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Data Science and Machine Learning in Chemical Engineering

Course Information

Syllabus

Lectures

Module 00: Introduction
Module 01: NumPy Fundamentals
Module 02: Pandas Introduction
Module 03: Intermediate Pandas
Module 04: Feature Engineering
Module 05: Dimensionality Reduction
Module 06: Linear Regression
Module 07: Classification
Module 08: Regularization and Model Selection
Module 09: Nonlinear Methods
- Neural Networks and Hyperparameter Tuning
Module 10: Ensemble Methods
Module 11: Clustering
Module 12: Uncertainty Quantification
Module 13: Model Interpretability

Optional Lectures

Optional: Working with Databases
Optional: Introduction to Deep Learning
Optional: Symbolic Regression
Optional: LLMs for Scientific Research

Assignments

Homework 1: NumPy Fundamentals
Homework 2: Pandas Introduction
Homework 3: Intermediate Pandas
Homework 4: Feature Engineering
Homework 5: Dimensionality Reduction
Homework 6: Linear Regression
Homework 7: Classification
Homework 8: Regularization and Model Selection
Homework 9: Nonlinear Methods
Homework 10: Ensemble Methods
Homework 11: Clustering
Homework 12: Uncertainty Quantification
Homework 13: Model Interpretability
Course Project: Machine Learning for Chemical Engineering

Participation

In-Class Participation Exercises
Module 00: Introduction - Participation Exercises
Module 01: NumPy Fundamentals - Participation Exercises
Module 02: Pandas Introduction - Participation Exercises
Module 03: Intermediate Pandas - Participation Exercises
Module 04: Feature Engineering - Participation Exercises
Module 05: Dimensionality Reduction - Participation Exercises
Module 06: Linear Regression - Participation Exercises
Module 07: Classification - Participation Exercises
Module 08: Regularization & Model Selection - Participation Exercises
Module 09: Nonlinear Methods - Participation Exercises
Module 10: Ensemble Methods - Participation Exercises
Module 11: Clustering - Participation Exercises
Module 12: Uncertainty Quantification - Participation Exercises
Module 13: Model Interpretability - Participation Exercises

Reference

Data Detective Academy Characters
Data Science Quick Reference Sheet
Execution Statistics
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By John Kitchin

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