The Master in Applied Artificial Intelligence (MAAI) program is designed to address the critical skills gap in artificial intelligence (AI) applications within industry, both in the UAE and globally. Combining theoretical knowledge with practical, hands-on experience, the program equips students with advanced skills for adapting and implementing AI technologies in industry, research, and government sectors.
Aligned with Abu Dhabi’s knowledge economy vision, the MAAI program educates current and future leaders in cutting-edge AI applications, supporting national AI strategies and economic and societal development in the UAE. This industry-focused, project-based curriculum emphasizes development of applied and entrepreneurial skills, preparing graduates to create AI innovations and societal, economic and environmental impact.
The goals of the MAAI are:
Graduate students with skills and knowledge to develop innovative and sustainable AI solutions and AI-based ventures.
Educate future leaders with advanced data analysis and problem-solving skills.
Equip graduates with skills and knowledge to apply AI in critical areas of advanced technology adaptation in industry, research, and development, as well as in critical government functions.
Develop graduate skills to manage and lead collaborative interdisciplinary research projects.
Graduate students to support the broader strategic societal, environmental, and economic goals as set forward in the UAE’s future economy strategy.
Mode: Part-time
34 credits
Location: On Campus
Duration: Two years
Fees: AED 170,000 (AED 5,000 per credit)
Ting Yu
Program Director of Master of Applied AI and Professor of Computer Science
It is my great pleasure and honor to introduce the Master in Applied Artificial Intelligence degree program at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI).
The vision of the program is to educate tomorrow’s innovators and leaders in the application of artificial intelligence (AI) to solve both organizational and societal problems, while contributing to the growth and prosperity of the UAE.
This applied master’s program is designed for working professionals in the UAE – offering a balanced mix of fundamental AI knowledge, practical projects, and industry experience. As the first program of its kind in the UAE, it is delivered by MBZUAI’s world class faculty. Students will also gain access to MBZUAI’s cutting-edge research community, top-tier facilities and a lifetime alumni network.
The program supports Abu Dhabi’s knowledge economy and AI strategies by equipping future leaders of the nation with advanced AI skills. It follows the global trend in AI graduate education, branching toward a highly application-focused, project-based and industry-driven program with entrepreneurial elements.
We invite you to join the MBZUAI community, and take your career to new heights by applying for the Master in Applied Artificial Intelligence (MAAI) program today.
Close all sectionsExpand all sections
A fee-based, two-year part-time program. No scholarships available.
Designed for UAE-based working professionals with multi-year experience in AI, digital, or technology-related roles.
Lectures and lab sessions delivered outside of normal business hours including evenings and weekends.
Emphasis on hand-on skills for full-cycle industrial AI solutions.
Engagement with regional and global leading AI stakeholders.
Key curriculum areas include:
Data science: Apply machine learning (ML) techniques to industry challenges such as forecasting, fraud detection, recommender systems, and pattern mining for data-driven decision-making.
Software engineering and machine learning operations (ML Ops): Build, deploy, and maintain production AI systems with industry-standard MLOps practices and software engineering principles.
Deep learning: Master neural networks and deep learning architectures for computer vision, natural language processing (NLP), and complex pattern recognition tasks.
Generative AI: Learn to build and deploy cutting-edge generative models including large language models (LLMs), diffusion models, and multimodal AI systems.
Diverse electives: Choose from specialized courses in NLP, computer vision, robotics, reinforcement learning, and emerging AI domains. Tailor your learning path to align with your organization's strategic priorities.
National Qualifications Framework – three strands
The program learning outcomes (PLOs) are aligned with the Emirates Qualifications Framework and, as such, are divided into the following learning outcomes strands: knowledge (K), skills (S), and responsibility (R).
Upon completion of the program requirements, the graduate will be able to:
PLO 01: Design innovative and sustainable artificial intelligence (AI) models to support organizational entrepreneurship and new value creation.
PLO 02: Apply advanced analytical and problem-solving skills for the application of AI in solving organizational challenges.
PLO 03: Analyze big contextual data sets to improve AI applications.
PLO 04: Manage complex collaborative research, development and innovation projects in the domain of AI and advanced technologies.
PLO 05: Assess ethical, societal and legal considerations related to AI technologies.
