IB DP Computer Science
DP
Machine Learning
All knowledge base resources related to Machine Learning.
This category introduces Higher Level students to the fundamental principles and real-world applications of machine learning. It emphasizes the use of data-driven models that enable systems to identify patterns, make predictions, and adapt over time without explicit programming. Students will explore key ML concepts such as supervised and unsupervised learning, training and testing data, overfitting, classification, and clustering.
Mathematical underpinnings, such as loss functions and optimization, are introduced conceptually to support algorithmic understanding. Ethical considerations, data bias, and interpretability are discussed to foster critical thinking about the societal implications of intelligent systems. Students will engage with practical tools and libraries to implement basic ML models, evaluate their performance, and reflect on the power and limitations of modern AI.