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UOWD's Graduate Diploma in Applied Artificial Intelligence
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  • Graduate Diploma in Applied Artificial Intelligence

Graduate Diploma in Applied Artificial Intelligence

Degree Summary
Program Outcomes
Program Structure
Faculty
Accreditation
Admission Requirements
Duration

12 months
8 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 Graduate Diploma in Applied Artificial Intelligence: a practice-driven, industry-aligned program designed to equip students with the advanced technical skills 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, and multi-agent systems.
  • 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.
     
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Program Selection Guide
UOWD's Graduate Diploma of Applied Artificial Intelligence
View Program Structure
  • Academic Requirements
  • Application Procedures
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 2nd 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, 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:

  1. The issuing University and/or recognised authority for tertiary education in the host country in which you completed your education
  2. The Ministry of Foreign Affairs in the host country
  3. 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 Graduate Diploma in Applied Artificial Intelligence is a 48-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, and advanced AI practice.

The program consists of:

  1. Foundation Component (4 subjects – 24 CPs): 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. 
  2. Core Component (4 subjects – 24 CPs): Focused on advanced AI domains including deep learning, NLP & LLMs, MLOps, and agentic AI.
  • 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.

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:

  1. Microsoft Certified: Azure AI Engineer Associate
  2. IBM AI Engineering Professional Certificate
  3. Google Cloud Professional Machine Learning Engineer

 

Program Outcomes

Upon successful completion of the Graduate Diploma in Applied AI, graduates will be able to:

  1. Analyse real-world problems related to data, automation, and intelligence, and design practical AI solutions using modern machine learning, data engineering, and MLOps practices.        
  2. Analyse the suitability of traditional and modern AI models and architectures for different data types and industry applications.        
  3. Formulate and implement innovative, automated, and value-driven solutions across diverse industry sectors, using modern AI and transformative leadership concepts.        
  4. 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
  • NLP Engineer
  • AI Solutions Architect
  • Technology Lead for AI-driven Automation
     

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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Faculty

Prof Farhad Oroumchian

Professor
Program Leader
(General Computer Science)

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Dr Patrick Mukala

Assistant Professor

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Dr Pallavi Ranjan

Assistant Professor

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Dr Milan Dordevic

Associate Professor

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