Explainable Machine Learning 1000-1M18WUM
Lecture:
Understanding of the model:
- Measures of identifying important variables (based on permutations, based on loss functions),
- model quality testing measures (for regression and classification models),
- measurements of the boundary model response (partial model response, conditional model response, individual model responses).
Understanding of predictions:
- local approximations and the LIME approach,
- attribution of the importance of features based on breakDown and Shapley values method.
Laboratory:
Performing predictive analysis for a specific phenomenon.
Application of methods of explaining for a given phenomenon.
Project:
Implementation of a new library or validation of the chosen algorithm of understanding black box models.
Type of course
Learning outcomes
KNOWLEDGE
W01 Knows basic methods of data pre-treatment, including data size reduction and feature extraction.
W02 Knows basic methods of XAI and their use in business data analysis
SKILLS
U01 Knows the basic methods of studying the structure of ML models and their use in business data analysis.
U02 is able to build a classifier and assess the significance of individual variables for the final result.
SOCIAL COMPETENCES
K01 Able to work in a project group taking on different roles in it
Assessment criteria
The evaluation will consist of three components
activity during classes (20%),
housework (20%)
project (60%)
You need at least 50% of the points to pass.
Bibliography
1. Examples and documentation for Descriptive mAchine Learning EXplana-tions. Biecek 2018. https://pbiecek.github.io/DALEX_docs
2. Ribeiro, Marco Tulio, Sameer Singh, and Carlos Guestrin. 2016. “‘Why Should I Trust You?’: Explaining the Predictions of Any Classifier.” In, 1135–44. ACM Press. https://doi.org/10.1145/2939672.2939778.
3. Fisher, Aaron, Cynthia Rudin, and Francesca Dominici. 2018. “Model Class Reliance: Variable Importance Measures for Any Machine Learning Model Class, from the ’Rashomon’ Perspective.” Journal of Computational and Graphical Statistics. http://arxiv.org/abs/1801.01489.
Additional information
Information on level of this course, year of study and semester when the course unit is delivered, types and amount of class hours - can be found in course structure diagrams of apropriate study programmes. This course is related to the following study programmes:
- Bachelor's degree, first cycle programme, Computer Science
- Master's degree, second cycle programme, Computer Science
- Master's degree, second cycle programme, Mathematics
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: