Getting Started with Machine Learning in General Chemistry - Part 1. Supervised learning models.

Welcome to Machine Learning in General Chemistry - Part 1!
In this series, we’ll explore the exciting intersection of machine learning and chemistry. Whether you’re just starting with machine learning or looking to apply it to real-world chemistry problems, this course will give you the hands-on experience you need to understand the power and potential of machine learning in scientific research. Through four engaging labs, we’ll investigate topics like predictive modeling, classification, and image analysis, all while focusing on their practical applications in chemistry.
Later this year, we will perform real machine learning experiments. You now know what to look for!
🔬 Course Overview:
This course consists of four labs designed to introduce chemistry students to the fundamentals of machine learning:
Graphical and Numerical Evaluation of Machine Learning Results: Learn how to evaluate the performance of machine learning models using predicted vs. actual plots and residuals.
Exploring Machine Learning Models in Cobberland: Apply machine learning models to predict relationships between material properties in a fictional world.
Polymer Electron Microscopy Image Inspector and Sorter: Use Convolutional Neural Networks (CNNs) to classify synthetic polymer images for quality control.
Exploring Surface Reactivity Through Machine Learning: Analyze crystallographic images using CNNs to predict surface reactivity and understand the impact of surface defects.
🌟 Key Takeaways:
Understanding the basics of machine learning in chemistry
Learning how to evaluate models using graphical and numerical methods
Applying machine learning to practical chemistry problems like property prediction and image classification
Gaining experience with tools like CNNs for advanced image analysis
📚 Educational Value:
This series is designed to help chemistry students understand the growing role of machine learning in scientific research. By the end of the course, you’ll be equipped to apply machine learning techniques to a variety of chemistry-related tasks, from predicting molecular properties to analyzing complex datasets.
💡 Hands-On Labs:
Each lab is designed to build your skills step by step, providing practical experience in:
Model evaluation
Predictive modeling
Image classification with CNNs
Applying machine learning to real-world chemistry problems
🔗 Useful Resources:
Course Website: Machine Learning in General Chemistry
Instructor's Website: www.darinulness.com
Concordia College Chemistry Department: Concordia Chemistry
GitHub Repository: Machine Learning General Chemistry
👍 Stay Connected:
Ready to explore how machine learning can transform the way we approach chemistry? Make sure to like, share, and subscribe for more deep dives into chemistry and machine learning.
#MachineLearning #GeneralChemistry #ChemistryEducation #ConcordiaCollege #GNLProject