The Role of Derivatives in Machine Learning
Fernando Luís Almeida, Carlos Sousa, Catarina Lucas · Advances in computational intelligence and robotics book series · 2025
This chapter explores the critical role of derivatives in optimizing cost functions and driving the backpropagation algorithm in neural networks, emphasizing their applications in the education field. The study examines the use of derivatives in personalized learning systems, particularly within the Khan Academy platform, and evaluates their impact on scalability, bias, and efficiency. Five research questions guide the analysis, ranging from environmental impact to fairness in AI-driven education. Employing methods like Experimental Performance Evaluation and Comparative Analysis, the study offers both technical insights and ethical considerations. While derivatives enable precise optimization, the chapter highlights how they can unintentionally reinforce biases in training data, raising critical concerns about fairness and representation in educational technologies.