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Non-linear Machine Learning for Calibration and Classification

September 22, 2025

Glasgow, Scotland


Eigenvector Research, Inc. is pleased to offer Non-Linear Machine Learning for Calibration and Classification, a live, in-person short course showing how to use the advanced machine learning methods in Eigenvector’s PLS_Toolbox and Solo. This course will be held at the Advances in Process Analytics and Control Technologies (APACT) conference in Glasgow, UK.

Complete information about the course can be found by following the links below.

Target Audience

Non-Linear Machine Learning for Calibration and Classification is aimed at spectroscopists and other scientists who want to be able to use machine learning methods to develop their own non-linear models for calibration/regression or sample classification. It is recommended that participants be familiar with Principal Components Analysis (PCA) and multivariate regression methods such as Partial Least Squares (PLS). Courses in these topics can be found on the EigenU Recorded Courses page.

Course Description

The material for this in-person course is drawn from our popular Eigenvector University Series. The course focuses on machine learning methods designed to deal with non-linear data. Emphasis will be on applying these techniques to spectroscopic, especially NIR data. Methods for instrument calibration, sample classification and exploratory data analysis will be covered.

The course will focus on how to use the machine learning methods in Eigenvector’s PLS_Toolbox and Solo software. In order to take advantage of these hands-on examples participants should equip their computers with current versions of Solo or PLS_Toolbox (and MATLAB) installed. Demo copies will work just fine. Users with Eigenvector accounts can download free demos. If you don’t have an account, start by creating one.

About the Instructor

The course will be led by Eigenvector’s Principal Consultant and Data Scientist Manuel A. Palacios. Manny has delivered many chemometrics courses at scientific conferences, on-site for companies, on-line and at our popular Eigenvector University each year in Seattle.

Registration and Fees

Please register through the APACT website. Fees are £300 for those attending the APACT Conference and £500 for those attending the course only.

Venue

Non-Linear Machine Learning will be held at the Hilton Glasgow just prior to the 2025 APACT conference.

Schedule

Schedule, Central European Time (CET)
10:30 – 11:00 Registration and check-in
11:00 – 12:30 Instruction
12:30 – 13:30 Lunch (provided)
13:30 – 15:00 Instruction
15:00 – 15:30 Tea and coffee
15:30 – 17:00 Instruction
17:00 – 17:30 Wrap-up Discussion and Questions

Course Outlines

Non-Linear Machine Learning for Calibration and Classification will cover the follow topics:

  • Introduction to Machine Learning
    • Nomenclature and Definitions
    • Methods: Unsupervised vs. Supervised
    • Bias vs. Variance Trade-off
    • Model Quality Metrics
  • Machine Learning Algorithms (Methods) – Part 1
    • Locally Weighted Regression (LWR)
    • Support Vector Machines (SVM)
    • Artificial Neural Networks (ANN)
  • Machine Learning Algorithms (Methods) – Part 2
    • Gradient Boosted Decision Trees (brief overview)
    • Model Fusion (Model Ensembles)
    • Choosing the Right Method
    • Final Remarks