Master Lab Course - Explainable AI for Machine Learning (IN2106, IN4286)
Course 0000003487 in WS 2020/1
General Data
| Course Type | practical training |
|---|---|
| Semester Weekly Hours | 5 SWS |
| Organisational Unit | Informatics 2 - Chair of Formal Languages, Compiler Construction, Software Construction (Prof. Seidl) |
| Lecturers |
Edoardo Mosca Responsible/Coordination: Georg Groh |
| Dates |
Assignment to Modules
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IN2106: Master-Praktikum / Advanced Practical Course
This module is included in the following catalogs:- Further Modules from Other Disciplines
Further Information
Courses are together with exams the building blocks for modules. Please keep in mind that information on the contents, learning outcomes and, especially examination conditions are given on the module level only – see section "Assignment to Modules" above.
| additional remarks | A considerable amount of research and applications leverages machine learning techniques to solve several real-life tasks. While learning directly from the data is a major key for their success in terms of automation and performance, methods are progressively becoming more complex and opaque to humans. Most famously, Deep Neural Networks have gained popularity due to their success in practice but behave like black-boxes. In this course, we explore solutions coming from the recent research field of eXplainable Artificial Intelligence (XAI). In particular, we focus on the practical development of interpretable models and the application of explainability techniques to understand black-box frameworks. Therefore, we provide a range of related research literature based on which students develop concrete projects towards making machine learning techniques and predictions understandable for humans. We potentially encourage the participants to use cutting-edge machine learning models such as neural networks and engage themselves in several fields of applications such as Natural Language Processing, Computer Vision, and Machine Learning on Graphs. |
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| Links |
Additional information TUMonline entry |
Equivalent Courses (e. g. in other semesters)
| Semester | Title | Lecturers | Dates |
|---|---|---|---|
| WS 2022/3 | Master Lab Course - Explainable AI for Machine Learning (IN2106, IN4286) |
Eder, T.
Mosca, E.
Responsible/Coordination: Groh, G. |
Wed, 10:00–12:00, MI 01.07.014 and singular or moved dates |
| WS 2021/2 | Master Lab Course - Explainable AI for Machine Learning (IN2106, IN4286) |
Eder, T.
Mosca, E.
Responsible/Coordination: Groh, G. |
Mon, 09:00–11:00, MI 01.09.014 Mon, 10:00–12:00, MI 02.07.014 |