Non-linear Machine Learning for Calibration and Classification at CAC

June 29, 2026

Non-linear Machine Learning for Calibration and Classification will be presented at the 2026 Chemometrics in Analytical Chemistry conference in Tarragona, Catalonia (SPAIN). It is based on our Eigenvector University course of the same name.

Course Description

This course offers a comprehensive overview of modern machine learning with an emphasis on non-linear, supervised methods. Students begin by establishing a strong foundation in machine learning fundamentals—including essential nomenclature, clear definitions, and key quality metrics—while also exploring the critical concepts of bias vs. variance trade-off and the distinctions between supervised and unsupervised methods.

The course focuses on several pivotal machine-learning algorithms:

• Locally Weighted Regression: Techniques for localized modeling using weighted regression and distance measures.
• Support Vector Machines: Core principles, kernel functions, and both classification and regression applications.
• Artificial Neural Networks: A study of various ANN architectures, effective training procedures, strategies to prevent overfitting, and practical deep learning implementations.
• Gradient Boosted Decision Trees: An introduction to decision trees, focusing on gradient boosting concepts and the practical use of tools like XGBoost.

Hands-on example problems will be worked using Eigenvector’s PLS_Toolbox and Solo software. Participants are encouraged to bring their laptops with the software installed so they can follow along.

About the Instructor

Non-linear Machine Learning will be led by Eigenvector Research President and PLS_Toolbox creator Barry M. Wise, Ph.D.

Prerequisites

Participants should have a working knowledge of linear algebra, Principal Components Analysis (PCA), Partial Least Squares (PLS) regression and PLS Discriminant Analysis (PLS-DA).

Course Outline

1. Introduction 
   – Nomenclature and Definitions.
   – Methods: Unsupervised vs. Supervised.
   – Bias vs. variance trade-off.
   – Model Quality metrics.
   – Data Preprocessing
2. Review (if needed)
   – Data Preprocessing
   – Principal Components Analysis (PCA)
   – Partial Least Squares (PLS) Regression
   – PLS Discriminant Analysis (PLS-DA)
3. Non-Linear Machine Learning Algorithms (Methods)
   – Locally Weighted Regression.
   – Support Vector Machines.
   – Artificial Neural Networks.
   – Gradient Boosted Decision Trees. (a brief overview)
   – Model Fusion (Model Ensembles)
4. Interpreting Non-linear Models
   – Interferent tests.
   – Single variable tests.
   – Shapley values.
5. Choosing the Right Method and Final Remarks:
   – Computational performance, and deployment options for effective model selection.

Registration

Registration will be handled by the CAC conference as described on the Preliminary Courses page.