Machine Learning for Mechanical Engineering

Aim

After completing the course, the student will be able to

• Explain fundamental concepts in data-driven modelling and machine learning for mechanical engineering
• Process, analyze, and visualize datasets from mechanical and energy systems using appropriate computational tools
• Apply data-driven methods to extract and represent relevant structures in data from fluid, solid, and energy systems
• Implement and evaluate machine-learning models for prediction, classification, and nonlinear modelling of complex engineering systems
• Assess and justify the choice, performance, and limitations of data-driven and machine-learning methods for a given problem in engineering mechanics

Content

The course is delivered through a combination of lectures, computer laboratory sessions, and assignments. Lectures introduce fundamental concepts and methods, while laboratory sessions and assignments focus on practical implementation and application using Python programming. The final assignment provides an opportunity for in-depth application of the course methods to a mechanical engineering problem and promotes independent problem solving and critical analysis.