Machine Learning and AI

Overview

Machine learning allows computers to learn from data without being explicitly programmed. This section explores basic ML concepts, algorithms, and applications in data analysis. In Data Analytics we look at artificial intelligence as a software feature for analysing data, as well as using machine learning for making predictions or recommendations. AI is also part of our study of cyber security and ethical issues.

Key Concepts

• Supervised vs. Unsupervised Learning: Understanding different types of learning.
• Common Algorithms: Linear regression, decision trees, and clustering.
• AI in protecting data: ways AI tools can be used to protect data integrity
• Ethical use of data in AI: understanding the ethical issues posed by large machine-learning models’ use of data, including data that may be copyrighted.

Machine Learning and AI in the Study Design

Unit 3 Outcome 1

Key Knowledge:

• emerging trends in data analytics using artificial intelligence, including:
  • integration of artificial intelligence features into software tools
  • generating data visualisations through the writing and refinement of prompts
  • machine learning and statistical modelling for making predictions, decisions and recommendations

Unit 4 Outcome 1

Key Knowledge:

• effective and efficient methods to manipulate data using software tools, including:
  • use of templates
  • software functions
  • use of artificial intelligence tools to represent data and information

Unit 4 Outcome 2

Key Knowledge:

• emerging trends in cyber security, including:
  • the use of artificial intelligence to protect data
• ethical issues arising from the implementation of data and information security practices, including:
  • data collection and use

Study design key knowledge and key skills are taken verbatim from the VCE Applied Computing Study Design 2025-2028. © Victorian Curriculum and Assessment Authority. For current versions and related content visit www.vcaa.vic.edu.au

Learning Resources

• Introduction to Machine Learning
• Google’s Machine Learning Crash Course

Exercises

Exercise 1: Building a Linear Regression Model

• Use Excel or Google Sheets to create a simple linear regression model.
• Analyze the results and identify the relationship between variables.