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Master of Applied Artificial Intelligence

Degree Summary

Program Outcomes

Program Structure

Faculty

Accreditation

Admission Requirements

Duration

15 - 24 months
12 subjects

Classes commence

September (Autumn)

January (Winter)

April (Spring)

Fees*

AED 9,415.35 / USD 2,565 per subject
*VAT 5% inclusive

Artificial Intelligence (AI) is rapidly becoming the defining technology of our time, driving innovation across virtually every sector; from healthcare and finance to logistics, government, and digital services. As organizations race to adopt AI-driven automation and intelligence, the demand for professionals who can build, manage, and lead these systems has surged.

However, a significant gap exists between the need for AI adoption and the availability of talent capable of executing AI driven solutions. The market urgently requires professionals who can bridge the gap between data, intelligence, and business transformation.

To meet this critical market need, the University of Wollongong in Dubai is introducing the Master of Applied Artificial Intelligence: a practice-driven, industry-aligned program designed to equip students with the advanced technical skills, strategic insight, and ethical governance capabilities required to lead in the AI economy.

Specifically, the program aims to:

Develop industry-ready professionals capable of designing, building, and deploying intelligent systems using modern tools and platforms.
Equip students with in-demand skills in machine learning, deep learning, NLP, AI solutions design & MLOps, generative models, multi-agent systems, AI Strategy, ethics, and governance, as well AI for innovation.
Foster strategic leadership capabilities to align AI initiatives with business goals, manage innovation, and navigate complex regulatory environments.
Offer hands-on learning through applied labs, enterprise-grade tools, and a capstone project tackling real-world challenges.
Support professional certification readiness (including Microsoft, IBM, and Google certifications) to enhance employability and global recognition.

What is Applied AI really about & why is it important?

At its core, Applied Artificial Intelligence is about moving beyond theory to put AI to work. It involves the practical application of machine learning, natural language processing, computer vision, and agentic AI to solve real-world problems, automate complex processes, and create new value for businesses and the society.

In an increasingly data-driven world, AI is no longer a futuristic concept; it is a competitive necessity. From personalized healthcare and smart cities, to fraud detection in finance, to smart logistics & supply chain optimization, to advanced government services and humanoid robotics, AI is the engine powering the next wave of global economic growth.

What’s required for effective AI leadership today?

To build impactful, scalable, and sustainable AI systems, three critical pillars are needed:

Pillar 1: Technical Mastery Deep knowledge of machine learning algorithms, deep learning architectures, data engineering pipelines, and MLOps is essential to build systems that work reliably at scale.
Pillar 2: Strategic Vision AI is not just a technical upgrade; it is a business transformation tool. Organizations need leaders who understand how to align AI capabilities with strategic goals and govern them ethically.
Pillar 3: Real-World Application Success in AI depends on the ability to take a model from a notebook to production. Applied learning, hands-on experience with cloud platforms, advanced machines learning models, and "human-in-the-loop" design are key to driving impact.

So why are organizations investing heavily in AI talent?

Because the economic potential is immense. Reports indicate that AI is expected to contribute $320 billion to the Middle East economy by 2030, with the UAE taking the lead in AI adoption. The reports also highlighted the increasing need for AI talent as industries across the region implement AI-driven business automation and innovation strategies. Companies are seeking professionals who can bridge the gap between technology and industry needs, driving efficiency and competitiveness.

The World Economic Forum (WEF) estimates that nearly 23% of jobs globally will change by 2027, driven by automation and AI adoption, creating a significant demand for AI skills. McKinsey & Company also reports that the demand for advanced AI and tech skills is expected to more than double by 2030, especially in the UAE, where industries like finance, healthcare, and logistics are rapidly embracing AI-driven transformations.

References:

World Economic Forum (2023). The Future of Jobs Report 2023. World Economic Forum. Available at: https://www.weforum.org/reports/the-future-of-jobs-report-2023
McKinsey & Company (2021). Jobs Lost, Jobs Gained: Workforce Transitions in a Time of Automation. McKinsey & Company. Available at: https://www.mckinsey.com/featured-insights/future-of-work/jobs-lost-jobs-gained-what-the-future-of-work-will-mean-for-jobs-skills-and-wages

What are the challenges in building AI capability?

