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Machine Learning Summer School

August 8, 2023 - August 10, 2023

Eigenvector Research, Inc. is pleased to offer Machine Learning Summer School, a live webinar-based short course covering 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.

Target Audience

Machine Learning Summer School is aimed at engineers, chemists and other scientists who want to be able to use pattern recognition methods to analyze complex data and develop their own non-linear data models for calibration/regression or sample classification.

The course serves individuals with a need for exploratory data analysis, development of predictive models such as 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). Courses in these topics can be found on the EigenU Recorded Courses page.

Course Description

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 focus on machine learning methods designed to deal with non-linear data. Emphasis will be on applying these techniques in the chemical process and laboratory environment for pattern recognition, instrument calibration, sample classification and exploratory data analysis.

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.

About the Instructors

The courses will be led Eigenvector Senior Software Developer Donal O’Sullivan and Associate Data Scientist & Software Developer Sean Roginski. They will be assisted by Eigenvector President and PLS_Toolbox creator Barry M. Wise, Technology Principal Bob Roginski, and Principal Consultant and Data Scientist Manny Palacios. Eigenvector Research has delivered over 200 chemometrics 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) and a certificate of completion.

Prices shown are shown below. Payment must be received by 5pm PDT, Friday, August 4, 2023.

Machine Learning Summer School

Note: Payment must be received by 5pm PDT, Friday, August 4, 2023. 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 the class you would like to attend 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, Friday, August 4, 2023.

Complete refunds will be made for cancellations prior to July 28, 2023. No refunds will be made for cancellations after that date, however, substitutions are gladly accepted.


Daily Schedule, Pacific Daylight Time (PDT)
06:45 – 07:00 Webex 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 Outlines

Machine Learning Summer School will cover the follow topics:

  • Classification and Regression Methods
    • Linear and Non-linear Data
    • Brief review of Linear Methods
    • Data Transformations
    • Hierarchical Modeling (HMB and HMAC)
    • Locally Weighted Regression (LWR)
    • Soft Independent Modeling of Class Analogy (SIMCA)
    • K-Nearest Neighbors (KNN)
    • Support Vector Machine Discriminant Analysis (SVM, SVMDA)
    • Artificial Neural Networks Discriminant Analysis (ANN, ANNDA)
    • Boosted Regression Trees Discriminant Analysis (XGB, XGBoostDA)
    • Hyperspectral Image Classification
    • Conclusion/ Choosing Which Method
  • Data Compression for Visualization and Preprocessing
    • Brief review of Principal Components Analysis (PCA)
    • Sammon Mapping
    • t-distributed Stochastic Neighbor Embedding (t-SNE)
    • Uniform Manifold Approximation and Projection (UMAP)
    • Conclusion/ Choosing Which Method
  • Conclusions

Schedule for the Week
Tuesday — Classification and Regression Methods
Wednesday — Classification and Regression Methods
Thursday — Compression and Visualization Methods