06-325 Numerical Methods and Machine Learning for Chemical Engineering

6 units, Carnegie Mellon University

This course will focus on applying numerical methods and machine learning to chemical engineering problems. Students will learn how modern programming environments (on laptops and in the cloud) can run python code. Programming concepts such as defining functions and plotting quantities will be reviewed. Students will learn how to apply and debug numerical integration techniques to systems of ODEs. Solving systems of nonlinear equations and black-box optimization will be covered. Machine learning will be introduced starting with the statistics of linear and non-linear regression with regularization. Polynomial fitting and interpolation will be covered. With this base, students will learn how to apply machine learning techniques such as gaussian process regression and neural networks to regression tasks. A small project will be included near the end to encourage creative applications to chemical engineering problems.

Prerequisites: 06-262 and (15-110 or 15-112)

Offered: Fall 2025

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