The biggest challenge is the talent gap. While organizations are eager to adopt AI, they struggle to find professionals who possess both the deep technical skills to build models and the strategic awareness to deploy them effectively.

That’s exactly where the Master of Applied Artificial Intelligence degree from the University of Wollongong in Dubai comes in—training the next generation of AI leaders through an industry-aligned, skill-based, hands-on, and certification-embedded master’s program.

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Program Selection Guide

UOWD's Master of Applied Artificial Intelligence

Entry Requirements

Students applying for a master's degree must have completed a bachelor's degree (or equivalent) at an accredited institution of higher education and have achieved the minimum grades outlined below:

Country	Direct Admission	Probationary Entry GPA 2.5-2.99 or equivalent	Conditional Entry GPA 2.0-2.49 or equivalent
Australia	65%	57%	50%
Brazil	7/10	6/10	5/10
China	75%	63%	50%
Colombia	3.5/5	3.25/5	3/5
Egypt	65% (Good)	57%	50% (Pass)
India	50%	45%	40%
	10 Point System: 7.0/10.0	10 Point System: 6.0/10.0	10 Point System: 5.0/10.0
	4 Point System: 3.0/4.0	4 Point System: 2.5/4.0	4 Point System: 2.0/4.0
Iran	14	13	12
Japan	70/100 or 4/5	65/100 or 3.5/5	60/100 or 3/5
Jordan	70%	60%	50%
Kenya	50%	45%	40%
Lebanon	11	10.5	10
Mexico	8/10	7/10	6/10
Nepal	50%	45%	40%
Nigeria	50%	45%	40%
Pakistan	50%	45%	40%
Russia	Average of 4	Average of 3.5	Average of 3
South Korea	80/100	75/100	70/100
UAE	3	2.5	2
UK	2^nd Class Honours with lower division (or min 50%)	45%	Pass (or min 40%)
USA	3	2.5	2

Note:

[1] Unless otherwise stated, the entry requirements above are based on Section I institutions. Further details on alternate entry requirements can be obtained by contacting the Admissions department.
[2] A recognised bachelor’s degree in a related field such as IT, computer science, engineering, business information systems or any bachelor’s degree with relevant professional experience and/or certification.
[3]Students with bachelor degrees in non-related fields could be admitted to the program subject to an interview and completion of remedial subjects.

English Language Requirements

At UOWD, all classes are taught in English and in order to enrol in a master's degree you will need to submit one of the following certificate of English language proficiency:

English Language Test	Entry Score
IELTS Academic	Overall score of 6.0 with 6.0 in Reading and Writing and 5.0 in Listening and Speaking
TOEFL Internet-based	79 with not less than 20 in Writing, 18 in Reading, 17 in Listening, 16 in Speaking
Pearson Test of English (PTE) Academic	54 over all with Minimum 54 PTE Communicative Skill Scores

Credit for Prior Learning

If you are seeking credit for prior learning, you will need to submit an official academic transcripts showing all courses studied and the grades obtained, syllabus details (including information on course content) for the courses you have successfully completed and an explanation of the grading system. Your eligibility for Credit for Prior Learning will be decided in consultation with UOWD's Faculty Offices prior to the beginning of the program.

To make an application for your chosen program, complete the online application form and submit it along with all your supporting documentation (see below) prior to the application deadline.

Late applications may be accepted subject to the availability of places but applicants are encouraged to apply as early as possible.

Supporting Documentation

The following records are required to process your application. Please ensure that all these documents accompany your UOWD application for admission form submittal:

The original transcripts of your bachelor’s degree results
A Certificate of Completion

(If the above documents are not in English, a certified English translation is required)

Proof of your english language proficiency

(Note: Results from IELTS & TOEFL tests must be sent to the University directly from the IELTS or TOEFL testing centres quoting the UOWD institution code IELTS: AE109 / TOEFL: 7907)

Your current resume or CV
A copy of your UAE ID card (if applying from within the UAE)
A copy of your passport (and Residence Visa, if resident in the UAE).

