Course Overview

Learning Objectives

• Understand how machine learning applies to Chemical and Materials Engineering
• Understand the mathematical underpinnings of core machine learning methods
• Understand how to apply machine learning algorithms using Python
• Understand how to formulate, execute, and interpret machine learning projects as they apply in an industry setting

Featured Industry Speakers

• Jennifer Schumacher, 3M
• Brian Gettelfinger, P&G

Course Outline

Day 1: Foundations of machine learning

• Survey of applications in chemical and materials engineering
• Core mathematical tools
• Breaking down machine learning problems

Day 2: Data preparation and exploration

• Unsupervised learning
• Training and validating models
• Case study from 3M

Day 3: Supervised learning

• Linear and nonlinear models
• Interpretable machine learning
• Case study from industry

Day 4: Optimization

• Uncertainty
• Experimental design
• Case study from P&G

Day 5: Deep learning

• Representing complex data
• Fundamentals of neural networks
• Emerging topics in deep learning for materials and chemicals