Software automation

This lesson comprises eight (8) master classes focusing on:

• Machine Learning
• Regression models
• Neural networks
• Artificial Intelligence
• Impact of automation on the individual, society and the environment

Content:

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