11–12Software Engineering 11–12 Syllabus (2022)
Content
Year 12
Investigate how machine learning (ML) supports automation through the use of DevOps, robotic process automation (RPA) and business process automation (BPA)
Distinguish between Loading and ML
Explore Loading of training ML
Including:- supervised learning
- unsupervised learning
- semi-supervised learning
- reinforcement learning
Investigate common applications of key ML Loading
Including:- data analysis and forecasting
- virtual personal assistants
- image recognition
Research models used by software engineers to design and analyse ML
Including:- decision trees
- neural networks
Describe types of algorithms associated with ML
Including:- linear regression
- logistic regression
- K-nearest neighbour
Design, develop and apply ML regression models using an Loading to predict numeric values
Including:- linear regression
- polynomial regression
- logistic regression
Apply neural network models using an OOP to make predictions
Assess the impact of automation on the individual, society and the environment
Including:- safety of workers
- people with disability
- the nature and skills required for employment
- production efficiency, waste and the environment
- the economy and distribution of wealth
Explore by implementation how patterns in human behaviour influence ML and AI software development
Including:- psychological responses
- patterns related to acute stress response
- cultural protocols
- belief systems
Investigate the effect of human and dataset source bias in the development of ML and AI solutions