Explainable machine learning
General data
Course ID: | 1000-319bEML |
Erasmus code / ISCED: |
11.3
|
Course title: | Explainable machine learning |
Name in Polish: | Wyjaśnialne uczenie maszynowe |
Organizational unit: | Faculty of Mathematics, Informatics, and Mechanics |
Course groups: |
Elective courses for Computer Science and Machine Learning Obligatory courses for 2nd year Machine Learning |
ECTS credit allocation (and other scores): |
6.00
|
Language: | English |
Type of course: | elective monographs |
Requirements: | Deep neural networks 1000-317bDNN |
Short description: |
The goal of the course is to learn about concepts, methods and techniques for explaining complex machine learning models. Predictive models are becoming increasingly complex, tree ensembles, deep neural networks are models with thousands of parameters. For models with such dimensionality, it is easy to lose track of what the model has learned. During this course we will discuss tools for analysing the structure of a model treated as a black box, and analysing the predictions from the model. This will allow us to increase confidence in the model, improve model performance, and be able to extract useful knowledge from the model. We will learn about the most popular explanatory methods, discuss their strengths and weaknesses so that the class participant has the necessary competences to further explore the literature in this area. |
Full description: |
- 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 |
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 |
Assessment methods and 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 |
Classes in period "Winter semester 2024/25" (past)
Time span: | 2024-10-01 - 2025-01-26 |
Go to timetable
MO TU W TH FR LAB
CW
CW
LAB
LAB
CW
CW
CW
WYK
CW
|
Type of class: |
Classes, 30 hours
Lab, 15 hours
Lecture, 15 hours
|
|
Coordinators: | Przemysław Biecek | |
Group instructors: | Hubert Baniecki, Przemysław Biecek, Bartłomiej Sobieski | |
Course homepage: | https://github.com/mim-uw/eXplainableMachineLearning-2025/ | |
Students list: | (inaccessible to you) | |
Credit: | Examination | |
Main fields of studies for MISMaP: | computer science |
Classes in period "Winter semester 2025/26" (future)
Time span: | 2025-10-01 - 2026-01-25 |
Go to timetable
MO TU W TH FR CW
LAB
CW
LAB
LAB
CW
CW
CW
WYK
CW
|
Type of class: |
Classes, 30 hours
Lab, 15 hours
Lecture, 15 hours
|
|
Coordinators: | Przemysław Biecek | |
Group instructors: | Hubert Baniecki, Przemysław Biecek, Bartłomiej Sobieski | |
Course homepage: | https://github.com/mim-uw/eXplainableMachineLearning-2025/ | |
Students list: | (inaccessible to you) | |
Credit: |
Course -
Examination
Lecture - Examination |
Classes in period "Summer semester 2025/26" (future)
Time span: | 2026-02-16 - 2026-06-07 |
Go to timetable
MO TU W TH FR |
Type of class: |
Classes, 30 hours
Lab, 15 hours
Lecture, 15 hours
|
|
Coordinators: | Przemysław Biecek | |
Group instructors: | Hubert Baniecki, Przemysław Biecek, Bartłomiej Sobieski | |
Course homepage: | https://github.com/mim-uw/eXplainableMachineLearning-2025/ | |
Students list: | (inaccessible to you) | |
Credit: |
Course -
Examination
Lecture - Examination |
Copyright by University of Warsaw.