A4.3 Machine learning approaches (HL only)

This section provides an in-depth exploration of advanced machine learning approaches, highlighting a variety of algorithms and their real-world applications. Students will examine supervised learning techniques such as linear regression for predicting continuous outcomes and classification for categorical outcomes, as well as the importance of hyperparameter tuning for model optimization. The section also covers unsupervised learning through clustering and association rule mining, reinforcement learning where agents learn via environmental interaction, and the application of genetic algorithms. In addition, students will study the structure and function of artificial neural networks (ANNs), including convolutional neural networks (CNNs), and understand how these models learn complex patterns—culminating in the critical process of model selection and comparison to ensure effective and accurate solutions.