Learning outcome
1.1

Strategy and planning
1.2

Security and privacy
1.3

Governance, risk and compliance
1.4

Advice and guidance
2.5

Change implementation
2.6

Change analysis
2.7

Change planning
3.8

Systems development
3.9

Data and analytics
3.10

User experience
3.11

Content management
3.12

Computational science
4.13

Technology management
4.14

Service management
4.15

Security services
5.16

People management
5.17

Skills management
6.18

Stakeholder management
6.19

Sales and marketing
A1

Demonstrate judgement in identifying and adapting machine-learning methodologies to meet requirements 
A2

Effectively communicate machine-learning concepts or proposed solutions 
K1

Identify and explain a range of deep-learning architectures and methodologies for solving complex problems;
K2

Recognize complex modelling scenarios such as potential biases in data, and noise and confounding factors that may impact model performance 
K3

Critically evaluate merits and limitations of artificial intelligence architectures, including societal and environmental impacts
S1

Design and implement prototypical solutions to complex problems that meet industry guidelines, including strategies to mitigate assessed risks   
S2

Develop methodologies to evaluate and monitor machine learning models, prior to and after deployment
Learning outcome
1.1

ICT Fundamentals

1.2

ICT Infrastructure

1.3

Information & Data Science and Engineering

1.4

Computational Science and Engineering

1.5

Application Systems

1.6

Cyber Security

1.7

ICT Projects

1.8

ICT Management and Governance

2.1

Professional ICT Ethics
2.2

Impacts of ICT

2.3

Working Individually and in ICT development teams

2.4

Professional Communication

2.5

The Professional ICT Practitioner

A1

Demonstrate judgement in identifying and adapting machine-learning methodologies to meet requirements 
A2

Effectively communicate machine-learning concepts or proposed solutions 
K1

Identify and explain a range of deep-learning architectures and methodologies for solving complex problems;
K2

Recognize complex modelling scenarios such as potential biases in data, and noise and confounding factors that may impact model performance 
K3

Critically evaluate merits and limitations of artificial intelligence architectures, including societal and environmental impacts
S1

Design and implement prototypical solutions to complex problems that meet industry guidelines, including strategies to mitigate assessed risks   
S2

Develop methodologies to evaluate and monitor machine learning models, prior to and after deployment