Kies de Nederlandse taal
Course module: 822182-B-6
Methodology for CSAI
Course info
Course module822182-B-6
Credits (ECTS)6
CategoryBA (Bachelor)
Course typeCourse
Language of instructionEnglish
Offered byTilburg University; Tilburg School of Humanities and Digital Sciences; TSH: Department Cognitive Science and AI; TSH: Department Cognitive Science and AI;
Is part of
B Communication and Information Sciences
B Communication and Information Sciences (Spc.Cog.Sc & AI)
B Cognitive Science and Artificial Intelligence
A. Abrahamian
Other course modules lecturer
dr. R.J.C.M. Starmans
Other course modules lecturer
Academic year2020
Starting block
SM 1
Course mode
RemarksCaution: this information is subject to change
Registration openfrom 20/08/2020 up to and including 20/08/2021

After successful completion of the course the students:

·         can describe and explain the main paradigms, concepts, techniques, and problems in Methodology.

·         can apply these concepts and techniques to CS & AI as a research area.

·         can assess and evaluate the relevance of these concepts and techniques for CS & AI as a research area.

·         can identify and explain the several stages in the research process.

·         can identify and assess the main threads to validity and reliability, particularly in research designs and data sets.

·         can analyze and compute performance measures and interpret graphs, tables and diagrams associated with it.

·         can identify and explain the differences between statistical learning and machine learning from a methodological point of view.

·         can evaluate the ethical aspects related to research in general and responsible and explainable AI in particular.


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.

Course available for exchange students
Conditions of admission apply
Contact person
dr. R.J.C.M. Starmans
Timetable information
Methodology for CSAI
Written test opportunities
Schriftelijk (70%) / Written (70%)EXAM_01SM 1222-01-2021
Written test opportunities (HIST)
Midterm (30%) / Midterm (30%)MIDTERM_01SM 1116-10-2020
Schriftelijk (70%) / Written (70%)EXAM_01SM 1111-12-2020
Midterm (30%) / Midterm (30%)MIDTERM_01SM 1215-01-2021
Required materials
Recommended materials
• The course will use substantial parts of Paul R. Cohen, Empirical Methods for Artificial Intelligence. (1995), MIT Press. • Throughout the course we will introduce papers, book chapters, and other documents, which will be classified as optional, recommended, or mandatory reading. All documents with the label mandatory reading, all slides of the lecturers/videos will be part of the exams.
Written (70%)

Midterm (30%)

Final Grade

Kies de Nederlandse taal