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Non-Linear Machine Learning for Calibration and Classification

November 12, 2023

Plainsboro, NJ

Barry M. Wise, Ph.D., President, Eigenvector Research, Inc.
Manuel A. Palacios, Ph.D., Principal Consultant and Data Scientist, Eigenvector Research, Inc.

Crowne Plaza Princeton Conference Center
Plainsboro, NJ

Course Description:

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.

Who Should Attend:

Chemists, life scientists and engineers who want to be able to analyze their own laboratory or process data or develop their own data models. The course is especially well suited for those with an interest in process analytical technology (PAT) in the pharmaceutical industries, metabolomics, and systems biology. The courses serve individuals with a need for exploratory data analysis, development of predictive models such as analytical instrument calibrations, sample classification, and soft sensor models.


– Why non-linear methods?
– How linear methods deal with non-linear data
Locally Weighted Regression
– Weighted Regression
– Distance Measures
– Basing Models on PCA Scores
Support Vector Machines
– SVM basics
– Kernel functions
– Classification Models
– Regression Models
Artificial Neural Networks
– ANN structures
– Training procedures
– Avoiding overfitting
– Deep-learning networks using Sklearn and TensorFlow
Gradient Boosted Decision Trees
– Intro to decision trees
– Classification and Regression Ensemble Models
– XGBoost
Choosing the right method
– Prediction skill
– Computational performance
– Deployment options

To Register:
This course will be presented prior to the Eastern Analytical Symposium. Register here.