Exzellenzcluster Veranstaltungen

Aufgrund der aktuellen Lage rund um die Corona-Pandemie sind alle unsere derzeit geplanten Präsenzveranstaltungen auf unbestimmte Zeit ausgesetzt - wir informieren zeitnah über mögliche Alternativen.


Alle zurückliegenden Cluster-Veranstaltungen finden Sie untenstehend im ARCHIV.

Cluster Kolloquium "Maschinelles Lernen" -- 1. Mittwoch im Monat -- PAUSIERT

Seminarreihe des Exzellenzclusters "Maschinelles Lernen"

Mittwoch, 14:00 - 15:00
, mit anschliessendem Get Together

Hörsaal, AI Research Building, Maria von Linden-Str. 6 (Erdgeschoss), 72076 Tübingen


Workshop "Philosophy of Science Meets Machine Learning", 17. - 19. Juni 2020 - VERSCHOBEN

Workshop "Philosophy of Science Meets Machine Learning"

Der für Juni 2020 geplante Workshop wird verschoben - neues Datum wird sobald wie möglich bekannt gegeben.

The workshop is organised by the ‘Ethics and Philosophy Lab’ of the Cluster of Excellence ‘Machine Learning: New Perspectives for Science’ at the University of Tübingen.

Workshop Convenors: Thomas Grote, Thilo Hagendorff, Eric Raidl

Machine learning does not only transform businesses and the social sphere, it also fundamentally transforms science and scientific practice. The workshop focuses on that latter issue. It aims to discuss whether and how exactly recent developments in the field of machine learning potentially transform the process of scientific inquiry. For this, it sets out to analyse the field of machine learning through the lenses of philosophy of science, epistemology, research ethics and cognate fields such as sociology of science. The workshop will bring together philosophers from different backgrounds (from formal epistemology to the study of the social dimensions of science) and machine learning researchers. The workshop`s central topics are:

  1. A critical reflection on key-concepts, such as ‘learning’, ‘inference’, ‘explanation’ or ‘understanding’.
  2. The implications of machine learning for the special sciences, e.g. cognitive science, social science or medicine.
  3. The ethics of machine learning-driven science, e.g. the moral responsibilities of researchers.
  4. Social aspects of machine learning-driven science, e.g. the impact of funding structures on research.

2. Mini-Konferenz "Machine Learning in Science", 21. - 22. Juli 2020

Exzellenzcluster Konferenz "Machine Learning in Science"

Wir informieren sobald wie möglich in welchem Format die Konferenz stattfinden kann.
Dienstag, 21. Juli 2020

     (ORT: Hörsaal MPI-IS (vormittags) & Hörsaal AI Research Building (nachmittags))

Mittwoch, 22. Juli 2020

    (ORT: Hörsaal MPI-IS)

Nach dem Erfolg der 1. "Machine Learning in Science" Konferenz im Jui 2019, wollen wir auch in diesem Jahr unsere Cluster-Konferenz in ähnlichem Format (Vorträge, Posterbeträge,...) im Juli abhalten.

Veröffentlichung der Agenda folgt demnächst.


Hier finden Sie alle vergangenen Cluster-Veranstaltungen im Überblick.

Cluster Kolloquium "Maschinelles Lernen" - 1. Mittwoch im Monat

Seminarreihe des Exzellenzclusters "Maschinelles Lernen"

Mittwoch, 14:00 - 15:00
, mit anschliessendem Get Together

Hörsaal, AI Research Building, Maria von Linden-Str. 6 (Erdgeschoss), 72076 Tübingen


Vortrag Reinhard Diestel - 24. Januar 2020

Tangles: from graph minors to identifying political mindsets

Vortrag von Reinhard Diestel, Universität Hamburg, Fachbereich Mathematik

WANN: Freitag, 24.01.2020 um 10:00
WO: Hörsaal am MPI for Intelligente Systeme, Erdgeschoss


Traditional clustering identifies groups of objects that share certain qualities. Tangles do the converse: they identify groups of qualities that often occur together. They can thereby discover, relate, and structure types of phenomena: of behaviour, political views, texts, or bacteria. Tangles can identify key phenomena that allow predictions of others. Tangles also offer a new paradigm for clustering in large data sets. Tangle clusters are, by necessity, fuzzy: they tell us where in a large structure a cluster lies, which key properties of data points identify it, and how the overall data set is structured with respect to these clusters. But they do this without needing, or attempting, to assign individual points to any cluster. Tangles of graphs are central to the theory of graph minors developed by Robertson and Seymour for their celebrated proof of the graph minor theorem. For many years, however, algorithmic applications of graph minor theory were largely confined to applications of tree-decompositions, an overall structure dual to the existence of large tangles. Very recently, tangles have been axiomatised in a way that makes them directly applicable to a much wider range of contexts than graphs, even outside mathematics. This talk will outline how this works, with an emphasis on the basic concepts of abstract tangle theory and how these are applicable in real-world scenarios. No knowledge of graph minor theory will be needed.

