Applications Layer
Theme A, sections A3 (Databases) and A4 (Machine Learning)
Hardware executes instructions, the operating system manages resources and programs run on top of them, but what people use directly is an application: software that stores their data, answers their questions or learns from examples. This layer covers two kinds of application: databases, which organise and retrieve data, and machine learning, which uses data to train models (patterns learned from examples and used to make predictions). The pages are currently aligned with IB Computer Science.
Sections in this layer
| # | Topic | Syllabus | Key Concepts | Level |
|---|---|---|---|---|
| 1 | Databases | A3 | Relational databases from first principles to SQL: schemas, ERDs, keys, normalisation to 3NF, queries and updates. HL adds transactions, views, alternative database types, data warehouses, and distributed databases. Complete, with a practice library. | SL + HL |
| 2 | Machine Learning | A4 | How machines learn from data: the types of machine learning and where they run (SL), then data preparation, supervised and unsupervised techniques, reinforcement learning, and neural networks (HL). Complete, plus three stretch concept pages (how LLMs work, how image generation works, AI beyond machine learning) for reading past the syllabus. | SL + HL |
Ethics & Implications covers machine learning ethics (A4.4.1) and the effects of computing on daily life (A4.4.2). These have their own section because they are examined as discussion topics in their own right.
Want to build an application yourself?
Two parts of the site are about building applications rather than the examined theory of this layer:
- The JavaFX Cookbook is a recipe collection for building a desktop app in Java: tables, dialogs, file pickers, charts, and a SQLite connection when your app needs a real database.
- The Internal Assessment pages cover planning, developing and evaluating a software project for the IB CS assessment. If you are not taking IB exams, the planning and evaluation pages still work as a template for any software project.
The cookbook is under Programming; the IA guidance is under IB CS Assessments.
Suggested order
Work through Databases first if you can. Machine learning depends on handling data confidently, and several HL machine learning ideas (data cleaning, feature selection) are easier to follow once you have normalised and queried datasets yourself. Machine Learning can still be studied on its own.
Learning Objectives
By the end of this layer, you should be able to:
- Explain the features, benefits, and limitations of relational databases, and design one normalised to 3NF (A3)
- Construct SQL queries and updates across multiple tables (A3)
- Describe the types of machine learning and their real-world applications and hardware requirements (A4)
- HL: evaluate advanced database approaches and explain core machine learning techniques, from regression to neural networks (A3/A4 HL)
Connections
- Programming Layer: data structures and OOP are the substrate every application is built from
- Information Layer: how the data that applications manage is actually encoded
- Ethics & Implications: what applications, especially learning ones, owe the people they affect