© TUHH, Martin Kunze


MLE@TUHH - internes Netzwerkevent


Machine-Learning in Engineering (MLE@TUHH) ist eine Initiative zur Bündelung der Kompetenzen im Bereich Machine Learning an der TUHH mit dem Ziel des Wissenstransfers in Richtung Wirtschaft und Industrie. Am 4. November 2020 um 10:00 Uhr möchte sich MLE allen Mitglieder der Technischen Universität Hamburg vorstellen. Die Veranstaltung wendet sich an alle Mitglieder der TUHH, vom Institutsleiter bis zum Studierenden. Wir wollen so auf die verschiedenen Angebote der MLE Initiative aufmerksam machen und den Informationsaustausch innerhalb der Universität fördern. Insgesamt soll die Sichtbarkeit der Machine-Learning-Aktivitäten innerhalb der TUHH gesteigert, Kooperationen gefördert und interessierten Studierenden ein Einblick ermöglicht werden. Auch die Verankerung von MLE in der der Lehre soll aufgezeigt werden.



  • 4.11.2020 um 10:00 Uhr bis 11:30 Uhr


  • via zoom (Einladung erfolgt über Email@tuall)

Machine Learning in Engineering with MATLAB

MLE together with MathWorks organizes two online events that take place one week apart:

  • Machine Learning with MATLAB (05.11.2020, 14:00-17:00)
  • Deep Learning with MATLAB (12.11.2020, 14:00-17:00)

The events are free for members of TUHH and HZG.

Both events will include

  • Industry/research examples using MATLAB for ML/DL
  • Complete ML/DL workflow using real-world ECG Data (signal processing, referencing image processing in the DL)
    • emphasis on feature engineering, model selection and tuning
    • comparison with automated machine learning (eliminating manual steps required to go from a data set to a predictive model)
    • tips and tricks

Engineers and scientists use AI techniques on large amounts of data in various formats, and across different domains and industries.

In two interactive sessions, we show examples of engineers and scientists using MATLAB for building AI-driven systems. We explore the complete workflow of developing machine learning and deep learning applications with MATLAB using a real-world ECG data set. We also show how to easily develop and apply data analytics solutions that take advantage of enhanced signal processing and AI techniques, including automated feature extraction, model selection and tuning.

We recommend taking the free, two-hour interactive introductions to practical machine learning methods and deep learning methods, respectively, before each session. In between the two sessions, participants will have access to the used data set, to apply your acquired knowledge and share their achieved results. The second session does not have the first session as a prerequisite and can be attended independently.

You receive access to

  • complete workshop materials
  • data sets, software & GPU resources during the workshops
  • data sets in between the workshops, so that participants can try to tune the models and share their results two days before the second workshop

The event will take place via Zoom. Registration for the events is open until 20.10.2020. If you intend to participate:

Download Flyer

Machine Learning in Engineering with MATLAB

Train Your Engineering Network


  • Die Vortragsreihe “Train your engineering network” zu vielfältigen Themen des Machine Learnings wendet sich in erster Linie an die wissenschaftlichen Mitarbeiterinnen und Mitarbeiter der TUHH sowie allgemein in der Region Hamburg und zielt darauf ab, den Informationsaustausch zwischen diesen Personen sowie deren Vernetzung in lockerer Atmosphäre zu fördern. Dadurch sollen die Machine-Learning-Aktivitäten innerhalb der TUHH sowie in deren Umfeld sichtbarer gemacht, Kooperationen gefördert und auch interessierten Studierenden ein Einblick ermöglicht werden.


Ort und Zeit:

  • Die Vorträge finden aktuell im Wintersemester 2020 online montags ab 17:00 und je nach Ankündigung (siehe Vortragstitel) in deutscher oder englischer Sprache statt.

