Introduction to Explainable Machine Learning (xML)5 ECTS (englische Bezeichnung: Introduction to Explainable Machine Learning)
(Prüfungsordnungsmodul: Introduction to Explainable Machine Learning)
Modulverantwortliche/r: Thomas Seel, Simon Bachhuber, Ive Weygers Lehrende:
Thomas Seel
Startsemester: |
SS 2022 | Dauer: |
1 Semester | Turnus: |
jährlich (SS) |
Präsenzzeit: |
60 Std. | Eigenstudium: |
90 Std. | Sprache: |
Englisch |
Lehrveranstaltungen:
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Introduction to Explainable Machine Learning
(Vorlesung mit Übung, 4 SWS, Thomas Seel et al., Di, 14:15 - 15:45, 0.151-115; Mi, 10:15 - 11:45, K2-119)
Empfohlene Voraussetzungen:
Participants should be familiar with fundamental methods and concepts
in machine learning. They should, for example, have completed one of
the following courses
Inhalt:
This course gives an introduction to explainable and interpretable
methods and approaches in machine learning. We discuss prominent
concepts in explainable machine learning, analyze and compare their
potential and shortcomings, and apply them to example problems. The
covered topics include but are not limited to:
the role of explanations in machine learning (ML)
definitions and terminology in explainable ML
inherent versus post-hoc explainability
prototypes in classification
heat maps and saliency-based approaches
global post-hoc explanations via surrogate models
additive feature attribution methods
local interpretable model-agnostic explanations
explanations via Shapley values
advanced methods from recent literature
plausability, faithfulness, comprehensibility and consistency of
explanations
The example problems to which we will apply the concepts and methods
will stem from application domains in which explainability is considered
crucial, such as digital health.
Lernziele und Kompetenzen:
- Fachkompetenz
- Wissen
- Participants will be familiar with several machine learning concepts and methods that yield explainable results. They will know which properties explanations should ideally have and in which ways they can be assessed.
- Verstehen
- Participants will understand the relevance and usefulness of different levels and types of explainability in machine learning.
- Anwenden
- Participants will be familiar with the employment of several methods that yield explainable results, and they will be able to apply them to example problems.
- Lern- bzw. Methodenkompetenz
- Participants analyze and discuss scientific publications in the context of a given broader topic. Participants deepen and challenge their understanding of the taught concepts by designing and answering short quizzes.
- Sozialkompetenz
- Participants successfully collaborate in small teams, they effectively exchange arguments and self-organize to produce a joint result within a given time frame.
Literatur:
- C. Molnar. “Interpretable Machine Learning – A Guide for Making Black Box Models Explainable” https://christophm.github.io/interpretable-ml-book/
A. Thampi. “Interpretable AI – Building explainable machine learning systems”, Manning, https://www.manning.com/books/interpretable-ai
Samek, W., Montavon, G., Vedaldi, A., Hansen, L.K., Müller, K.-R. (Editors). “Explainable AI: Interpreting, Explaining and Visualizing Deep Learning”, Springer, 2019.
HJ Escalante, S. Escalera, I. Guyon, X. Baró, Y. Güçlütürk, U. Güçlü, M. van Gerven (Editors) . “Explainable and Interpretable Models in Computer Vision and Machine Learning”, Springer, 2018.
Biran, Or, and Courtenay Cotton. "Explanation and justification in machine learning: A survey." In IJCAI-17 Workshop on ExplainableAI (XAI), p. 8. 2017, http://www.cs.columbia.edu/~orb/papers/xai_survey_paper_2017.pdf.
Doshi-Velez, Finale, and Been Kim. "Towards a rigorous science of interpretable machine learning." arXiv preprint, 2017, https://arxiv.org/abs/1702.08608.
R Guidotti, A Monreale, F Turini, D Pedreschi, F Giannotti. "A survey of methods for explaining black box models." arXiv preprint, 2018, https://arxiv.org/abs/1802.01933.
Organisatorisches:
StudOn-Kurs: https://www.studon.fau.de/crs4419539.html
Verwendbarkeit des Moduls / Einpassung in den Musterstudienplan:
- Data Science (Bachelor of Science)
(Po-Vers. 2020w | Vertiefungsrichtungen | Maschinelles Lernen / Artificial Intelligence (AI) | Introduction to Explainable Machine Learning)
- Data Science (Bachelor of Science)
(Po-Vers. 2020w | Vertiefungsrichtungen | Nicht gewählte Vertiefungsrichtungen | Introduction to Explainable Machine Learning)
Dieses Modul ist daneben auch in den Studienfächern "Artificial Intelligence (Master of Science)", "Data Science (Master of Science)", "Informatik (Bachelor of Science)", "Informatik (Master of Science)", "Medizintechnik (Master of Science)" verwendbar.
Studien-/Prüfungsleistungen:
Introduction to Explainable Machine Learning (Prüfungsnummer: 76981)
zugeh. "mein campus"-Prüfung: | - 76981 Introduction to Explainable Machine Learning (Gewichtung: 100.0 %, Prüfung, Form: variabel, Drittelnoten (mit 4,3), Dauer: -, 5 ECTS, Prüfung).
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- Prüfungsleistung, Klausur mit MultipleChoice, Dauer (in Minuten): 60, benotet, 5 ECTS
- Anteil an der Berechnung der Modulnote: 100.0 %
- weitere Erläuterungen:
Answering the questions requires understanding of the concepts taught throughout the course and the ability to apply these concepts to specific example problems. The exam contains multiple-choice questions. It counts 100% of the course grade. By submitting small optional homework assignments, up to 20% of bonus points can be obtained, which will be added to the result of the exam.
- Erstablegung: SS 2022, 1. Wdh.: WS 2022/2023
1. Prüfer: | Thomas Seel (100950) |
- Termin: 05.08.2022
Termin: 05.08.2022
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