This blog was born out of several years of teaching a graduate course, Chemometrics. In it, I teach students how to use statistics and Machine Learning tools in MATLAB to solve Chemistry problems
You can access some of the Machine Learning for Chemistry course materials including a detailed topical outline, homework tutorials and in-class activities at the GitHub repository.
The blog
Along the way, I’ve found that students
- really like short examples of how to do things in MATLAB
- learn more technical Machine Learning concepts when they’re broken down into “bite-sized” pieces
- are all different and have different ways of learning
So, I thought that documenting some of these pieces of advice, explanations and examples would be a good idea. Now, anyone can peruse the posts and find something that will help them out when they need it. They no longer have to rely solely on notes taken from class or wait until we cover a specific topic in an upcoming class.
I hope you, too, will benefit from these posts and maybe even enjoy looking around!
My story
I’m a professor of Chemistry at the University of Georgia. My research lab builds instruments to measure how aerosol particles in the atmosphere interact with sunlight. We also use Machine Learning to identify clusters of aerosols with similar optical properties. Find out more at: SmithLab group website
I teach undergraduate Thermodynamics and Statistical Thermodynamics (CHEM 3212), graduate Chemometrics - Machine Learning for Chemistry (CHEM 8860) and a First-Year Odyssey course on baseball statistics (yes, freshman can take a 1-credit class in which they learn about advanced baseball stats!).