Programme content
AI systems and modern model families
Guided explanation, practical examples and an applied task focused on ai systems and modern model families.
Machine-learning workflow and evaluation
Guided explanation, practical examples and an applied task focused on machine-learning workflow and evaluation.
Neural-network and deep-learning concepts
Guided explanation, practical examples and an applied task focused on neural-network and deep-learning concepts.
Natural-language processing
Guided explanation, practical examples and an applied task focused on natural-language processing.
Computer vision
Guided explanation, practical examples and an applied task focused on computer vision.
Generative AI and large language models
Guided explanation, practical examples and an applied task focused on generative ai and large language models.
Prompting, retrieval and workflow design
Guided explanation, practical examples and an applied task focused on prompting, retrieval and workflow design.
AI agents, automation and tool integration
Guided explanation, practical examples and an applied task focused on ai agents, automation and tool integration.
Risk, bias, privacy, governance and human oversight
Guided explanation, practical examples and an applied task focused on risk, bias, privacy, governance and human oversight.
Capstone: design, test and present an AI solution
Guided explanation, practical examples and an applied task focused on capstone: design, test and present an ai solution.
Learning outcomes
- Explain the core technical concepts accurately.
- Apply the relevant methods in guided practical work.
- Test and improve outputs systematically.
- Complete an applied challenge or capstone.
- Present the final work and justify key decisions.