Speed Dating Chemometrics and Machine Learning

There is a lot of confusion on what constitutes best practices in the application of multivariate statistics to laboratory, process, and field analytics. The terminology in use does not always clarify and most of the time a technique touted in the literature is not compared to any other technology that could be applied to the same problem. The focus here is to refresh the basics of the field of multivariate analysis and data visualization plus how the history of the field now ties to machine learning. The course illustrates the principles of chemometrics as they apply to routine product quality maintenance, primarily on the most common use of the algorithms in organizing the information flow from sensors, spectrometers, and chromatographs.
An introduction to data visualization and exploratory data analysis techniques such as principal component analysis (PCA) and hierarchical cluster analysis (HCA) will be covered along with the practical basis for their use. A wide variety of examples will be shown ranging from laboratory analysis, in-line and on-line process monitoring, and field applications. The course covers the thought process that helps organize and complete the implementation of an application-specific evaluation system.
These topics are designed to cover the best practices of chemometrics technology and will prepare participants for tackling a vast array of problems. This course is useful for any scientist concerned with optimizing their analytical methods to get the most out of either laboratory or process operations.