Component

No.

Full

Module Code

Module Title

Compulsory / Core / Optional

NQF Level

Credits

PGAI01

COM101

Managing Innovation and Computing

Core

Level 7

10

PGAI01

DAT105

Business Intelligence Systems

Core

Level 7

10

PGAI01

SEC121

Implementing and Managing Cybersecurity

Core

Level 7

10

PGAI01

SDU124

System development and User Experience (UX)

Core

Level 7

10

PGAI01

DIV101

Data Insights and Visualisation

Core

Level 7

10

PGAI01

SESD105

Software Engineering and Systems Design

Core

Level 7

10

PGAI01

AI105

Artificial Intelligence

Core

Level 7

10

PGAI01

DLCV110

Deep Learning for Computer Vision

Core

Level 7

10

PGAI101

ML123

Machine Learning

Core

Level 7

10

A.  Knowledge and understanding

Learning outcomes

A1

Demonstrate a comprehensive understanding of core AI concepts including supervised, unsupervised, and reinforcement learning.

A2

Evaluate the role of AI in real-world applications such as natural language processing, computer vision, and robotics.

A3

Understand the ethical, societal, and legal implications of deploying AI systems.

A4

Critically assess AI model performance and explainability in practical scenarios.

A5

Demonstrate knowledge of the underlying mathematical foundations of AI, including linear algebra, probability, and optimisation.

Learning methods

Lectures and Seminars, Workshops and Labs, Case Studies and Simulations, Individual and Group Projects, Industry Guest Lectures, Online Learning and VLE

Assessment methods

Coursework, Practical Reports, Examinations, Presentations, Research Papers

B.  Intellectual/cognitive skills

Learning outcomes

B1

Analyse complex problems and design AI-based solutions with appropriate modelling and algorithms.

B2

Evaluate alternative approaches to AI model development based on performance metrics and data characteristics.

B3

Interpret AI research literature and assess its relevance and applicability to given challenges.

B4

Apply critical thinking to assess the risks and benefits of deploying AI in different domains.

Learning methods

Lectures and Seminars, Journal Clubs, Critical Reviews, Case Studies, Research Projects

Assessment methods

Essays, Project Reports, Peer Reviews, Oral Presentations

C.  Practical and professional skills

Learning outcomes

C1

Develop and deploy AI models using modern programming tools such as Python, TensorFlow, and PyTorch.

C2

Manage and preprocess datasets using data wrangling, transformation, and feature engineering techniques.

C3

Use version control, testing, and documentation in AI software development.

C4

Collaborate in multidisciplinary teams to deliver data-driven AI solutions.

Learning methods

Lectures and Seminars, Workshops and Labs, Case Studies and Simulations, Individual and Group Projects, Industry Guest Lectures, Online Learning and VLE

Assessment methods

Code Submissions, Technical Reports, Group Projects, Software Demos

D.  Key Skills

Learning outcomes

D1

Communication

Present AI concepts and project findings effectively through reports, visualisations, and oral delivery.

D2

Information Technology

Use programming, data platforms, cloud services, and ML tools to develop AI applications.

D3

Numeracy

Apply mathematical reasoning to model development, tuning, and validation.

D4

Problem solving

Identify, frame, and solve technical and strategic problems using AI techniques.

D5

Working with others

Engage collaboratively in teams, respecting diverse viewpoints and roles.

D6

Improving own learning and performance

Reflect on feedback, monitor progress, and pursue continuous skill development.

Learning methods

Lectures and Seminars, Labs, Group Activities, Reflective Practice, Self-directed Study

Assessment methods

Portfolios, Presentations, Peer Evaluations, Reflective Journals, Group Reports