Machine Learning
IB Syllabus: A4 – Machine Learning (A4.1 what machine learning is and the hardware it runs on; A4.2 and A4.3 how models are trained and evaluated, HL).
Most software does exactly what a programmer told it to do, step by step. Machine learning is different: instead of writing the rules, you show a program many examples and let it work out the rules for itself. That one shift, from writing rules to learning rules from data, is behind spam filters, recommendation feeds, medical image screening, voice assistants, and the recent wave of generative AI.
These pages teach the ideas the current IB Computer Science course (first assessment 2027) expects you to understand, and they pair each idea with a hands-on simulator on the companion app so you can watch a real model learn. The concepts reach well beyond any one exam: knowing what a model can and cannot do, and how it can quietly go wrong, is now basic literacy for anyone who builds or uses software.
What This Topic Covers
| # | Topic | Syllabus | Key Concepts | Level |
|---|---|---|---|---|
| 1 | What Machine Learning Is | A4.1.1, A4.1.2 | Learning from data, the five types of machine learning and their applications, the hardware that runs it (GPUs, TPUs, ASICs, FPGAs, cloud, HPC) | SL + HL |
| 2 | Types of Learning | A4.1.1 · A4.3.2, A4.3.4, A4.3.5, A4.3.6, A4.3.7 | Supervised, unsupervised, and reinforcement learning in depth: k-nearest neighbours, decision trees, clustering, association rules, genetic algorithms | SL overview, HL depth |
| 3 | Training and Evaluating a Model | A4.2.1-A4.2.3 · A4.3.1, A4.3.3, A4.3.9, A4.3.10 | Preparing data, linear regression, overfitting and underfitting, accuracy, precision, recall and F1, neural networks and CNNs, choosing between models | HL |
The fourth part of the syllabus, the ethics of machine learning (A4.4.1) and of computing in daily life (A4.4.2), has its own topic. Once you understand how models work here, read Ethics of Machine Learning for the questions you should ask before trusting one.
How the Levels Work
Machine learning splits cleanly by level in the current syllabus:
- Everyone (SL and HL) studies what machine learning is: the types of learning, where each is used, and the hardware it runs on. That is the first page.
- HL only goes on to study how models are built and judged: the specific algorithms behind each type of learning, how data is prepared, and how you measure whether a model is any good. That is most of the second page and all of the third.
Standard level students should read page 1 in full and can skim the HL sections on the later pages for interest. Higher level students need all three. Each HL section is labelled, and you can hide HL material with the level toggle at the top of each page.
How to Use These Pages
- Start with What Machine Learning Is even if you already know the buzzwords. The precise vocabulary (a label, a feature, a model) is what the later pages and the exam build on.
- Open the linked simulator for each concept. Machine learning is much easier to believe once you have watched a boundary line shift as a model trains, or seen accuracy stay high while fairness quietly fails.
- Keep the ethics page in mind throughout. Every technique here has a responsible-use question attached to it, and the strongest answers connect the two.