Introduction to Machine Learning for Mechanical Engineers

Catalog description

This course introduces Machine Learning (ML) concepts and methods to Mechanical Engineering (ME) students. The course starts with basic supervised and unsupervised learning methods, ensemble methods, the bias-variance tradeoff, and model selection. It then covers two topics in detail: Neural Networks as a key method in the modeling of mechanical systems, and Reinforcement Learning as a key method in robotics and cyber-physical systems. The course is structured around hands-on assignments that illustrate the use of ML in addressing ME problems. It is tailored to ME students as it builds on an engineering mathematics and probability foundation along with basic programming skills but does not assume a background in algorithms.