The PLOs are mapped to the National Qualifications Framework Level Seven (7) qualification and categorized into three domains (knowledge, skill, and responsibility) as per the National Qualifications Framework set by the UAE National Qualifications Centre (NQC) and the Ministry of Higher Education and Scientific Research (MoHESR):
PLOs
Knowledge
Skill
Responsibility
PLO 01
K
–
R
PLO 02
K
S
–
PLO 03
K
S
–
PLO 04
–
S
R
PLO 05
–
S
R
The minimum degree requirements for the Master in Applied Artificial Intelligence is 34 credits, distributed as follows:
Number of courses
Credit hours
Core
4
16
Electives
2
8
Internship
1
2
Research methods
1
2
Research project
1
6
Total
9
34
Code
Course title
Credit hours
INT799
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.
2
MAAI701
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 large-scale datasets, 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.
4
MAAI702
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.
4
MAAI703
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 application, 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.
4
MAAI704
Assumed knowledge: Applied deep learning.
Prerequisite courses: MAAI703 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.
4
MAAI799
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.
6
RES799
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 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.
2
Students will select a minimum of two (2) elective courses, with a total of eight (8) (or more) credit hours, based on interest, proposed research thesis, and career aspirations, in consultation with their supervisory panel. The elective courses available for the Master in Applied Artificial Intelligence are listed below.
Code
Course title
Credit hours
AI7101
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.
2
AI7102
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.
2
CS7101
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.
4
CV701
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.
4
CV702
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.
4
CV703
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 tasks of visual recognition, detection, and tracking.
4
DS701
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.
4
DS702
Assumed knowledge: Databases. Proficiency in Java or Python. Basic knowledge on 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.
4
DS703
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.
4
DS704
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, AI7101 Machine Learning with Python, MTH701 Mathematical Foundations of AI
Course description: This course covers the fundamentals of theoretical statistics, which are the foundations 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.
4
ENT799
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.
2
HC701
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.
4
ML707
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.
4
ML709
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.
4
MTH702
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.
4
NLP701
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.
4
ROB701
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 for 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.
4
ROB702
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.
4
ROB703
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.
4
Students first work in groups to specify and address complex real-life organizational and business challenges. This part of the enquiry emphasizes critical thinking, problem-solving skills, and teamwork. The latter part of the project is individual. It enables students to demonstrate their learning throughout the program by conducting an enquiry into a specific topic related to the initial research question from their disciplinary background.
Code
Course title
Credit hours
MAAI7199
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 the 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.
6
Applicants should hold a bachelor’s degree in a STEM field such as computer science, electrical engineering, computer engineering, mathematics, physics or other relevant science or engineering major from a university accredited or recognized by the UAE Ministry of Higher Education and Scientific Research (MoHESR) with a minimum CCGPA of 3.0 (on a 4.0 scale) or equivalent.
While a degree in a STEM field is preferred, MBZUAI can grant program access to bachelor’s degree holders from other disciplines if they can provide evidence of gaining technical knowledge and expertise through engagement or work experience.
All applicants whose first language is not English must demonstrate proficiency in English through one of the following:
Accepted only where IELTS/TOEFL/PTE/Cambridge access is limited. Mandatory interview required. Evidence of limited access to other tests may be requested.
*Exams must be administered at an approved physical test center. Home Edition exams are not accepted.
English language proficiency waiver eligibility
Applicants may qualify for a waiver if they meet one of the following conditions:
Full exemption: Completed a degree entirely taught and assessed in English at a university located in a country where English is both an official language and the dominant language of instruction in higher education. This includes:
American Samoa, Australia, Botswana, Canada (excluding Quebec), Fiji, Ghana, Guyana, Ireland, Jamaica, Kenya, Lesotho, Liberia, New Zealand, Nigeria, Papua New Guinea, Samoa, Singapore, Solomon Islands, South Africa, Tonga, Trinidad and Tobago, United Kingdom, United States, Zambia, Zimbabwe.
Conditional exemption: If your degree was completed in another country, you may still request an exemption if you can provide official documentation (on university letterhead, signed by an academic official) confirming that your entire program of study was taught and assessed in English.
English language requirement deadline:
The English language requirement should be submitted within the application deadline. However, for those who require more time to satisfy this requirement, there is a final deadline of March 1.
A strong foundation in AI related mathematical concepts through undergraduate preparation or practical experience.
Within 10 days of submitting your application, you will receive an invitation to book and complete a mandatory online screening exam that assesses knowledge and skills relevant to your chosen field.
Exam topics
Math: Calculus, probability theory, linear algebra, and trigonometry.
Programming: Knowledge surrounding specific programming concepts and principles such as algorithms, data structures, logic, OOP, and recursion, as well as language-specific knowledge of Python.
Applicants are highly encouraged to complete the following online courses to further improve their qualifications:
For more information regarding the screening exam (e.g. process, opting out criteria, and technical specifications), register on the application portal
here, and view this
knowledge article.
