Standalone Micro-Credential
A structured pathway in Artificial Intelligence, Machine Learning, and Deep Learning, delivered through credit-bearing courses and awarded as an independent credential.
Credential Offeror | College of Engineering, Abu Dhabi University |
Embedded Courses | COE101 · AIRE310 · AIRE410 |
Minimum Achievement | B+ (85%) in each course |
At a Glance
Competency-based recognition aligned to defined learning outcomes.
- Foundations: data-centric AI, evaluation, bias & ethics
- ML practice: pipelines, optimization, model selection
- Deep learning: CNNs, modern architectures, applied systems
- Evidence: assessed coursework + applied achievement artifact
Digital credentials are issued via Certifier.io with a verification page accessible through the credential link.
Sample Credentials
Example of the digital micro-credential as issued to learners.
What you'll see
- Credential title and issuing unit
- Recipient name and issuance date
- Verification link / QR
The actual credential is issued digitally via Certifier.io.
Three embedded courses form a vertically integrated sequence from foundations to advanced deep learning practice.
COE101 — Introductory Artificial Intelligence
Foundations of AI and data-centric practice: problem framing, preprocessing, supervised/unsupervised learning, performance evaluation, bias, ethics, and an introduction to neural networks and CNNs.
Topics covered:
- Data cleaning
- Evaluation metrics
- Bias & ethics
- Intro to NN/CNN
Prerequisite: STT100 (General Statistics)
AIRE310 — Machine Learning and Pattern Recognition
Programming-intensive ML pipelines in Python: regression/classification, clustering and dimensionality reduction, optimization (GD, momentum, Adam), and neural network implementation progressing into modern frameworks.
Topics covered:
- Python + NumPy
- Optimization
- Model selection
- Keras/PyTorch intro
Prerequisites: CSC201 (Computer Programming I), COE101 (Introductory Artificial Intelligence), MTT200 (Calculus II)
AIRE410 — Deep Learning
Advanced, application-driven deep learning: CNN-based systems, modern architectures and training strategies, recurrent models, attention/transformers, and practical exposure to LLMs and prompt engineering.
Topics covered:
- CNNs
- Transformers
- LLMs
- Applied systems
Prerequisite: AIRE310 (Machine Learning and Pattern Recognition)
Pilot implementation:
During the pilot offering, learners who achieved a grade of B+ (85%) or higher in each of the three embedded courses were considered to have satisfied the academic requirements for the micro-credential. This pilot phase was used to validate the credential structure, confirm alignment with the defined learner outcomes, and gather performance and feedback data to support continuous improvement.
Learning Outcomes
Upon completion, learners demonstrate the following outcomes.
LO-1: Explain how artificial intelligence and data-driven approaches are used to analyze problems, extract insights, and support decision-making across a range of application domains.
LO-2: Apply appropriate analytical and computational reasoning to develop and assess intelligent solutions, considering technical requirements, data characteristics, and contextual constraints.
LO-3: Evaluate the performance, strengths, and limitations of intelligent systems, and use evidence-based judgment to interpret results and inform improvement decisions.
LO-4: Integrate ethical and professional considerations into the design and use of AI-driven solutions, recognizing their broader societal, economic, and environmental implications.
LO-5: Communicate and collaborate effectively when working on intelligence-driven problems, demonstrating responsibility, adaptability, and awareness of multidisciplinary perspectives.
Credential Educational Goals
CEG-1 — Professional Application
Enable learners to apply artificial intelligence and data-driven intelligence concepts to enhance professional practice in engineering and related technical domains, supporting effective problem analysis, solution development, and informed decision-making.
CEG-2 — Career and Workforce Relevance
Support learner readiness for current and emerging roles that require competence in intelligent systems, data-driven technologies, and applied AI, thereby enhancing employability, career progression, and effective participation in professional and organizational environments.
CEG-3 — Lifelong Learning and Adaptability
Prepare learners to engage in continuous professional development and to adapt to evolving artificial intelligence technologies, tools, standards, and ethical considerations in rapidly changing technical and societal contexts.
Credential Completion Conditions
Requirements to be awarded the micro-credential.
Primary Completion Requirements
All learners must satisfy both conditions.
Academic achievement: B+ (85%) or above in each of the three embedded courses.
Applied evidence: At least one of the following: technical/applied paper, substantial project or report, faculty reference letter, or verified experience letter aligned to the credential domain.
Alternative Completion Pathway (Equivalency)
For alumni or external learners not completing the embedded courses through standard enrollment, verified equivalent evidence may be considered through formal academic evaluation by qualified faculty designated by the College of Engineering.
Any equivalency determination is documented to ensure consistency and continued alignment with the credential’s learning outcomes and standards.
Content creation and delivery are conducted by qualified faculty within the College of Engineering.
Prof. Mohammed Ghazal, Ph.D.
Director of the Research Institute of AI and Emerging Technologies; Associate Dean of the College of Engineering; Electrical, Computer, and Biomedical Engineering Department Chair
Research leadership in AI for bioimaging, genomics, and smart systems with extensive publication record and academic and industry engagement.
Dr. Taimur Hassan, Ph.D.
Dr. Syed Omer Gilani, Ph.D.About the Micro-credential
The Deep Intelligence and Applications Micro-Credential recognizes structured achievement in artificial intelligence, machine learning, and deep learning through a defined sequence of credit-bearing courses offered by the College of Engineering at Abu Dhabi University. The credential is designed to formally acknowledge learners who demonstrate strong competency in modern data-driven intelligence techniques and their practical application to real-world problems.
