ML for Innovation Research: PhD/DBA Seminar
Prof. J. Osterrieder — 2 × 3 hours hands-on
Session 1: Unsupervised ML
01
The Map
The map — ML paradigms, workflow, dataset
ML paradigmsworkflowfeatures
04
Where Branches Cross
Crossing branches — cross-tabulation, synthesis
cross-tabmulti-method
Session 2: Supervised ML & GenAI
05
The Prediction Branch
Entering prediction — recap, supervised learning
supervisedfeature importance
06
The Prediction Branch
Testing theory — Random Forest, Logistic, ROC/AUC
Random ForestLogRegROC
07
Beyond the Tree
Extending the map — LLMs, prompts, structured output
LLMspromptsstructured output
08
The Complete Map
Complete map — decision framework, thesis patterns
decision treethesisresources
ML for Innovation Research: PhD/DBA Seminar — Prof. J. Osterrieder