Additionally, if you are seeking advance standing in your chosen program:

Certified copies of official academic transcripts showing all courses studied and grades obtained, syllabus details (including information on course content) of the courses you successfully completed and an explanation of the grading system.

Attestation of Documents

Applications are required to obtain attestation / equivalency of their bachelor’s degree from the UAE Ministry of Education. The attestation / equivalency process is dependent upon whether you completed your bachelor’s degree within the UAE or outside the UAE.

If the bachelor’s degree was completed outside UAE, you must have your bachelor’s degree and transcripts/mark sheets certified by:

The issuing University and/or recognised authority for tertiary education in the host country in which you completed your education
The Ministry of Foreign Affairs in the host country
The UAE Embassy in that host country OR the Embassy of the host country in UAE must attest the authenticity of the documents and attestations.

Additionally, the UAE Ministry of Education must issue an Equivalency Certificate. Applicants must have a valid UAE residency visa when applying for an Equivalency Certificate. In special cases where complying with conditions (2) and (3) are not feasible, the certificates may be verified against originals by Embassies in the UAE and the UAE Ministry of Foreign Affairs.

If you are unable to secure the Equivalency Certificate(s) as outlined above will be asked to sign a “Consent to Provide Documents” form agreeing to secure the equivalency. You will be permitted to commence your studies at UOWD, but will be given a maximum of one semester to obtain the equivalency certificate(s). UOWD reserves the right to take appropriate action against any applicant who cannot secure the appropriate documentation in this time, which may result in the termination of the student’s enrolment at UOWD.

For more information or assistance on attesting documents from outside UAE, click here.

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Program Structure

The Master of Applied Artificial Intelligence is a 72-credit points (CPs) postgraduate degree designed to accommodate both technical and non-technical learners. The program structure supports a progressive, skill-based learning pathway, integrating foundational knowledge, advanced AI practice, and applied research or industry-based projects.

The program consists of:

Foundation Component (4 subjects – 24 cp): Designed for students from non-technical backgrounds, this stage covers essential skills in programming, databases, machine learning, and data engineering to ensure readiness for advanced topics. Students with recognized prior qualifications in computing or IT may receive advanced standing and bypass this component.
Core Component (6 subjects – 36 cp): Focused on advanced AI domains including deep learning, NLP & LLMs, MLOps, agentic AI, and AI strategy & ethics.
Capstone Project (1 subject – 12 cp): A two-trimester applied research or industry-based project that allows students to solve real-world AI challenges and demonstrate mastery of both technical and managerial competencies.

The full program comprises 12 subjects (72 cp), while eligible students with relevant academic or professional background may complete the program in just 48 CPs.

Artificial Intelligence Subjects

Foundation Subjects

This subject provides an introduction to the foundational concepts, algorithms, and real-world applications of Artificial Intelligence (AI) and Machine Learning (ML). Students will develop practical skills in key ML techniques such as classification, clustering, association rule mining, neural networks, and reinforcement learning. The subject emphasizes hands-on learning, using modern tools and platforms to bridge theory with practice. Through applied assignments and workshops, students will learn to select, implement, and evaluate appropriate AI and ML techniques to solve practical problems across various domains. Ethical, legal, and trust-related considerations in AI applications will also be explored to promote responsible and informed AI practice.

This subject introduces students to Python programming with a focus on data manipulation and machine learning applications. It is designed for students with little or no prior programming experience. Students will learn core Python concepts, how to work with key libraries such as NumPy, Pandas, and Scikit-learn, and how to build simple AI solutions. Through hands-on labs and coding workshops, students will develop skills in writing modular code, preprocessing data, and automating common AI pipeline tasks. By the end of the subject, students will have a foundational coding proficiency applicable to advanced AI subjects.

This subject provides foundational knowledge in databases and cloud computing, focusing on their essential roles in AI pipelines. Students will explore the principles of relational (SQL) and non-relational (NoSQL) databases, learning how to query, manage, and model data for AI applications. The subject also introduces key concepts in cloud computing, including cloud infrastructure, storage solutions, and deployment environments using platforms such as AWS and Google Cloud. Through practical exercises and guided projects, students will gain the technical skills needed to design data solutions and deploy scalable cloud-based environments that support modern AI workflows.

