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Mixture Experiments Using Machine Learning: “A How-To Approach”

Mixture Experiments Using Machine Learning:

A “How-to” Approach

Marie Gaudard

Marie Gaudard, Course Author

Senior Data Scientist and Statistical Consultant at Predictum Inc.

Phil Ramsey

Philip Ramsey, Course Author

Senior Data Scientist and Statistical Consultant at Predictum Inc.

This is a shorter, method-centered version of “Design of Experiments for Mixtures using Machine Learning”.
By taking the 1-day “How-To” course, you will only learn the practical steps to perform mixture experiments using statistical software, suitable for those with little time for training or who need fast, practical help.
1 day (delivered as 2 half-day sessions)
On-premises or virtual
Available on request

About this Course

Traditional Mixture DOE methods are not up to the challenge of today’s complex mixture systems, in which the components of interest are mixed and their proportions are optimized.

Traditional methods overburden users, assume inadequate models, and require too many experimental runs.

This 1-day, method-centered version of “Design of Experiments for Mixtures with Machine Learning” presents a revolutionary approach to the design of experiments for mixtures. It is based on modern machine learning methods that reduce sample size requirements, streamline the process of analyzing and modeling, improve accuracy, and provide deep insight for experiments of high complexity.

Upon completion, you will be able to use statistical software to perform mixture experiments and optimize mixture systems.

Applications pertain to a wide variety of industrial sciences. Examples of industries in which we have had success include:
  • Pharmaceutical and bio-engineering, conducting media or buffer optimization experiments for increased protein yields from bacteria or mammalian cells
  • Semi-conductors, modeling yield on wafers, identifying yield loss mechanics, and investigating new wafer substrates
  • Asphalt, improving asphalt mixtures for critical process attributes
  • Metals manufacturing, optimizing critical characteristics in metallurgy


  • About the course
  • Mixture Spaces, Components, and Blending

SVEM and Space Filling Designs

  • A Machine-Learning Approach to Experimentation
  • Case Study: Etch Rate (SVEM analysis)
  • Exercise: Construct a Space-Filling Design

SVEM Analysis

  • The SVEM Add-In
  • Exercise: Conduct a SVEM Analysis (Pesticide)

Multiple Responses

  • Case Study: Detergent

Bounds and Linear Constraints

  • Case Study: Flare

Mixture of Mixtures Experiments

  • Mixture of Mixtures Experiments
  • Case Study: Harvey Wallbanger
  • Exercise: Superfood Drinks

Mixture-Process Factor Experiments

  • Mixture-Process Factor Experiments
  • Case Study: Fly Ash


Marie Gaudard
Sr. Data Scientist

Marie Gaudard

Specialist in data science, predictive modeling, design of experiments, and machine learning.

Marie is a Senior Data Scientist at Predictum. She specializes in predictive modeling, design of experiments, statistics, and machine learning. She has extensive experience in consulting and statistical training in a wide variety of industries.
Marie is Professor Emerita of Statistics at the University of New Hampshire (UNH), where she has worked extensively with students and companies on the practical application of statistics. She is also a co-author of two books, one of which is about the use of JMP software and statistical methods to improve quality and the other is about the partial least squares technique. She was also a statistical writer for several years as a member of the JMP documentation team at SAS Institute.

Marie holds a Ph.D. in Statistics from the University of Massachusetts at Amherst.

Phil Ramsey
Sr. Data Scientist

Philip Ramsey

Specialist in data analytics, empirical modeling and optimization.

Phil is a Senior Data Scientist and Statistical Consultant at Predictum. He provides consulting services in data science, statistics, and machine learning for integrated analytical systems, custom projects, and training. He specializes in modern experimental design and analysis strategies and the use of statistics, data science, and machine learning in engineering and science.

In addition, Phil is a Professor in the Department of Mathematics and Statistics at the University of New Hampshire (UNH) where he teaches courses at the undergraduate and graduate levels in design of experiments, machine learning, and statistical methods for quality improvement. He has held the following relevant industrial positions: Senior Engineer for Materials and Processes Development, McDonnell Douglas, St. Louis, MO; Staff Scientist/Statistician, Alcoa Technical Center, Pittsburgh, PA; and Statistician/Senior Engineer, Rohm & Haas Electronic Materials (now Dow), Marlboro, MA.

Phil holds a Ph.D. in statistics from Virginia Polytechnic Institute and State University.

Who Should Attend

Engineers, scientists, and researchers who work directly with mixtures, mixture designs, and mixture processes in various industries.


2 half-day sessions
Additional “office hours” with instructors available

Delivery Methods

On-premises or virtual


No prior or formal training in design of experiments is assumed. Some familiarity with basic statistics is desirable.

JMP is required if you want to actively participate in the course. If you do not have JMP you can get a free trial version to install on your work or personal computer.

Available on request

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Mixture Experiments with Machine Learning: A “How-To” Approach

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