Machine Learning meets Social Science, 12. November 2019

Machine Learning meets Social Science

Max-Planck-Institut für Intelligente Systems, Seminarraum im Erdgeschoss
Max-Planck-Ring 4, 72076 Tübingen

Anmeldung: Wenn Sie an dem Meeting teilnehmen möchten, melden Sie sich bitte per E-Mail an, Sebastian Schwenk.

  09:00 - 10:30  Session "ML Cluster"

09:00 - 09:20    Begrüßung  &  Fairness in Machine Learning
                          Ulrike von Luxburg
09:20 - 09:40   On the Integration of Machine Learning into Healthcare
                          Thomas Grote
09:40 - 10:00   Explaining Neural Network Decisions with
                          Minimal Supervision         
                          Zeynep Akata

10:00 - 10:20   Explainability & Explanation
                          Eric Raidl

10:30 - 11:00 Kaffeepause

  11:00 - 12:30  Session "Sociology"

11:00 - 11:20   What does ML do? Key questions from a sociology of
                          technology and science perspective
                          Renate Baumgartner
11:20 - 11:40    (How) does ML affect the shop floor and the
                          sociology of work?
                          Werner Schmidt
11:40 - 12:00   Three Worlds of AI – How political strategies differ
                          Daniel Buhr
12:00 - 12:20   Classifying scientific texts with supervised learning algorithms
                          Steffen Hillmert

12:30 Diskussion und Ende

1. Mini-Konferenz "Machine Learning in Science", 22. - 23. Juli 2019

Exzellenzcluster Konferenz "Machine Learning in Science"

Montag, 22.07.2019 | 09:00 Uhr - 19:00 Uhr | Alte Aula
Dienstag, 23.07.2019 | 09:00 Uhr - 16:00 Uhr | Pfleghofsaal

Anmeldung: Bitte melden Sie sich bis spätestens 15.07.2019 verbindlich zu der Tagung an, E-Mail an Sebastian Schwenk. Bitte geben sie an, an welchen Tagen Sie teilnehmen möchten.


Montag, 22.07.2019

Alte Aula, Münzgasse 30, 72070 Tübingen


Opening Remarks

Ulrike von Luxburg, Philipp Berens

Speakers of the Cluster of Excellence “Machine Learning”, University of Tübingen


Towards Neural Networks Which Probably Know When They Don't Know

Matthias Hein

Department of Computer Science, University of Tübingen


Inception Loops - Using Deep Learning to Control Biological Neurons

Fabian Sinz

Department of Computer Science, University of Tübingen




Machine Learning for Heterogeneous and Partially Biased Data in Medicine

Nico Pfeifer

Department of Computer Science, University of Tübingen


The Art of Using t-SNE for Visualization of Very Large Data Sets

Dmitry Kobak

Institute for Ophthalmic Research, University of Tübingen





Machine Learning inside Scientific Methods and Procedures

Philipp Hennig

Department of Computer Science, University of Tübingen



Dynamic Structural Equation Models in the Social and Behavioral Sciences
and Some Estimation Problems

Augustin Kelava

Methods Center, University of Tübingen



Identifying Climate, Vegetation, and Plate Tectonic Controls on Earth’s Topography

Todd Ehlers

Department of Geosciences, University of Tübingen


Poster Session

Posterbeiträge siehe unten *


Mitgliederversammlung Exzellenzcluster (nicht-öffentlich)


Speaker’s Dinner (nicht-öffentlich)

Dienstag, 23.07.2019

Pfleghofsaal, Schulberg 2 (Pfleghof), 72070 Tübingen


Language Change as a Random Walk in Vector Space

Gerhard Jäger

Institute of Linguistics, University of Tübingen


Ethics and Explainability

Eric Raidl, Thomas Grote, Thilo Hagendorff

Ethics & Philosophy Lab, Cluster of Excellence Machine Learning, University of Tübingen





Filter ranking for neural network compression

Mijung Park

Department of Computer Science, University of Tübingen



Fairness and Interpretability in ML for Consequential Decision Making

Isabel Valera

Max Planck Institute for Intelligent Systems, Tübingen




Statistical Limits of Hypothesis Testing: Do We Expect Too Much from ML?

Debarghya Ghoshdastibar

Department of Computer Science, University of Tübingen


How to Learn Predictive Conceptual Structures, including Causal Relationships, and Generate Goal-Directed Control with them? Achievements and Challenges

Martin Butz

Department of Computer Science, University of Tübingen


Machine Learning Algorithms as Tools and Models in Vision Science

Felix Wichmann

Department of Computer Science, University of Tübingen


Closing Remarks

Ulrike von Luxburg, Philipp Berens

Speakers of the Cluster of Excellence “Machine Learning”, University of Tübingen


   * Poster Session, July 22, 16:00 – 18:00 *

Weber, D, Kasneci E., Zell A. (Cluster Innovation Fund Project)
Human-robot interface with eye-tracking and augmented reality to teach mobile robots about the real-world.

             University of Tübingen, Department of Computer Science

Valera I.1, Utz S.² (Cluster Innovation Fund Project)
Extracting expertise from tweets: Exploring the boundary conditions of ambient awareness).

             1Max Planck Institute for Intelligent Systems Tübingen, ² Leibniz-Institut für Wissensmedien

Luxburg U., Wichmann F. (Cluster Innovation Fund Project)

Machine learning approaches for psychophysics with ordinal comparisons

             University of Tübingen, Department of Computer Science

Zabel S.1, Hennig P.2, Nieselt K.1 (Cluster Innovation Fund Project)

Visualizing Uncertainty from Data, Model and Algorithm in Large-Scale Omics Data

             University of Tübingen, 1Center for Bioinformatics Tübingen, ²Department of Computer Science

Karlbauer, M.1, Lensch H.1, Scholten T.², Butz M.1 (Cluster Innovation Fund Project)

Short-to-Mid Scale Weather Forecasting with a Distributed, Recurrent Convolutional ANN

             University of Tübingen, 1Department of Computer Science, ²Department of Geosciences

Behrens, T.1, Schmidt, K.1, Hennig, P.², Scholten, T.1 (Cluster Innovation Fund Project)
Feature engineering for spatial modelling.

             University of Tübingen, 1Department of Geosciences, ²Department of Computer Science

Baayen H.1, Lensch H.² (Cluster Innovation Fund Project)
Enhancing Machine Learning of Lexical Semantics with Image Mining

            University of Tübingen, 1Department of Linguistics, ²Department of Computer Science


Macke J.1, Hennig P.², Berens P.³, Oberlaender M.4
Automatic Data-driven Inference of Mechanistic Models

              1Technische Universität München, Computational Neuroengineering Group 1

               University of Tübingen, ²Department for Computer Science, ³Institute for Ophthalmic Research

              4Center of advanced european studies and research

Pawlowski, T.1, Berens, P.², Kelava, A.³
Emotional cues and alcohol use: evidence from football.

               University of Tübingen, 1Department Institute of Sport Science, ²Institute for Ophthalmic Research, ³ Methods Center

Kilian P.
Predicting math student college dropout with sparse information using approaches from statistical learning

              University of Tübingen, Methods Center

Klopotek M., Oettel M.
Variational autoencoders put up to the test in learning a statistical-mechanical model system

             University of Tübingen, Institut für Angewandte Physik

Lin SC, Oettel M.
Classical density functionals from machine learning

             University of Tübingen, Institut für Angewandte Physik

Greco A.1, Starostin V.1, Hinderhofer A.1, Gerlach A.1, Karapanagiotis C.², Liehr S.², Kowarik S.²,
Schreiber F.1
Fast Scattering Data Analysis Using Machine Learning.
                1University of Tübingen, Institut für Angewandte Physik, Uni Tübingen, ² Bundesanstalt für Materialforschung
                 und -prüfung (BAM), Berlin

Sümer Ö.1,2, Kasneci E.1
Attention Flow: End-to-End Joint Attention Estimation

             University of Tübingen, 1Department of Computer Science, ²Hector Research Institute of Education Sciences and 
           Psychology (HIB)

Fuhl W., Kasneci G., Rosenstiel W., Kasneci E.
Training decision trees as replacement for convolution layers

               University of Tübingen, Department of Computer Science

Zadaianchuk A., Martius G.
Equation Learning for Extrapolation and Control

             Max Planck Institute for Intelligent Systems Tübingen

Machine Learning meets Physics, 10. Juli 2019

Machine Learning meets Physics

AI Gebäude, Hörsaal (EG)

Maria von Linden Str. 6, 72076 Tübingen

9:00 – 9:25:   Frank Schreiber et al: “Analysis of X-ray Scattering Data Using Artificial Neural Networks”
9:25 – 9:50:   Hendrik Lensch: “Deep Learning on Unstructured Point Clouds”
9:50 – 10:15: Martin Oettel: “Density functionals from machine learning”

