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Machine learning (ML)
Term Key terms bank Y12 Software Automation
Also listed as: Machine learning
Definition
A branch of AI where a program learns patterns from data to make predictions or decisions without being explicitly programmed with fixed rules.
Example: A spam filter learns from labelled emails.
Found in: syllabus, course specifications, 2025 HSC marking guidelines, sample exam and familiarisation guidelines, school notes.
Algorithms that learn patterns from data.
Where it is used in the test
Syllabus area: Y12 Software Automation.
Question types it can appear in
- Multiple choice: Pick the option that matches the definition or example.
- Short answer: Define, outline or explain it and tie it to the scenario.
- Extended response: Use it as a supporting point linked to the stimulus.
- Fill in the blanks: Choose this term to complete a statement or table.
Where it has appeared in the paper
Software Automation (Part B, C). Part letters follow each paper’s own layout.
Related past questions
- Sample exam Q15 (3 marks): Machine learning in data analysis Software Automation (Part B) · 3 marks
- Familiarisation Q14 (8 marks): AI and ML in society; privacy Software Automation (Part C) · 8 marks
These are the questions the flashcard set links to this term. Open each one and compare your answer with its marking criteria.
Other questions that mention it
- Sample exam Q25 Software Automation (Part B) · 8 marks
- Sample exam Q23a Software Automation (Part B) · 3 marks
- 2025 HSC exam Q3 Software Automation (Part A) · 1 mark
- Sample exam Q20a Software Automation (Part B) · 1 mark
Study links
- Flashcard: Machine learning (ML) in Software Automation (Year 12) · study that set
- Memorise: AI v ML; ML supporting automation (DevOps, RPA, BPA)
- Key terms bank entry: Machine learning
- Notes: Investigate how machine learning (ML) supports automation through the use of DevOps,… (Software Automation (Year 12))
- Master answer: sample-q25 (8 marks, extended response (discuss, max 300 words, slideshow stimulus)) (Master model answers: Software Automation)
- Notes: AI v ML; ML supporting automation (DevOps, RPA, BPA) (Software Automation (Year 12))
- Notes: Distinguish between artificial intelligence (AI) and machine learning (ML) (Software Automation (Year 12))
How to use it in an answer
A model sentence
“A branch of AI where a program learns patterns from data to make predictions or decisions without being explicitly programmed with fixed rules.” Then apply it: A spam filter learns from labelled emails.
In a short answer, give the definition in your own words first, then tie it to the situation in the question. Do not copy the example above unless it fits the scenario.
Command words used with it
- Describe: Provide characteristics and features. (Sample exam Q15 (3 marks): Machine learning in data analysis)
Technique for the question types
- Multiple choice: Read all four options, rule out wrong ones, pick one; no penalty for guessing so never leave blank.
- Short answer: One clear point per mark, use the stimulus and course terms, and answer the command word.
- Extended response: Plan with the command word, give a judgement or explanation supported by named details from the stimulus, use course terms and cover both sides when asked.
- Fill in the blanks: Choose the right word from a list; use exact syllabus terms.
What the marking guidelines and feedback say
The MG 'sample answer' is NOT a written response: it lists the syllabus dot points the question draws on (ML training models; ML applications; impact of automation on individual, society and environment; patterns in human behaviour; human and data set source bias; security, privacy, regulatory compliance; privacy by design; social, ethical and legal issues; data mining; streaming service management). The slideshow is not available. Use these dot points as a checklist for a judgement answer: state your judgement, give advantages and disadvantages, and cite slideshow items.
Worked answers from the marking guidelines
Sample exam Q15 (3 marks): Machine learning in data analysis (3 marks): criteria and sample answer
Criteria
3: Describes how ML algorithms can be used in data analysis. 2: Outlines some features of ML algorithms relevant to data analysis. 1: Some relevant information.
Sample answer
Machine learning algorithms can be trained with a subset of the data to automatically identify patterns and trends within large datasets. The algorithms can handle complex relationships and nonlinearities present in the data allowing for efficient and comprehensive analysis. Machine learning can also be used for predictions and forecasting. These models can learn from new data over time, continuously improving their analysis and predictive accuracy and performance.
Common mistakes
- Use the term exactly as the syllabus words it, then link it to the scenario in the question; the 2025 feedback repeatedly rewards using the stimulus.
- Answer the command word: outline gives main points only, explain gives a reason or cause, discuss gives both sides.
Related terms
Same group in the syllabus: Artificial intelligence (AI) · Distinguishing AI and ML · DevOps · Robotic process automation (RPA) · Business process automation (BPA)