visual object recognition and detection problems. 

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. This course aims to instill in students a strong grasp of digital twin technology and making sense of multi-sensory data using fusion techniques, understanding the different characteristics of data (text, audio, image) and applying the proper machine learning/deep learning technique that best fits the type of data at hand. In addition, this course aims to expose students to the basics of digital twin modeling, data visualization, and human machine interaction. 

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. The aim of this course is to provide students with a comprehensive understanding of the modern development of data mining foundations and techniques. Students will be able to develop advanced skills to solve a wide range of unsupervised learning problems, such as frequent pattern mining and data clustering. This course introduces the basic concepts, principles, methods, implementation techniques, and applications of data mining, with a focus on two major data mining functions: (1) pattern discovery and (2) cluster analysis. 

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. The aim of this course is to provide students with the comprehensive understanding of the academic and industrial development of big data processing foundations and techniques. Students will understand the basic concepts of MapReduce, locality-sensitive hashing, PageRank, mining data stream, etc. and will be able to develop advanced skills to solve practical big data processing problems. This course introduces the basic concepts, principles, methods, implementation techniques, and applications of MapReduce, locality-sensitive hashing, PageRank, and mining data stream. 

This course is an introductory course on Information Retrieval (IR). The explosive growth of available digital information (e.g., Web pages, emails, news, Tweets, 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. 

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. This course aims to inculcate a deeper understanding of the fundamentals of medical imaging and its analysis. The course introduces the physics behind various imaging modalities such as X-ray, CT, MRI, ultrasound, and PET and presents an overview of the 3D geometry relevant to medical image analysis from the perspective of solving different medical imaging problems. 

This course comprehensively introduces using AI/ML in smart city services and applications. The course will start by reviewing basic concepts. Students will learn how to apply AI/ML to develop, design, and improve smart city services. They will be able to demonstrate an understanding of the smart city concept, applications, requirements, and system design. They will develop capabilities of integrating emerging technologies in smart city components and be able to implement them. In addition, they will gain knowledge in applying security, data analytics, Internet of Things (IoT), communications, and networking and work on case studies solutions for smart city infrastructures.  

This course provides students with a comprehensive introduction to various trust-related issues in artificial intelligence and machine learning applications. Students will learn about attacks against computer systems that use machine learning and defense mechanisms to mitigate such attacks. The aim of this course is to familiarize students with emerging research topics related to security, privacy, explainability, ethics, and fairness in machine learning. Students will develop the ability to audit machine learning systems to identify their vulnerabilities and ethical implications and propose solutions to address the vulnerabilities and ethical concerns.  

This course provides a comprehensive introduction to using AI/ML in Internet of Things (IoT) smart systems, services and applications. The course will start by reviewing advanced concepts. Students will learn how to apply AI/ML to develop, design and improve IoT systems and services. They will be able to demonstrate an understanding of IoT concepts, applications, requirements and system design. They will develop capabilities of integrating emerging technologies in smart IoT components and be able to implement them. In addition, they will gain knowledge and skills in applying security, data analytics, AI models, communications and networking and work on case studies solutions for IoT infrastructures.  

This course provides a comprehensive introduction to Natural Language Processing. It builds upon fundamental concepts in Mathematics, specifically probability and statistics, linear algebra, and calculus, and assumes familiarity with programming. This graduate level course aims to familiarize students with the foundations of core Natural Language Processing algorithms. The course covers the following major modules: (I) Sequence Tagging, (II) Parsing, (III) Text Categorization (IV) Sequential Modelling and (V) Machine Translation 

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. This graduate-level course aims to equip students with a deep understanding of the foundations of core speech processing algorithms. The course covers the following major modules: (I) Speech Recognition, (II), Speech Synthesis, and (III) Dialogue and Conversational Systems. 

The course covers the mathematical foundation of robotic systems. It introduces students to the fundamental concepts of ROS (Robot Operating System) as one of the most popular and reliable platforms for programming 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. This course provides a coherent perspective of some basic robotic concepts, including dynamics, perception, motion control, navigation, and path planning, and equips students with the necessary mathematical tools to formally model and analyze the sensory and kinematic structure of robotic systems. It also exposes them to robotic programming under Robot Operating System (ROS).  

Robots must be able to sense and learn from experience to achieve autonomy. The most frequently used sensing technique is vision. We will explore both the fundamental techniques used in image processing and computer vision analysis (localize objects, recognize objects, segment images) together with advanced tools that allow robots to estimate the motion of objects, estimate depth or reconstruct 3D scenes from a set of images. To give robots the ability to learn, we will explore Reinforcement Learning (RL). RL is a subfield of ML that is inspired by how humans learn. The RL agent interacts with its environment, observes the impact of its actions, and receives rewards (positive or negative, depending on how well it accomplishes a given task). We cover both the fundamental and advanced RL algorithms and discuss their advantages and disadvantages in various robotics settings. 

The course covers different topics and techniques within the context of mapping, localization, and navigation. It highlights SLAM methods using various types of filters, such as Kalman filter, Extended Kalman filter (EKF) and Particle filter. It investigates grid- and graph-based SLAM and data association. It puts in perspective map-based and reactive navigation techniques. To reinforce these concepts and methods, they are applied within ROS (Robot Operating System) through dedicated state-of-the-art ROS packages, such as the tf package, AMCL, and mapping. This course will provide a comprehensive coverage of self-localization and mapping (SLAM) techniques in robotics. It provides a thorough analysis of the state-of-the-art SLAM algorithms. Students will implement such methods within ROS and apply them to realistic robotic applications. 

The Applied Project exposes students to a real-world problem, requiring them to propose solutions. Students pursue independent project work for one semester. This involves a review of the literature and systematically applying frameworks, models, concepts, and theories from Master in Applied Artificial Intelligence courses to a specific problem or situation for which students develop practical solutions. One semester is the project design and planning stage, and the study’s final semester is for the project’s completion. The Applied Project helps train graduates to pursue an industry-aligned project involving a research component independently. 

The MBZUAI internship with industry is intended to provide the student with hands-on experience, blending practical experiences with academic learning. For Master level students, the internship should be six weeks long and align with the working hours of the host organization. The internship does not have to align with the student’s research at MBZUAI directly. As per the learning outcomes of the MBZUAI Internship outlined in the MBZUAI Industry Partners Guide, MBZUAI Supervisors Internship Guide, and related program-specific marking rubrics.