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Machine Learning for Calibration and Classification

May 13, 2020

May 13, 2020 - May 14, 2020

The recorded version of this course is now available in our EigenU Recorded Courses area.

Eigenvector Research, Inc. is pleased to announce Machine Learning for Calibration and Classification, a live webinar-based short course covering modern non-linear modeling methods including Artificial Neural Networks (ANNs), Support Vector Machines (SVMs) and Gradient Boosted Ensemble Methods (XGBoost).

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

Target Audience

Machine Learning for Calibration and Classification is aimed at engineers, chemists and other scientists who develop predictive calibration or classification models with complex non-linear multivariate data. It is applicable to a wide array of areas including

  • Process Analysis, e.g. in the pharmaceutical, food and beverage, and process industries.
  • Analytical Chemistry, e.g. QC labs, forensic investigations, etc.
  • Medical Devices, Clinical Analysis
  • Sensory and Consumer Science
  • Any field that produces chemical/analytical data

The course serves individuals who need to develop predictive models such as analytical instrument calibrations, soft sensor models, and sample classification systems. No prior knowledge is required for this course, although some knowledge of elementary linear algebra and basic chemometrics methods (PCA, PLS), is useful.

Course Description

This course will be delivered via WebEx webinar in two segments of three and a half hours each. The course material is based on our popular Eigenvector University Machine Learning for Chemometricians course. Please refer to it for a complete description and course outline.

The course will include several 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 course will be led by Eigenvector Senior Software Developer Donal O’Sullivan, Ph.D.. Dr. O’Sullivan has been the lead developer on integrating modern machine learning methods into PLS_Toolbox and Solo. He will be assisted by Eigenvector Senior Data Scientist Manuel A. Palacios, Ph.D. and Eigenvector President and PLS_Toolbox creator Barry M. Wise. Dr. Wise and the Eigenvector staff have delivered over 200 chemometrics courses at scientific conferences, on-site for companies and at our popular Eigenvector University each year in Seattle.

Course Fee

Eigenvector Research is pleased to offer this course at a special reduced “COVID-19 Lockdown” price. 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, Tuesday, May 12, 2020.

Machine Learning for Calibration and Classification

Note: Payment must be received by 5pm PDT, Tuesday, May 12, 2020. 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. 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, Tuesday, May 12, 2020.

Complete refunds will be made for cancellations prior to May 11, 2020. 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