Machine Learning for Calibration and Classification

While linear methods, such as PLS regression, work in a very wide range of problems of chemical interest, there are times when the relationships between variables are complex and require non-linear modeling methods. Many non-linear methods have been developed, however, we will focus on a few that we have found quite useful when dealing with data from chemical systems. The course begins with a discussion of linearizing transforms. Augmenting with non-linear transforms, e.g. polynomials, is discussed next. It is then shown how Locally Weighted Regression (LWR) and Hierarchical Models (HM) can handle non-linearity by using linear sub-models. More difficult non-linear relationships can be handled using Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), and Gradient-boosed Ensemble methods (XGBoost) for both regression and classification analysis. These methods are explained in detail and the meta-parameters associated with them discussed. The course includes hands-on computer time for participants to work example problems using PLS_Toolbox or Solo.