Inhalte und Vortragende im aktuellen Semester:

Vergangene Semester

116.11.20Lennart BargstenDeep learning approaches for intravascular ultrasound image processing: dealing with data scarcity (Video)
223.11.20Morten SchierholzData Sources for Machine Learning Applications in Engineering
330.11.20Marvin KastnerHow to Talk About Machine Learning with Jupyter Notebooks (Video)
407.12.20Thomas KohlscheData-driven construction of fast predictors for complex systems using Gaussian process regression techniques (Video)
514.12.20Benjamin BollWeakbond Detection – Enhancing Vibro-Acoustic Modulation Analysis by Machine Learning
-21.12.20-Winter break
-28.12.20-Winter break
604.01.21Lars Köttner, Jan Mehnen, Denys RomanekoProzessüberwachung mit maschinellem Lernen für semi-automatisches Bohren von Nietlöchern in der Luftfahrtindustrie
711.01.21Christian FeilerExploring Chemical Space using Computational Techniques
818.01.21Rupert AngerbauerAnwendung von Methoden des Maschinellen Lernens zur Darstellung komplexer Strömungsfelder
925.01.21Pascal Gleske, Konrad Nölle, Hendrik Sieck ( HULKs)Distributed Genetic Neural Network Architecture Search at HULKs (Video)


  1. Lennart Bargsten: Deep learning approaches for intravascular ultrasound image processing: dealing with data scarcity.
    Intravascular ultrasound (IVUS) plays a major role in clinical practice when it comes to assessing vessel morphologies during percutaneous coronary interventions (PCIs) or for treatment planning. Usually, the physician estimates morphological features by marking important regions in multiple IVUS images. This is a rather time consuming task and the results depend heavily on the physician’s experience. Automated detection and segmentation of meaningful image content can thus help streamlining the clinical workflow. Data driven methods like deep learning have gained huge importance in the field of medical image analysis over the last years. The usual scarcity of annotated image data in the medical field makes it important to tailor deep learning methods with respect to specific tasks and imaging modalities. Possibilities are generating synthetic image data, considering specific image characteristics as well as performing multi-task learning. This talk presents such approaches for improving deep learning performance on IVUS image analysis.

  2. Morten Schierholz: Data Sources for Machine Learning Applications in Engineering.
    Investigating the possibility of machine learning tools and techniques in the domain of engineering is a widely found concept with an increased number of authors and increasing audience. This development in the machine learning community has been led by researchers from data science. However this development has been possible due to large amount of data that has been available to these researchers in combination with an increased number of computational resources. For example the object recognition on images or in speech would not have been progressed as quickly without the widely available images over the internet. Generating and sharing knowledge is a broadly established concept in engineering domain, in form of conferences or other publications. However it is observed that in the domain of data the sharing of those is not established. First data sources are available where data is shared, however larger projects are not observed. We therefore propose a new database to share data and knowledge of this data. Thereby enabling researches that do not have the possibility to create those data samples by themselves, to work with interesting and rich data and apply new tools and techniques of the machine learning domain or others. Besides the data that is available in the database and the investigated machine learning tools and techniques on this data, a general overview of the database is given.

  3. Marvin Kastner: How to Talk About Machine Learning with Jupyter Notebooks.
    In the field of Machine Learning, scientists often use programming for data preprocessing, running the learning algorithms, and obtaining key metrics. To increase transparency, nowadays more and more additional material (such as datasets, code, documentation etc.) is shared so that fellow researchers can replicate these experiments. Jupyter Notebooks are a very valuable medium in this context – they are capable of displaying documentation, code, its output (such as visualizations, tables or logging messages) etc. side by side. Recently, Jupyter Notebooks have also been used in university courses more often. Here, the students benefit from the integration of code, its documentation, and the related exercise questions into a single interactive document. There are plenty of options how to design very appealing exercises for a course. Both in the scenario of transparent science and when using Jupyter Notebooks for teaching, the author’s code is meant to be run at another machine and achieve the same results. During this talk, possible issues during replication and suitable fixes are highlighted. The open source application JupyterHub can be part of that strategy. While the backend of the Integrated Development Environment runs on TUHH resources, the frontend is just a simple browser application. This reliefs the students from having up-to-date equipment for replication. Especially in times of COVID-19 this allows students to program from home more easily.

