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A1 Computer fundamentals
A1.1 Computer hardware and operation
A1.2 Data representation and computer logic
A1.3 Operating systems and control systems
A1.4 Translation (HL only)
A2 Networks
A2.1 Network fundamentals
A2.2 Network architecture
A2.3 Data transmissions
A2.4 Network security
A3 Databases
A3.1 Database fundamentals
A3.2 Database design
A3.3 Database programming
A3.4 Alternative databases and data warehouses (HL only)
A4 Machine learning
A4.1 Machine learning fundamentals
A4.2 Data preprocessing (HL only)
A4.3 Machine learning approaches (HL only)
A4.4 Ethical considerations
B1 Computational thinking
B1.1 Approaches to computational thinking
B2 Programming
B2.1 Programming fundamentals
B2.2 Data structures
B2.3 Programming constructs
B2.4 Programming algorithms
B2.5 File processing
B3 Object-oriented programming
B3.1 Fundamentals of OOP for a single class
B3.2 Fundamentals of OOP for multiple classes (HL only)
B4 Abstract data types (HL only)
B4.1 Fundamentals of ADTs
A4.3.1 Linear Regression in Predicting Continuous Outcomes (HL only)
12 minute read
A4.3.2 Classification Techniques in Supervised Learning (HL only)
14 minute read
A4.3.3 Role of Hyperparameter Tuning in Supervised Learning Algorithms (HL only)
15 minute read
A4.3.4 Clustering Techniques in Unsupervised Learning (HL only)
A4.3.5 Learning Techniques Using the Association Rule Used in Large Data Sets (HL only)
13 minute read
A4.3.6 Agents Learning to Make Decisions (HL only)
A4.3.7 Application of Genetic Algorithms (HL only)
A4.3.8 Structure and Function of ANNs (HL only)
18 minute read
A4.3.9 Design of CNNs (HL only)
20 minute read
A4.3.10 Model Selection and Comparison in Machine Learning (HL only)
A4.3 Machine Learning Approaches