10:15 – 10:45: Kaffeepause

10:45 – 11:10: Miriam Klopotek: “Variational autoencoders put up to the test in learning a statistical-mechanical
                                                            model system”
11:10 – 11:35: Georg Martius: “Machine Learning for Equation Identification”
11:35 – 12:00: Andreas Zell: “ML, Physics and Robotics”

12:00 – 13:00: Diskussion

TÜFFF - Tübinger Fenster für Forschung, 24. Mai 2019

TÜFFF - Tübinger Fenster für Forschung

Spitzenforschung zum Anfassen für alle Altersgruppen

WANN:  Freitag, 24. Mai 2019, 15 – 22 Uhr
WO:       Hörsaalzentrum der Naturwissenschaften, Auf der Morgenstelle 16
              Eintritt frei

Das „Tübinger Fenster für Forschung“ (TÜFFF) bietet allgemein verständliche und interaktive Einblicke in die Tübinger Spitzenforschung. Mitmach-aktionen, Demonstrationen, Laborführungen, Vorträge, eine Informations-messe sowie ein Science Slam erwarten die interessierte Öffentlichkeit beim 4. TÜFFF an der Universität Tübingen. Durch die Aufbereitung und Präsentation aktueller Forschungsthemen für ein fachfremdes Publikum richtet sich die Veranstaltung an alle Altersgruppen.

Das Exzellenzcluster „Maschinelles Lernen“ beteiligt sich mit 8 Ständen am „Markt der Möglichkeiten“:
  •  Deep Capturing - Computer Vision, Prof. Hendrik Lensch
  •  Deep Deblurring - Computer Vision, Prof. Hendrik Lensch
  •  Interaktive Karte zur Bodenqualität im Raum Tübingen - Geowissenschaften, Prof. Thomas Scholten
  •  Was ist ein neuronales Netzwerk? – Bioinformatik, Prof. Dr. Kay Nieselt
  •  Antizipatives Verhalten in künstlichen neuronalen Netzen - Kognitionswissenschaften, Prof. Martin Butz
  •  Wie kann ein Computer lernen, Wörter in Latein, Russisch, Estnisch und Hebräisch zu beugen?
     Linguistik, Prof. Harald   Baayen
  •  Vorhersage von Blickrichtungen - Neurowissenschaften,
     Prof. Matthias Bethge
  •  Briefumschlag-Computer – Theorie des maschinellen Lernens,
     Prof. Ulrike Luxburg

Weitere Informationen im Programmheft und auf der Veranstaltungsseite

Symposium "Machine Learning in Science", 22. Mai 2019

Symposium "Machine Learning in Science"

22. Mai 2019

Max-Planck-Gästehaus – Hörsaal
Max-Planck-Ring 6, 72076 Tübingen



Tropical circulation: Current challenges and potential for machine learning algorithms
Bedartha Goswami
-- Potsdam-Institut für Klimafolgenforschung (PIK), Deutschland

10:30 High-throughput behavioral analysis for neural circuit understanding
Alexander Mathis
-- Dept. of Molecular and Cellular Biology, Harvard University, USA




Reverse Engineering the Early Visual System with Artificial Neural Networks
Stéphane Deny
-- Department of Applied Physics at Stanford University, USA


Visualization of georeferenced open government data: benefits, issues, opportunities for machine learning research
Auriol Degbelo -- Institute of Geography, University of Osnabrück, Deutschlan

Symposium "Ethik und Philosophie des Maschinellen Lernens in der Wissenschaft", 15. Mai 2019

„Ethik und Philosophie des Maschinellen Lernens in der Wissenschaft“

15. Mai 2019

Max-Planck-Gästehaus – Hörsaal
Max-Planck-Ring 6, 72076 Tübingen




Simplicity and Scientific Progress: A Topological Perspective
Konstantin Genin -- Department of Philosophy, University of Toronto, Kanada


Learning Through Creativity
Caterina Moruzzi -- Department of Philosophy, University of Nottingham, UK




Black-Boxes, Understanding, and Machine Learning
Emily Sullivan-Mumm
-- Ethics and Philosophy of Technology, Delft Data Science, Niederlande


Working at the margins of machine learning – the ethics of labeling
Thilo Hagendorff
-- Internationales Zentrum für Ethik in den Wissenschaften,
Universität Tübingen, Deutschland


Inductive Bias and Adversarial Data
Tom Sterkenburg
-- LMU München, Philosophie,
Wissenschaftstheorie und Religionswissenschaft, Deutschland



