This course focuses on the application of machine learning in sports science research and introduces the basic concepts, development trends, and research value of integrating sports science with artificial intelligence. The course begins with the fundamental principles of machine learning, including data collection, feature extraction, model training, and result validation, helping students build a foundational understanding. It then connects these concepts with common types of sports science data, such as motion capture, electromyography signals, heart rate, acceleration, and physical fitness test data, to explain how supervised and unsupervised learning methods can be used for classification, prediction, and pattern analysis. Finally, through practical examples related to sports performance evaluation, injury risk prediction, and health promotion, students will explore the potential of machine learning in both research design and real-world applications, while developing interdisciplinary analytical skills, data interpretation abilities, and the competence to use technology to address sports science problems.

Implemented by Department of Exercise and Health Sciences
Date: 2026/03/09