Workshop: Statistical Modeling & Machine Learning

Participants acquire in-depth knowledge of modern data modeling, are able to integrate ML methods into their work in a meaningful way and support data-driven decisions for process improvements.

Core content of this module:

➡ Multivariate methods and classical regression models (refresher) to refresh statistical basics.
➡ Introduction to data orchestration and machine learning – understanding supervised and unsupervised learning.
➡ Model setup with KNIME®, Minitab® and Python, including data preparation, feature engineering and validation.
➡ Classification and regression methods such as decision trees, Naive Bayes or neural networks.
➡ Differences between classical and ML-based models, including fields of application and limitations.
➡ Practical application of ML methods for process optimization, including visualization and interpretation.
➡ Confident handling of model quality, cross-validation and model comparison for well-founded decisions.