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Machine Learning Summer School 2026

August 4, 2026 - August 6, 2026


Eigenvector Research, Inc. is pleased to offer Machine Learning Summer School 2026 (MLSS-26), a live webinar-based short course covering basic and advanced machine learning methods with applications in chemometrics and chemical data science.

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

Course Description

Generative Artificial Intelligence (AI) may capture most of the headlines but Predictive AI, also known as Machine Learning, is what controls and monitors chemical processes, food and beverage production, pharmaceutical manufacturing and many more applications. Machine learning models also power medical diagnostic tests, agricultural product grading, oil and gas production and chemical threat detection. MLSS-26 covers basic linear methods along with more advanced non-linear methods for pattern recognition, regression/quantitative analysis and classification. Emphasis will be on applying these techniques to spectroscopic and sensor data in the chemical process and laboratory environment.

This course will be delivered via webinar in three segments of three and a half hours each. The course material is drawn from our popular Eigenvector University series.

The course will include many follow-along examples. In order to take advantage of these, 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. If you have not used our software before, we recommend that you download it and try it out before the course. We have many recorded webinars which will help you get started and show how to use many popular chemometric and machine learning methods for pattern recognition, instrument calibration, sample classification and exploratory data analysis.

Target Audience

MLSS-26 is aimed at engineers, chemists, spectroscopists and other scientists who want to be able to use pattern recognition methods to analyze complex data and develop their own linear and non-linear Predictive AI models for calibration/regression or sample classification. Examples include development of analytical instrument calibrations, soft sensor models, sample classification, or analysis of designed data sets. It is recommended that participants be familiar with Principal Components Analysis (PCA) and multivariate regression methods such as Partial Least Squares (PLS) though these will be covered briefly. Courses in these topics can be found on the EigenU Recorded Courses page.

About the Instructors

The course will be led by Eigenvector President and PLS_Toolbox creator Barry M. Wise. He will be assisted by Eigenvector Vice-president Neal B. Gallagher, and Senior Software Developer Donal O’Sullivan. Eigenvector Research has delivered hundreds of chemometrics and machine learning courses at scientific conferences, on-site for companies, on-line and at our popular Eigenvector University each year in Seattle.

Course Fee

Prices include instruction, course materials (provided in advance in .pdf format), access to the recorded version of the course, and a certificate of completion.

Prices shown are shown below. Payment must be received by 5pm PDT, Thursday, July 30, 2026.

Regular
Academic
Machine Learning Summer School
$875
$225

Note: Payment must be received by 5pm PDT, Thursday, July 30, 2026. Credit card orders are strongly encouraged. Acceptable forms of payment include MasterCard, VISA, American Express, and checks drawn on a US bank. Wire transfers can also be arranged.

Academic discount: University students and faculty are eligible for the academic rate. Verification of University affiliation is required by providing valid university mailing and e-mail address. Note that we define academic as “degree granting institution.”

How to Register, Deadlines and Cancellations

To register, login to your Eigenvector account, or create an account, then select “Machine Learning Summer School (online)” under the “Purchase” tab. You can pay directly with your MasterCard, VISA or American Express using our secure credit card processing. You may ask to be invoiced, however, Payments must be received by 5pm PDT, Thursday, July 30, 2026.

Complete refunds will be made for cancellations prior to July 24, 2026. No refunds will be made for cancellations after that date, however, substitutions are gladly accepted.

Schedule

Daily Schedule, Pacific Daylight Time (PDT)
06:45 – 07:00 Zoom available for login
07:00 – 08:00 Instruction
08:00 – 08:10 Coffee Break
08:10 – 09:10 Instruction
09:10 – 09:20 Coffee Break
09:20 – 10:20 Instruction
10:20 – 10:30 Wrap-up and questions

Course Outline

Machine Learning Summer School 2026 will cover the following topics:

  • Day 1:  Introduction to Machine Learning in Chemometrics and Unsupervised Learning
    • Introduction to Machine Learning: Definitions and Nomenclature
    • Unsupervised Learning Methods-Pattern Recognition
      • Principal Components Analysis (PCA)
      • t-Distributed Stochastic Neighbor Embedding (t-SNE)
      • Uniform Manifold Approximation and Projection (UMAP)
  • Day 2:  Supervised Learning in Chemometrics
    • Linear Algorithms for Regression
      • Multiple Linear Regression (MLR)
        • Regularized Linear Regression:  Ridge, Lasso, Elastic Nets
      • Principal Components Regression (PCR)
      • Partial Least Squares (PLS)
      • Using Diviner to search for optimal regression models
    • Linear Algorithms for Classification
      • Logistic Regression
      • Linear Discriminant Analysis (LDA)
      • Partial Least Squares Discriminant Analysis (PLS-DA)
    • Non-Linear Methods
      • Locally Weighted Regression (LWR)
      • Support Vector Machines (SVM)
      • Support Vector Machines for Classification (SVM-C)
  • Day 3:  Advanced Supervised Learning Methods
    • Brief Overview of XGBoost
    • Artificial Neural Networks (ANNs)
      • Neurons, Layers, activation functions
      • Forward propagation, backpropagation, gradient descent
      • Implementation using ANN/BPN
    • Deep Learning ANNs
      • Difference between deep learning and traditional ML
      • Implementation using TensorFlow and scikit-learn
    • Model Interpretation and Robustness Testing for Non-Linear Models
      • Variable Importance:  SHAP (Shapley Additive Explanations)
      • Model Sensitivity Tests
      • Robustness Tests
    • Summary and Recap

Schedule for the Week
Tuesday —  Introduction to Machine Learning in Chemometrics and Unsupervised Learning
Wednesday — Supervised Learning
Thursday — Supervised Learning (cont.) and Interpretatbility; Summary and Recap