Course information
A4 · Higher Level

Machine learning

Investigate how models learn from data and how to evaluate their results.

DP1 introduction · May–June 2027; HL depth in DP2 · proposed

Learning goals

  • Distinguish types of learning and suitable applications.
  • Discuss data quality, bias and ethical implications.
  • Extend to model construction and evaluation during DP2.

In practice

  1. Explore a small labelled dataset.
  2. Compare model outputs with expected outcomes.
  3. Explain limitations and risks in a real application.

Show understanding

A short investigation with interpreted results and explicit limitations.

Predict first. Implement second. Test deliberately. Explain what the evidence shows.

Textbook: Baumgarten, Ganea & Turland. Topic codes follow the course; the book’s teaching order is not strictly numerical. Exact reading and submission requirements are set with each assignment.