This subject provides students with foundational knowledge and practical skills in data engineering, focusing on preparing quality data for AI and machine learning workflows. Students will learn how to collect, clean, transform, and manage data using modern tools and platforms such as Apache Airflow, Spark, and SQL-based ETL processes. Emphasis is placed on building scalable and reliable data pipelines, ensuring data integrity, and optimizing data flow for model training. The subject aims at equipping students with the technical fluency required to support AI applications in real-world environments.

Core Subjects

This subject equips students with advanced knowledge and practical skills in applying deep learning to visual data. It covers core and emerging techniques in computer vision, including Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and generative models. Students will explore applications in image classification, object detection, video understanding, and multimodal learning using state-of-the-art models such as CLIP and Stable Diffusion. Emphasis is placed on model training, fine-tuning, and deployment for real-world use cases. By the end of the subject, students will be able to build, adapt, and evaluate production-grade visual AI solutions across domains.

This subject explores the foundations and cutting-edge advancements in Natural Language Processing (NLP) and large language models (LLMs). Students will learn how to process and analyze textual data, apply core NLP techniques, and work with transformer-based architectures such as BERT and GPT. Topics include prompt engineering, fine-tuning open-source LLMs, and designing retrieval-augmented generation (RAG) systems. Emphasis is placed on real-world applications, ethical use of generative text, and evaluating model outputs. By the end of this subject, students will be equipped to design, implement, and assess advanced NLP systems for industry-specific applications.

This subject focuses on the practical aspects of building, deploying, and managing AI systems in production environments. Students will learn how to design scalable AI solutions, automate workflows using modern MLOps tools, and implement continuous integration and delivery pipelines. Emphasis is placed on reproducibility, model tracking, data versioning, and the ethical and regulatory dimensions of deploying AI at scale. By the end of the subject, students will be able to bridge the gap between model development and real-world operationalization, preparing them for technical leadership roles in AI implementation projects.

This subject provides students with an applied understanding of intelligent AI agents and autonomous software systems. It explores how modern AI agents operate using frameworks powered by large language models (LLMs), and how they interact, make decisions, and integrate with external tools. Topics include agent-to-agent communication, orchestration frameworks like LangGraph, memory management, API/tool integration, and reinforcement learning for agent behavior optimization. Students will gain hands-on experience in building autonomous workflows using both code-based and no-code/low-code platforms. By the end of the subject, students will be equipped to design, deploy, and evaluate agentic AI systems that solve real-world problems in dynamic environments.

This subject equips students with the tools to lead and shape the responsible development and deployment of AI systems. It explores the ethical, legal, societal, and strategic dimensions of AI within industry and government. Students learn to critically assess AI risks, bias, and explainability challenges while engaging with regulatory and governance frameworks such as the EU AI Act and GDPR. Through real-world case studies, workshops, and simulations, students will formulate governance strategies, policy briefs, and organizational AI transformation plans that align with both innovation and ethical principles.

This subject explores how artificial intelligence can be leveraged to enable innovation and drive business transformation across diverse industries. Students will examine modern AI use cases—from smart decision support systems and personalized customer services to creative content generation and AI-powered software development. Through hands-on activities and case studies, students will learn to develop AI transformation roadmaps, assess the impact of AI on productivity and ROI, and formulate strategies for successful implementation and change management. The subject equips students with the strategic mindset and applied skills needed to lead AI adoption and innovation initiatives in dynamic organizational environments.

Capstone Project

This is a two-semester capstone subject designed to integrate and apply the skills and knowledge developed throughout the Master of Applied AI program. In Semester 1, students learn research methods, explore AI problem spaces, and prepare a structured proposal for either an industry-based or research-based project. Through workshops and one-on-one mentoring, they develop research questions, conduct literature reviews, apply ethical principles, and design appropriate methodologies. In Semester 2, students execute their proposed work, either by solving a real-world problem in collaboration with industry or conducting original academic research. The final outputs include a formal thesis and a presentation/defense of their work. The subject emphasizes planning, critical thinking, scholarly writing, innovation, and professional communication, preparing students to contribute to AI knowledge and practice.

