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    • Stephan Günnemann
    • Sirine Ayadi
    • Tim Beyer
    • Jonas Dornbusch
    • Eike Eberhard
    • Dominik Fuchsgruber
    • Nicholas Gao
    • Lukas Gosch
    • Filippo Guerranti
    • Leon Hetzel
    • Chengzhi Martin Hu
    • Niklas Kemper
    • Amine Ketata
    • Marcel Kollovieh
    • Arthur Kosmala
    • Aleksei Kuvshinov
    • Richard Leibrandt
    • Marten Lienen
    • David Lüdke
    • Aman Saxena
    • Sebastian Schmidt
    • Yan Scholten
    • Jan Schuchardt
    • Leo Schwinn
    • Johanna Sommer
    • Tim Tomov
    • Tom Wollschläger
    • Alumni
      • Simon Geisler
      • Anna-Kathrin Kopetzki
      • Amir Akbarnejad
      • Roberto Alonso
      • Bertrand Charpentier
      • Marin Bilos
      • Aleksandar Bojchevski
      • Johannes Klicpera
      • Maria Kaiser
      • Richard Kurle
      • Hao Lin
      • John Rachwan
      • Oleksandr Shchur
      • Armin Moin
      • Daniel Zügner
  • Teaching
    • Sommersemester 2026
      • Machine Learning for Graphs and Sequential Data
      • Advanced Machine Learning: Deep Generative Models
      • Applied Machine Learning
    • Wintersemester 2025/26
      • Machine Learning
      • Robust Machine Learning
      • Seminar: Current Topics in Machine Learning
      • Seminar: Selected Topics in Machine Learning Research
    • Sommersemester 2025
      • Advanced Machine Learning: Deep Generative Models
      • Applied Machine Learning
      • Seminar: Selected Topics in Machine Learning Research
      • Seminar: Current Topics in Machine Learning
    • Wintersemester 2024/25
      • Machine Learning
      • Seminar: Selected Topics in Machine Learning Research
      • Seminar: Current Topics in Machine Learning
    • Sommersemester 2024
      • Machine Learning for Graphs and Sequential Data
      • Advanced Machine Learning: Deep Generative Models
      • Applied Machine Learning
      • Seminar: Selected Topics in Machine Learning Research
    • Wintersemester 2023/24
      • Machine Learning
      • Applied Machine Learning
      • Seminar: Selected Topics in Machine Learning Research
      • Seminar: Machine Learning for Sequential Decision Making
    • Sommersemester 2023
      • Machine Learning for Graphs and Sequential Data
      • Advanced Machine Learning: Deep Generative Models
      • Large-Scale Machine Learning
      • Seminar
    • Wintersemester 2022/23
      • Machine Learning
      • Large-Scale Machine Learning
      • Seminar
    • Summer Term 2022
      • Machine Learning for Graphs and Sequential Data
      • Large-Scale Machine Learning
      • Seminar (Selected Topics)
      • Seminar (Time Series)
    • Winter Term 2021/22
      • Machine Learning
      • Large-Scale Machine Learning
      • Seminar
    • Summer Term 2021
      • Machine Learning for Graphs and Sequential Data
      • Large-Scale Machine Learning
      • Seminar
    • Winter Term 2020/21
      • Machine Learning
      • Large-Scale Machine Learning
      • Seminar
    • Summer Term 2020
      • Machine Learning for Graphs and Sequential Data
      • Large-Scale Machine Learning
      • Seminar
    • Winter Term 2019/2020
      • Machine Learning
      • Large-Scale Machine Learning
    • Summer Term 2019
      • Mining Massive Datasets
      • Large-Scale Machine Learning
      • Oberseminar
    • Winter Term 2018/2019
      • Machine Learning
      • Large-Scale Machine Learning
      • Oberseminar
    • Summer Term 2018
      • Mining Massive Datasets
      • Large-Scale Machine Learning
      • Oberseminar
    • Winter Term 2017/2018
      • Machine Learning
      • Oberseminar
    • Summer Term 2017
      • Robust Data Mining Techniques
      • Efficient Inference and Large-Scale Machine Learning
      • Oberseminar
    • Winter Term 2016/2017
      • Mining Massive Datasets
    • Sommersemester 2016
      • Large-Scale Graph Analytics and Machine Learning
    • Wintersemester 2015/16
      • Mining Massive Datasets
    • Sommersemester 2015
      • Data Science in the Era of Big Data
    • Machine Learning Lab
  • Research
    • Robust Machine Learning
    • Machine Learning for Graphs/Networks
    • Machine Learning for Temporal and Dynamical Data
    • Bayesian (Deep) Learning / Uncertainty
    • Efficient ML
    • Code
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  2. Barrierefreiheit

Accessibility Statement

The Central IT Department of the Technical University of Munich is committed to making its websites — including their mobile versions — accessible in accordance with the Bavarian E-Government Regulation (BayEGovV).

This accessibility statement applies to the websites implemented using the TYPO3 content management system and listed at typo3.tum.de/en/t3/our-webpool/.

Compliance status

These websites are partially compliant with Bayerische Verordnung über die elektronische Verwaltung und die barrierefreie Informationstechnik (BayEGovV). The non-compliances and the exemptions are listed below.

Non-accessible content

  • Alt attributes are sometimes missing for images, tables, control elements and layout graphics.
  • Information in plain language is missing.
  • There may be errors in the hierarchy of headings, or links may be incomprehensible.
  • Not all PDF documents are accessible.
  • Full audio descriptions, full text alternatives or subtitles are not available for all embedded videos.
  • Some content and functionality is lost when text is enlarged.
  • Additional content inserted using pointer or keyboard focus may not always remain operable.
  • Words and paraghraphs in other languages are not highlighted as such.

Reasons for inaccessible content

As a university, we maintain a very extensive and diverse range of websites; therefore, errors cannot be completely avoided. The majority of these websites were published before September 23, 2018, and therefore do not fully comply with current accessibility standards. Various types of content — such as office file formats (PDF documents) created before September 23, 2018 — have not yet been converted. In some cases (particularly older versions of user information, annual reports, or bulletins that exist only in print), digitization and subsequent conversion to an accessible format are currently not possible. The transition to accessible websites is already underway. Due to the size of the video archive, providing audio transcriptions for all videos has not yet been completed.

Preparation of this accessibility statement

The statement was last updated on January 1, 2026.
The assessment is based on a self-evaluation carried out with the support of third parties.

Feedback and contact information

Please inform us of any non-compliance with accessibility standards. You can contact us at:

Data Analytics and Machine Learning  Group
Boltzmannstr. 3
85748 Garching
stefanie.dietrich@tum.de
+49 89 289 17256

Enforcement procedure

As part of an enforcement procedure, you may submit an online request to the competent enforcement body to review compliance with accessibility requirements.

Contact details of the enforcement body

Landesamt für Digitalisierung, Breitband und Vermessung
IT-Dienstleistungszentrum des Freistaats Bayern
Durchsetzungs- und Überwachungsstelle für barrierefreie Informationstechnik
St.-Martin-Straße 47
81541 Munich

E-Mail: bitv@bayern.de
Homepage: www.ldbv.bayern.de/digitalisierung/bitv.html

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Informatik 26 - Data Analytics and Machine Learning


Prof. Dr. Stephan Günnemann

Technische Universität München
TUM School of Computation, Information and Technology
Department of Computer Science
Boltzmannstr. 3
85748 Garching 

Sekretariat:
Raum 00.11.057
Tel.: +49 89 289-17256
Fax: +49 89 289-17257

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