While linear machine learning methods, such as PLS
regression, work in a very wide range of problems of chemical and biological
interest, there are times when the relationships between variables are complex
and require non-linear modeling methods. Many non-linear machine learning
methods have been developed, however, we will focus on a few that we have found
quite useful. The course begins with a discussion of linearizing transforms.
Augmenting with non-linear transforms, e.g. polynomials, is discussed next.
Locally Weighted Regression (LWR), Artificial Neural Networks (ANNs,
including Deep-learning Networks) and Support Vector Machines (SVMs)
are then considered, with SVMS for both regression and classification
considered. Boosted regression and classification trees (XGBoost)
and then covered. The course concludes with segments on how to choose a method
and how to implement models online. The course includes hands-on computer time
for participants to work example problems using PLS_Toolbox or Solo.
This course will be facilitated by Manny Palacios, Eigenvector
This one day course is offered prior to the APACT 25 conference.
For full details see https://apact.co.uk/Pre-Conference-Courses