The pathway integrates three courses—COE101 (Introductory Artificial Intelligence), AIRE310 (Machine Learning and Pattern Recognition), and AIRE410 (Deep Learning)—that progressively develop foundational knowledge, analytical reasoning, and advanced system design skills. Through this sequence, learners build capabilities in areas such as data preparation, model development, performance evaluation, neural network architectures, and the interpretation of intelligent system outputs.
Unlike participation-based certificates, this micro-credential is awarded based on demonstrated academic achievement in assessed coursework and verified attainment of defined learner outcomes. The structure emphasizes hands-on experimentation, programming-based problem solving, and the responsible use of artificial intelligence technologies.
The credential complements degree programs by recognizing a coherent set of AI competencies that are increasingly important across engineering and technology disciplines. It supports learners in strengthening their technical profile, demonstrating specialized skills to employers, and preparing for advanced study or professional roles involving intelligent systems and data-driven technologies.
Upon completion, recipients receive a digitally verifiable credential issued by Abu Dhabi University, enabling employers and institutions to confirm the achievement through an online verification page.
Standards & Rigor
Awarded based on demonstrated attainment through assessed, credit-bearing courses, not participation.
Accessible Pathway
Available to Abu Dhabi University students through the embedded course sequence, with an equivalency pathway for alumni or external learners through verified academic evidence.
Workforce Relevance
Emphasizes problem analysis, model development, performance evaluation, interpretation of results, and the responsible use of artificial intelligence technologies.
Three embedded courses form a vertically integrated sequence from foundations to advanced deep learning practice.
COE101 — Introductory Artificial Intelligence
Foundations of AI and data-centric practice: problem framing, preprocessing, supervised/unsupervised learning, performance evaluation, bias, ethics, and an introduction to neural networks and CNNs.
Topics covered:
- Data cleaning
- Evaluation metrics
- Bias & ethics
- Intro to NN/CNN
Prerequisite: STT100 (General Statistics)
AIRE310 — Machine Learning and Pattern Recognition
Programming-intensive ML pipelines in Python: regression/classification, clustering and dimensionality reduction, optimization (GD, momentum, Adam), and neural network implementation progressing into modern frameworks.
Topics covered:
- Python + NumPy
- Optimization
- Model selection
- Keras/PyTorch intro
Prerequisites: CSC201 (Computer Programming I), COE101 (Introductory Artificial Intelligence), MTT200 (Calculus II)
AIRE410 — Deep Learning
Advanced, application-driven deep learning: CNN-based systems, modern architectures and training strategies, recurrent models, attention/transformers, and practical exposure to LLMs and prompt engineering.
Topics covered:
- CNNs
- Transformers
- LLMs
- Applied systems
Prerequisite: AIRE310 (Machine Learning and Pattern Recognition)
Pilot implementation:
During the pilot offering, learners who achieved a grade of B+ (85%) or higher in each of the three embedded courses were considered to have satisfied the academic requirements for the micro-credential. This pilot phase was used to validate the credential structure, confirm alignment with the defined learner outcomes, and gather performance and feedback data to support continuous improvement.
Learning Outcomes
Upon completion, learners demonstrate the following outcomes.
LO-1: Explain how artificial intelligence and data-driven approaches are used to analyze problems, extract insights, and support decision-making across a range of application domains.
LO-2: Apply appropriate analytical and computational reasoning to develop and assess intelligent solutions, considering technical requirements, data characteristics, and contextual constraints.
LO-3: Evaluate the performance, strengths, and limitations of intelligent systems, and use evidence-based judgment to interpret results and inform improvement decisions.
LO-4: Integrate ethical and professional considerations into the design and use of AI-driven solutions, recognizing their broader societal, economic, and environmental implications.
LO-5: Communicate and collaborate effectively when working on intelligence-driven problems, demonstrating responsibility, adaptability, and awareness of multidisciplinary perspectives.
Credential Educational Goals
CEG-1 — Professional Application
Enable learners to apply artificial intelligence and data-driven intelligence concepts to enhance professional practice in engineering and related technical domains, supporting effective problem analysis, solution development, and informed decision-making.
CEG-2 — Career and Workforce Relevance
Support learner readiness for current and emerging roles that require competence in intelligent systems, data-driven technologies, and applied AI, thereby enhancing employability, career progression, and effective participation in professional and organizational environments.
CEG-3 — Lifelong Learning and Adaptability
Prepare learners to engage in continuous professional development and to adapt to evolving artificial intelligence technologies, tools, standards, and ethical considerations in rapidly changing technical and societal contexts.
Credential Completion Conditions
Requirements to be awarded the micro-credential.
Primary Completion Requirements
All learners must satisfy both conditions.
Academic achievement: B+ (85%) or above in each of the three embedded courses.
Applied evidence: At least one of the following: technical/applied paper, substantial project or report, faculty reference letter, or verified experience letter aligned to the credential domain.
Alternative Completion Pathway (Equivalency)
For alumni or external learners not completing the embedded courses through standard enrollment, verified equivalent evidence may be considered through formal academic evaluation by qualified faculty designated by the College of Engineering.
Any equivalency determination is documented to ensure consistency and continued alignment with the credential’s learning outcomes and standards.
Content creation and delivery are conducted by qualified faculty within the College of Engineering.
Prof. Mohammed Ghazal, Ph.D.
Director of the Research
Institute of AI and Emerging Technologies; Associate Dean of the College of
Engineering; Electrical, Computer, and Biomedical Engineering Department Chair
Research leadership in AI for bioimaging, genomics, and smart systems with extensive publication record and academic and industry engagement.
