edgarsmdn/MLCE_book
Hands-on material for a Machine Learning in Chemical Engineering course
This resource provides hands-on tutorials for applying machine learning techniques to real-world problems in chemical engineering. It takes raw process data or simulation outputs and demonstrates how to apply supervised, unsupervised, reinforcement learning, data-driven optimization, and hybrid modeling. This is ideal for chemical engineering students, researchers, and practitioners looking to integrate AI into their work.
123 stars. No commits in the last 6 months.
Use this if you are a chemical engineer or student who wants to learn how to apply various machine learning methods to solve problems like process optimization, fault detection, or material design.
Not ideal if you are looking for a theoretical textbook on machine learning or if your primary field is outside of chemical engineering applications.
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Jupyter Notebook
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Apache-2.0
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Last pushed
Aug 18, 2023
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