Machine Learning in Chemistry 101

This graduate-level course gives an overview of the machine learning (ML) techniques that are useful for solving problems in Chemistry, and particularly for the computational understanding and predictions of materials and molecules at the atomistic level. In the first part of the course, after taking a quick refresher of the basic concepts in Probabilities and Statistics, students will learn about basic and advanced ML methods including supervised learning and unsupervised learning. During the second part, the connection between Chemistry and the mathematical tools of ML will be made, and the concepts on the construction of loss functions, representations, descriptors and kernels will be introduced. For the last part, experts who are actively using ML methods to solve research problems in Chemistry and Materials will be invited to give real world examples on how did ML methods transformed the way they perform research.