Intro to Machine Learning for Chemical Engineers

Code Repository for ChE 197/ChE 297, a machine learning course for Chemical Engineers at U.P. Diliman

MLxChE: Intro to Machine Learning for Chemical Engineers

Welcome to the GitHub repository for my courses: ChE 197 and ChE 297, Intro to ML for Chemical Engineers. This repository contains lecture slides, Python codes (Jupyter notebooks), case studies, and datasets used in class. ChE 197 is an elective course for undergraduates and ChE 297 is for graduate students at the College of Engineering, University of the Philippines, Diliman. These courses aim to introduce the field of machine learning (ML) to chemical engineers, including the mathematical details, algorithms, code implementations, and practical chemical engineering applications of basic supervised and unsupervised ML methods.

This work is also published as Pilario, K.E. (2024). Teaching classical machine learning as a graduate-level course in chemical engineering: An algorithmic approach. Digital Chemical Engineering, Vol. 11, 100163, in Emerging Stars in Digital Chemical Engineering II. DOI: 10.1016/j.dche.2024.100163

Usage

The repository is organized into folders based on the weekly topics covered in the class, as follows:

• Week 1. Introduction
• Week 2. Exploratory Data Analysis
• Week 3. Linear and Logistic Regression
• Week 4. SVMs and Kernel Methods
• Week 5. Cross-validation and Hyper-parameter Tuning
• Week 6. Gaussian Processes and Bayesian Optimization
• Week 7. Neural Networks and Intro to Deep Learning
• Week 8. Trees, Weak Learners, and Ensemble Learning
• Week 9. Linear Dimensionality Reduction
• Week 10. Nonlinear Dimensionality Reduction
• Week 11. Clustering, Density Estimation, Anomaly Detection
• Week 12. AutoML and Explainability Methods

Contributing

If you find any issues or have any suggestions for improvement, feel free to contact me via [e-mail]. If any codes are not working on your terminal, let me know. :)