Machine learning (ML) principles along with their application to mechatronic systems are introduced. Different ML paradigms, such as supervised, unsupervised, and reinforcement learning are covered. Topics include data pre-processing and feature engineering, linear and polynomial regression, anomaly detection, deep learning for computer vision, and reinforcement learning. Students learn to model, control, and optimize mechatronic systems using ML models through weekly labs, programming assignments, and a hands-on final project.