Machine Learning With a Human Touch

Learn about advanced experimental designs to account for constraints: time, resources, material heterogeneity, randomisation restrictions as well as repeated measures. The construction of advanced designs and their analysis is covered.

Once influential factors are identified, the next goal consists of optimising their settings. This module covers the construction of experimental designs for optimisation.  Data modelling is carried out with response surface methodology.

In preliminary research phases, the number of potentially influential factors to investigate is usually large. Screening designs are essential to identify the most influential factors with a reasonable number of runs in preliminary research phases.

Variation is present in every experiment. Learn about DOE techniques to control variation, and to maximise data quality. Commonly used experimental designs are discussed as well as the statistical data analysis tools.