Machine Learning for Mechanical Engineers

Course Description

This course focuses on applications of state-of-the-art machine learning techniques in mechanical engineering. It also covers the fundamentals of probability and statistical learning theory. Students will gain both theoretical foundations and practical implementation skills using Python.

Topics Covered

• Introduction to Machine Learning
• Deep Learning Foundations
• Backpropagation & Optimization
• Advanced Deep Learning Architectures
• Recurrent Neural Networks (RNN & LSTM)
• Transformer Architecture & Attention Mechanisms
• Generative Adversarial Networks (GAN)
• Transfer Learning
• Large Language Models (LLM)
• Agentic AI Frameworks
• Reinforcement Learning

Lecture Slides

Prerequisites

• Basic/intermediate-level programming in Python
• Undergraduate-level mathematics (linear algebra, calculus, statistics)
• No prior ML experience required