The core topics will be:
· Introduction to methodology for CSAI: methodology between epistemology and science. Four paradigms for scientific research. CS & AI as a research area.
· The Research Process, cycle, or roadmap and its several stages.
· Explorative Data Analysis and Visualization
· Validity and Reliability in Research. Many faces of Validity and Reliability, including Cross validation
· Performance assessment and the Confusion matrix, including Bayes’ Theorem to compute performance measures
· Causality: confounding, moderation, and mediation, including Simpsons paradox and Robinsons paradox.
· Research designs and related threads to validity and reliability.
· Statistical Inference / learning 1 Tactics for Generalisation (external validity issues)
· Statistical Inference / learning 2 Causality (internal validity issues)
· Machine learning or how do new ML-algorithms affect research methodology and practice? Including such issues as the train and test paradigm, cross-validation and bootstrap, bias variance trade-off and overfitting.
· Responsible and explainable AI, including Ethics in empirical research
· The Crisis of Science, including p-values wars, false positive rates, reproducibility crisis, “The End of Theory”, some classical fallacies.