Software automation#
Algorithms in machine learning#
Investigate how machine learning (ML) supports automation through the use of DevOps, robotic process automation (RPA) and business process automation (BPA)
Distinguish between artificial intelligence (AI) and ML
Explore models of training ML
Including
supervised learning
unsupervised learning
semi-supervised learning
reinforcement learning
Investigate common applications of key ML algorithms
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
Programming for automation#
Design, develop and apply ML regression models using an OOP to predict numeric values
Including
linear regression
polynomial regression
logistic regression
Apply neural network models using an OOP to make predictions
Significance and impact of ML and AI#
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