Main content

In this course, students will explore the foundational principles of Machine Learning (ML) with a strong emphasis on its role in enhancing human learning and educational practices. They will develop a solid understanding of key ML concepts, including supervised and unsupervised learning, neural networks, and time-series forecasting, and examine how these methods can be applied to support teaching, personalize student learning, and improve decision-making in education. Students will study core ML algorithms—such as classification, regression, and clustering—and evaluate their effectiveness in predicting outcomes, detecting patterns, and supporting equity in learning environments. Through real-world case studies and tools like intelligent tutoring systems, multimodal analytics, and generative AI applications, learners will gain practical insights into how ML transforms classrooms and training contexts.