Module MA4666-KP05
Interpretable Statistical Learning (IStLern)
Duration
1 Semester
Turnus of offer
every second year
Credit points
5
Course of studies, specific fields and terms:
- Master Medical Informatics 2019, optional subject, Medical Data Science / Artificial Intelligence
- Bachelor CLS 2023, optional subject, mathematics
- Bachelor CLS 2016, optional subject, mathematics
- Master CLS 2023, optional subject, mathematics
- Master CLS 2016, optional subject, mathematics
Classes and lectures:
- Interpretable Statistical Learning (exercise, 1 SWS)
- Interpretable Statistical Learning (lecture, 2 SWS)
Workload:
- 60 hours private studies and exercises
- 45 hours in-classroom work
- 30 hours programming
- 15 hours exam preparation
Contents of teaching:
- Definition: Interpretable statistical learning
- Interpretable models
- Global model-agnostic methods
- Partial Dependence Plots (PDP)
- Accumulated Local Effects (ALE)
- Variable importance measures
- Local model-agnostic methods
- Individual Conditional Expectation (ICE)
- Local Surrogates (LIME)
- Counterfactional Explanations
- Shapley Werte, SHAP
Qualification-goals/Competencies:
- Students can explain the central ideas of interpretable statistical learning.
- They know the difference between model-based and model-agnostic methods.
- The can explain the differences between different methods for model interpretation.
- They can choose suitable methods for a given applicational setting.
- They can implement and apply these methods in R.
Grading through:
- Viva Voce or test
Teacher:
- Institute of Medical Biometry and Statistics
- Dr. rer. hum. biol. Björn-Hergen Laabs
Literature:
- Molnar, C. : Interpretable Machine Learning: A Guide for Making Black Box Models Explainable Springer, New York 2022 (2nd ed.)
- Hastie, T., Tibshirani, R., Friedmann, J. : The Elements of Statistical Learning: Data Mining, Inference and Prediction Springer, New York 2009 (2nd ed.)
- Wu, X., Kumar, V. : The Top Ten Algorithms in Data Mining CRC Press, Boca Raton 2009
Language:
- English, except in case of only German-speaking participants
Notes:
Admission requirements for taking the module- None (the competences of the modules mentioned under 'Requires' are needed for this module, but are not a formal prerequisite)
Admission requirements for participation in module examination(s):
- None
Module Exam(s):
- MA4666-L1: Interpretable Statistical Learning, oral exam (20 min) or written exam (60 min), 100% of the module grade
Last Updated:
28.11.2025