Nested Degrees & Exit Options:

Graduate Certificate in Applied AI - Awarded upon completion of the 4 foundation subjects (24 CPs).
Graduate Diploma in Applied AI - Awarded upon completion of 8 subjects (48 CPs).
Master of Applied AI - Awarded upon full completion of all 12 subjects (72 CPs).

Note: Please consult the School of Computer Science for more details.

Certification Support:

To further enhance career readiness, the program includes embedded support for globally recognized AI certifications. Students will benefit from free exam vouchers and dedicated preparation workshops for one of the following credentials:

Students can choose one of the following industry certifications:

Microsoft Certified: Azure AI Engineer Associate
IBM AI Engineering Professional Certificate
Google Cloud Professional Machine Learning Engineer

Program Outcomes

Upon successful completion of the Master's degree in Applied AI, graduates will be able to:

Evaluate organizational and societal challenges and formulate AI-driven strategies that align with business objectives, ethical standards, and regulatory frameworks.
Analyze real-world problems related to data, automation, and intelligence, and design practical AI solutions using modern machine learning, data engineering, and MLOps practices.
Analyze the suitability of traditional and modern AI models and architectures for different data types and industry applications.
Formulate and implement innovative, automated, and value-driven solutions across diverse industry sectors, using modern AI and transformative leadership concepts.
Conduct applied or research-based AI projects independently or collaboratively, demonstrating critical thinking, methodological rigor, and technical proficiency.
Demonstrate professional and interpersonal competencies by communicating technical concepts effectively, collaborating, and applying ethical reasoning and leadership in applied AI initiatives.

Graduates will be prepared for roles such as:

AI Specialist / Consultant
AI Engineer
Machine Learning Engineer
AI Project Manager
AI Product Manager
NLP Engineer
AI Solutions Architect
Technology Lead for AI-driven Automation
AI Researcher / Applied Scientist

Accreditation and Recognition

All degrees at the University of Wollongong in Dubai are accredited by the Commission for Academic Accreditation (CAA) of the UAE Ministry of Education and are licensed by the Knowledge and Human Development Authority (KHDA). In addition, the degree is quality assured by UOW, which is registered with the Tertiary Education Quality and Standards Agency (TEQSA), the national regulator of the higher education sector in Australia.

Students will be issued a UOW Australia degree upon graduation.

Prof May El Barachi

Dean
School of Computer Science

Prof May El Barachi is a distinguished professor, academic leader, and the Dean of the School of Computer Science at the University of Wollongong in Dubai. She is known for her transformative leadership, having launched pioneering programs such as the region’s first Master of Digital Transformation and significantly expanded student enrollment. With a strong track record in industry collaboration and over AED 5 million in research funding, she combines strategic vision with hands-on execution to drive innovation and excellence in education.

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Master of Applied Artificial Intelligence

Degree Summary

Program Outcomes

Program Structure

Faculty

Accreditation

Admission Requirements

Duration

Classes commence

Fees*

Enquire Now

Program Selection Guide

View Program Structure

Program Structure

Foundation Subjects

Foundations of AI & Machine LearningMAAI801 | 6 cp

Programming for AI ApplicationsMAAI802 | 6 cp

Databases & Cloud FundamentalsMAAI803 | 6 cp

Data Engineering for AIMAAI804 | 6 cp

Core Subjects

Deep Learning for Visual DataMAAI901 | 6 cp

Natural Language Processing & LLMsMAAI902 | 6 cp

AI Solutions Design and MLOpsMAAI903 | 6 cp

Agentic AI and Autonomous Software SystemsMAAI904 | 6 cp

AI Strategy, Ethics & GovernanceMAAI905 | 6 cp

AI for Innovation and Business TransformationMAAI906 | 6 cp

Capstone Project

Applied AI ProjectMAAI999 | 12 cp

Nested Degrees & Exit Options:

Certification Support:

Program Outcomes

Accreditation and Recognition

Prof May El Barachi

Faculty

Similar Career Paths

Open Day