Master in Applied

Assumed knowledge: Prior to undertaking an internship, students must have successfully completed 24 credit hours. 

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

Assumed knowledge: Participants should have a bachelor’s degree in science or equivalent from an accredited university or a university recognized by the UAE Ministry of Education (MoE). Basic understanding of Python programming, data structures, and basic algorithms. Familiarity with machine learning frameworks (e.g., PyTorch or TensorFlow), version control tools (e.g., Git), and basic data visualization (e.g., Matplotlib, Seaborn) is recommended. A basic understanding of linear algebra, probability, and statistics is also beneficial.   

Course description: 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

Assumed knowledge: Participants should have a bachelor’s degree in science or equivalent from an accredited university or a university recognized by the UAE Ministry of Education (MoE). Basic understanding of programming, data structures, and basic algorithms. Prior experience with linear algebra and optimization is recommended.  

Course description: This course provides an in-depth exploration of software engineering practices tailored for applied machine learning (ML). 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. 

Assumed knowledge: Applicants should have a solid foundation in programming, data structures, basic algorithms, and prior experience with linear algebra, optimization, and machine learning fundamentals (e.g., supervised and unsupervised learning).  

Course description: This course provides a comprehensive introduction to deep learning, focusing on practical applications, real-world problem-solving using PyTorch, and deep learning theory. 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, auto-encoders, 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. 

Assumed knowledge: Applied deep learning.  

Prerequisite course/s: MAAI7103 Deep Learning Foundations and Applications. 

Course description: 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. 

Course description: The applied project exposes students to a real-world problem, where they are required to propose solutions. Students pursue independent project work for a period of one semester. This involves a review of the literature and the systematic application of frameworks, models, concepts, and theories from Masters in Applied Artificial Intelligence courses to a specific problem or situation, for which students develop practical solutions. One semester is project design and planning stage, and the final term of the study is for the completion of the project. The applied project helps train graduates to independently pursue an industry-aligned project involving a 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 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. 

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 to give 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: 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 includes dynamic programming; divide and conquer, including FFT; randomized algorithms, including RSA cryptosystem; graph algorithms; max-flow algorithms; linear programming; and NP-completeness. 

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, and probability and statistics demonstrated through relevant coursework. Proficiency in Python and Pytorch. 

Prerequisite course/s: 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: 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. 

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. 

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, 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. 

Assumed knowledge: Familiarity with the fundamental concepts of probability theory, linear algebra, real analysis.  

A first course in statistics would be a plus. 

Prerequisite course/s: ML701 Machine Learning, AI7101 Machine Learning with Python, MTH7101 Mathematical Foundations of AI 

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. 

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: 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, and basic artificial intelligence (AI)/machine learning (ML) knowledge. 

Course description: 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.

Assumed knowledge: Basic concepts in calculus, linear algebra and programming, and basic artificial intelligence (AI)/machine learning (ML) knowledge. 

Course description: 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. 

Assumed knowledge: Linear algebra, matrix analysis, and 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, 1st order and 2nd order optimization methods, problems with equality and/or inequality constraints, and other advanced problems. 

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

Course description: The course covers the mathematical foundation of robotic systems and introduces students to the fundamental concepts of ROS (Robot Operating System) as one of the most popular and reliable platforms to program 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. 

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

Prerequisite course/s: ROB701 Introduction to Robotics 

Course description: 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 machine learning (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. 

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

Prerequisite course/s: ROB701 Introduction to Robotics  

Course description: 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. 

Course description: The applied project exposes students to a real-world problem, where they are required to propose solutions. Students pursue independent project work for a period of one semester. This involves a review of the literature and the systematic application of frameworks, models, concepts, and theories from Masters in Applied Artificial Intelligence courses to a specific problem or situation, for which students develop practical solutions. One semester is project design and planning stage, and the final term of the study is for the completion of the project. The applied project helps train graduates to independently pursue an industry-aligned project involving a research component.