In a 500- to 1000-word essay, explain why you would like to pursue a graduate degree at MBZUAI and include the following information:
Motivation for applying to the university
Personal and academic background and how it makes you suitable for the program you are applying for
Experience in completing a diverse range of projects related to artificial intelligence
Stand-out achievements, e.g. awards, distinction, etc
Goals as a prospective student
Preferred career path and plans after graduation
Any other details that will support the application
Applicants will be required to nominate referees who can recommend their application. MAAI applicants should have a minimum of two (2) referees wherein one was a previous course instructor or faculty/research advisor and the other a current or previous work supervisor.
To avoid issues and delays in the provision of the recommendation, applicants have to inform their referees of their nomination beforehand and provide the latter’s accurate information in the online application portal. Automated notifications will be sent out to the referees upon application submission.
Please submit a curriculum vitae highlighting your academic achievements, extracurricular involvement, skills, and experiences that make you a strong candidate for the program. It is highly recommended that candidates have more than three years’ experience in industry including private and public sector organizations.
You can also include your choice of the following sections (and/or create others as necessary):
Committee experience
Conference papers
Conference presentations
Dissertation (thesis)
Grants awarded
Professional activities
Professional membership/leadership
Publications
Published articles
References
Research experience
Service experience
Teaching experience
A select number of applicants may be invited to an interview with faculty as part of the screening process. The time and instructions for this will be communicated to applicants on timely bases.
Only one application per admission cycle must be submitted; multiple submissions are discouraged.
Application portal opens
Priority deadline*
Final deadline
Decision notification date
Offer response deadline
September 1, 2025 (8 a.m. GST)
November 15, 2025 (5 p.m. GST)
February 27, 2026 (5 p.m. GST)
April 15, 2026 (5:00 p.m. GST)
May 1, 2026
* Applications submitted by the priority deadline will be reviewed first. While all applications submitted by the final deadline (December 15, 2025) will be considered, applying by the priority deadline is strongly encouraged. Admissions are highly competitive, and space in the incoming cohort is limited.
Detailed information on the application process and scholarships is available
here.
The Master’s in Applied Artificial Intelligence (MAAI) program is a part-time, in-person, and on campus. Typical time to completion is two (2) years.
Part-time students must complete all degree requirements within a maximum of three (3) full academic years, unless terminated earlier by the conferral of the degree or by academic or administrative action.
Once this time-to-degree limit has lapsed, the person may resume work towards a Master’s degree only if newly admitted to a currently offered degree program under criteria determined by that program.
All students are required to take part in an internship. The internship is intended to provide students with hands-on experience, blending practical experiences with academic learning. The internship will normally take place during the final project work in participants’ home organizations, in a new professional role.
The program culminates in a final project, where students first work in groups to specify and address complex real life organizational and business challenges. This part of the enquiry emphasizes critical thinking, problem solving skills and teamwork. The latter part of the project is individual and enables students to demonstrate their learning throughout the program in an enquiry about a specific topic pertaining to the initial research question from their disciplinary background.
A typical part-time study plan is as follows:
SEMESTER 1
MAAI7101 Data Science for Industry (4CR)
MAAI7102 Software Engineering and Programming (4CR)
SEMESTER 2
MAAI7103 Deep Learning Foundations and Applications (4CR)
MAAI7104 Generative AI – From Theory to Practice (4CR)
SUMMER
RES799 RES799 Introduction to Research Methods (2CR)
SEMESTER 3
Elective 1 (4CR)
Elective 2 (4CR)
SEMESTER 4
MAAI799 Master in Applied Artificial Intelligence Industry Research Project (6CR)
INT799 Master of Science Internship (2CR)
Disclaimer: Subject to change.
The full tuition fee to complete the Master in Applied Artificial Intelligence is
AED 5,000 per credit paid each semester.
Total: AED 170,000
This is a fee-based program.
AED 5,000
The registration fee is a deposit that admitted students pay to hold their place in the class, but is fully credited toward the per credit program fees.
MAAI at MBZUAI
Become a leader of the future
Trailblazers in AI
Our alumni network offers lifelong connections and valuable resources, fostering an entrepreneurial ecosystem that benefits alumni and current students.
The Incubation and Entrepreneurship Center (IEC) is a leading AI-native incubator with the aim to nurture and support the next generation of AI-driven startups.
Our alumni network offers lifelong connections and valuable resources, fostering an entrepreneurial ecosystem that benefits alumni and current students.
The Incubation and Entrepreneurship Center (IEC) is a leading AI-native incubator with the aim to nurture and support the next generation of AI-driven startups.