13:30 - 14:30

Invited Talk

Co-Opt AI! Charting the emerging field of AI, ethics and social justice

Mona Sloane, Institute for Public Knowledge, New York University, USA

17:10 - 17:55

ML from a DiscO viewpoint: Compressed Sensing, Dictionary Learning
and beyond
(machine learning)
Andreas M. Tillmann
-- Operations Research & Visual Computing Institute,
RWTH Aachen, Deutschland


Symposium "Machine Learning in Science", 18. und 25. - 27. März 2019

Symposium „Machine Learning in Science“

18. und 25. - 26. März 2019

Max-Planck-Gästehaus – Hörsaal
Max-Planck-Ring 6, 72076 Tübingen


Montag, 18. März 2019

09:30 – 10:30

Neutrino Cosmology - Weighing the Ghost Particle with the Universe

   Dr. Elena Giusarma -- Simons Foundation, Flatiron Institute Center for Computational
    Astrophysics, New York, USA


Montag, 25. März 2019


Information Field Theory
   PD Dr. Torsten Enßlin -- MPI für Astrophysik, Garching


Active machine learning for automating scientific discovery
    Prof. Dr. Roman Garnett -- Washington University in St. Louis, USA




Bayesian optimisation: nano-machine-learning
   Assoc. Prof. Dr. Michael Osborne -- University of Oxford, UK


Robust and Scalable Learning with Graphs

   Prof. Dr. Stephan Günnemann  -- TU München




Representing and Explaining Novel Concepts with Minimal Supervision

   Asst. Prof. Dr. Zeynep Akata -- University of Amsterdam




Konstituierende Clustersitzung und Mitgliederversammlung (nicht-öffentlich)


Gemeinsames Abendessen (nicht öffentlich)


Dienstag, 26. März 2019


Expressive, Robust and Accountable Machine Learning for Real-world Data
   Dr. Isabel Valera -- MPI for Intelligent Systems, Tübingen


Algorithms of Vision: From Brains to Machines and Back
   Dr. Alexander Ecker -- Universität Tübingen




From Paired to Unpaired Image-to-Image Translation and Beyond
   Dr. Radu Timofte -- ETH Zürich, Schweiz


Face processing: Bridging Natural and Artificial Intelligence

   Assoc. Prof. Dr. Angela J. Yu -- University of California San Diego, USA



14:00 From statistics to mechanisms, and back
   Prof. Dr. Jakob Macke - TU München

Machine Learning meets Law, 19. März 2019

Machine Learning meets Law, Neue Aula

9:00 Stefan Thomas: Algorithms and Antitrust: How can the law make sure that machine learning does not impede competitive freedom?

9:15 Thilo Hagendorff: Regularory Needs in the Field of AI - From Ethics to Policies

9:30 Thomas Grote: The ethics of (expert-level) algorithmic decision-making

9:45 Isabel Valera: Fairness in Machine Learning

10:00 Oliver Kohlbacher: Legal issues related to AI in medicine

10:15 Discussion as long as we want

Erstes Treffen des Cluster "Machine Learning in Science", 12.-13. November, 2018

Internal Meeting of the Cluster "Machine Learning in Science": November 12-13, 2018

Meeting location: Ground floor lecture hall at the Max-Planck Institute for Intelligent Systems (directions)

Preliminary schedule:

Nov 12th
9:00-10:00   Welcome, information & organisation
                     Ulrike von Luxburg and Philipp Berens
10:00-12:15   Short introductory talks of new group leaders
10:00-10:15   Jörg Stückler
10:15-10:30   Falk Lieder

10:30-11:00   Coffee break

11:00-11:15   Georg Martius
11:15-11:30   Britta Dorn
11:30-11:45   Fabian Sinz
11:45-12:00   Zhaoping Li
12:00-12:15   Gabriele Schweikert
12:15-12:30   Augustin Kelava
12:30-12:45   Michael Krone

12.45 -14:00   Lunch

14:00-15:00   Spotlights for open questions
                       (all PIs: please prepare exactly 1 slide (3 minutes) and send it to Alla at latest Nov 11)
15:00-15:30   Coffee break
15:30-18:00   Work phase for project teams

18:30   Dinner at Hofgut Rosenau

Nov 13th
9:00-10:30   Discussion of open questions, directions, ideas for how
                     the Excellence Cluster should start and work

10:30-11:00   Coffee break

11:00-12:00   Discussion and work phase

12:00-14:00   Lunch

14:00-15:00   Presentations of project ideas and  discussion

15:00-15:30   Coffee break

Download program