Design of Experiments (DOE) and Machine Learning
November 13th
Participation in the Short Course is included in all full-conference registration fees. Attendees will be asked to RSVP their interest during registration. Single day admission for the Short Course only is available for purchase during Advance and Standard Registration periods.
Learning Objectives:
This is an introductory course on Design of Experiments (DOE) and Machine Learning and its integration for undergraduate and graduate students in the major of mechanical engineering. In this course, students are expected to expand upon their fundamental knowledge of DOE, Statistics and its application, and machine learning concepts to develop an understanding of the principal concepts and methods of its application, and the integration of the two concepts.
This course is divided into two (2) parts that provides a background in Design of Experiments with Machine Learning analysis.
- Design of Experiments (DOE)
- Machine Learning (ML)
The emphasis for each subsection is geared directly to engineering applications and data processing. This course is tied to an assigned analysis project which enforces the application of the fundamentals. Practicing engineers and graduate students will find it an indispensable source for interdisciplinary learning of DOE with Machine Learning.
List of Discussion/Lecture Topics
- Introductions
- Fundamental concepts of Design of Experiments
- Fundamental concepts of Machine Learning/A.I.
- Data Exploration, Aggregation, and Visualization
- Basic Statistical Concepts (Statistics, T and F Distributions/ Testing, Single and Two Sample Inference)
- Supervised and Unsupervised Machine Learning/A.I.
- ANOVA
- Simple Linear Regression and Multiple Regression
- Single-Factor Experiments (Designing Engineering Experiments
- Design of Experiments with Several Factors (Full Factorial and Fractional Factorial Experiments, Surface Response Experiments
- Examples of Integration of DOE and Machine Learning/A.I. Concepts
Course Instructor
Ed Marotta
Director Data Science – ChampionX
Adjunct Professor – University of Houston