ECHE 589 - Machine Learning in Chemical Engineering
Instructor Prof. Shengli Jiang
Term Fall 2026
Time Mondays, Wednesdays, and Fridays, 10:50 – 11:40 AM
Location Swearingen 2A27
Course Overview
This course provides a theoretical and practical introduction to machine learning (ML) methods and their applications in chemical engineering. After an overview of ML algorithms, we will focus on specific applications (e.g., QSPR modeling, molecular simulation, materials design, spectral and image analysis, and process control) to illustrate how ML methods widely adopted in Big Tech are applied to engineering problems. Through topical literature reviews, case studies, and programming-based assignments, students will engage with state-of-the-art methodologies and gain practical experience implementing ML algorithms in an engineering context.
Prerequisites
- ECHE 456: Computational Methods for Engineering Applications
- AND one of the following: CSCE 106 (Scientific Applications Programming) or CSCE 145 (Algorithmic Design I)
Schedule
| Week | Topic |
|---|---|
| 1 | Course introduction and Python foundations |
| 2 | Machine learning paradigms and workflow |
| 3 | Regression |
| 4 | Feature engineering |
| 5 | Model selection |
| 6 | Classification |
| 7 | Neural networks |
| 8 | Unsupervised learning |
| 9 | Explainable AI and molecular representations |
| 10 | Molecular featurization and midterm presentations |
| 11 | Graph neural networks |
| 12 | Convolutional neural networks |
| 13 | Gaussian processes and uncertainty quantification |
| 14 | Bayesian optimization and active learning |
| 15 | Thanksgiving recess |
| 16 | Machine learning best practices and final presentations |