Explainable machine learning 1000-319bEML
- Introduction to explainable artificial intelligence, interpretable machine learning and fairness
- Methods for conditional analysis of predictive models: Break-Down method, Break-Down with interactions, SHAP, ASV
- Methods for model analysis by perturbation: LIME method, LORE
- Methods for contenst model analysis and model sensitivity testing: Ceteris Paribus, Partial Dependence, Accumulated Local Methods
- Method for assessing the importance of variables: Variable Importance by Pertmutations, Model Class Reliance
- Fairness and Biases
- Explanations specific to neural networks
Type of course
Requirements
Course coordinators
Main fields of studies for MISMaP
Assessment criteria
The final grade is based on activity in four areas:
mandatory: Project (0-36)
mandatory: Exam (0-30)
optional: Homeworks (0-24)
optional: Presentation (0-10)
In total you can get from 0 to 100 points. 51 points are needed to pass this course.
Grades:
51-60: (3) dst
61-70: (3.5) dst+
71-80: (4) db
81-90: (4.5) db+
91-100: (5) bdb
Bibliography
Explanatory Model Analysis. Explore, Explain and Examine Predictive Models by Przemysław Biecek, Tomasz Burzykowski
Fairness and Machine Learning: Limitations and Opportunities by Solon Barocas, Moritz Hardt, Arvind Narayanan
Interpretable Machine Learning. A Guide for Making Black Box Models Explainable by Christoph Molnar
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
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: