ECHE 589 - Machine Learning in Chemical Engineering

This course provides a theoretical and practical introduction to machine learning (ML) methods and their applications in chemical engineering. Students will learn about supervised and unsupervised learning, model evaluation, and practical implementations in areas such as materials design and process engineering.

Instructor: Prof. Shengli Jiang

Term: Fall

Location: TBD

Time: Mondays, Wednesdays, and Fridays, 10:50 – 11:40 AM

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)

Textbooks

  • Suggested: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron
  • Suggested: Python Machine Learning (3rd Edition) by Sebastian Raschka and Vahid Mirjalili

Grading

  • Programming Assignments (4): 40%
  • Mid-term Presentation: 10%
  • Mid-term Report: 10%
  • Final Project Presentation: 30% (UG) / 15% (Grad)
  • Final Project Report: 0% (UG) / 15% (Grad)
  • Interactive Engagement: 10%

Schedule

Week Date Topic Materials
1 Week 1 Course Introduction

Overview of machine learning, course structure, and expectations.
2 Week 2 Overview of Machine Learning

Fundamental concepts, branches of ML, and math review.
3 Week 3 Regression

Linear and nonlinear regression, gradient descent, and model evaluation.
4 Week 4 Feature Engineering and Model Selection

Data representation, feature engineering, and model selection techniques.
5 Week 5 Classification

Logistic regression, decision boundaries, and multi-class classification.
6 Week 6 Neural Networks Fundamentals

Introduction to neural networks, TensorFlow/Keras, and molecular featurization.
7 Week 7 Unsupervised Learning and Explainable AI

Unsupervised methods, explainable AI, and best practices.
8 Week 8 Final Project Presentations

Student project presentations and course wrap-up.