  4. Thomas Kohlsche: Data-driven construction of fast predictors for complex systems using Gaussian process regression techniques.
    In many real life applications, engineers are often interested in accessible predictions of a complex systems behavior for which only sparse data is available. The data may arise either from measurements or numerical simulations. Especially for numerical design tasks like optimization and uncertainty quantification where a very large number of model evaluations is required, cheap predictors, often called meta- or surrogate models, are unavoidable. Since gathering data from such systems is typically very costly, this task requires machine learning techniques that are capable of operating on small data sets. For the problem described, Gaussian process regression has proved itself to be a powerful and flexible tool. New extensions of the technique to, e.g., data-fusion concepts can offer solutions to problems that are hard to tackle with other techniques.

  5. Benjamin Boll: Weakbond Detection – Enhancing Vibro-Acoustic Modulation Analysis by Machine Learning.

  6. Lars Köttner, Jan Mehnen, Denys Romaneko: Prozessüberwachung mit maschinellem Lernen für semi-automatisches Bohren von Nietlöchern in der Luftfahrtindustrie.
    Die meisten der mehreren hundert Millionen gebohrten Flugzeugnietbohrungen pro Jahr werden mit semi-automatischen und manuell gesteuerten, pneumatisch angetriebenen Maschinen hergestellt, da eine Vollautomatisierung aufgrund von Arbeitsraumbeschränkungen oft ungeeignet ist. Diese Maschinen sind im Hinblick auf die Anpassbarkeit der Bohrungsparameter unflexibel, was u.a. auf die hohen Qualitätsanforderungen der Luftfahrtbranche zurückzuführen ist. Um zuverlässige und sichere Nietverbindungen herzustellen, ist das Bohren in mehreren Prozessschritten, der Einsatz von Minimalmengenschmierung sowie anschließendes manuelles Entgraten und Reinigen der Bohrungen unabdingbar. Vor diesem Hintergrund ermöglichen neu entwickelte elektrisch angetriebene semi-automatische Advanced Drilling Units (ADUs) neue Potenziale, wie z.B. intelligente Prozesslayouts, eine Online-Zustandsüberwachung durch die Auswertung integrierter Sensordaten und eine automatische Anpassung der Prozessparameter. Für die Zustandsüberwachung des Bohrprozesses wurde der Einsatz von maschinellem Lernen (ML) überprüft, um Schnittkräfte und Prozessbedingungen auf der Grundlage der Ströme der verwendeten Elektromotoren der ADUs vorherzusagen. Die Anwendung von ML auf ADU- Daten ist vorteilhaft, da die hohe Anzahl zu fertigender Nietbohrungen große Datensätze liefert. ML-Methoden wie lineare Regression, künstliche neuronale Netze, Entscheidungsbäume und k-Nearest-Neighbor wurden hinsichtlich ihrer Anwendbarkeit bewertet. Bohrprozesseigenschaften wie Material- und Vorschubgeschwindigkeitsauswahl wurden mit den Modellvorhersagen verglichen. Die vorgestellten Ergebnissen zeigen, dass eine ML-basierte Prozessüberwachung das Potenzial besitzt Prozessabweichungen zuverlässig zu identifizieren und auf diese Weise manuelle Nacharbeit zu reduzieren, eine umfassende Qualitätssicherung zu gewährleisten und die Ausnutzung der Werkzeugstandzeiten zu erhöhen.

  7. Christian Feiler: Exploring Chemical Space using Computational Techniques.
    The degradation behaviour of magnesium (Mg) renders it one of the most versatile engineering materials available today as it can be employed in a large variety of applications ranging from automotive and aerospace components to battery applications where Mg is used as anode material. However, a prerequisite to unlock the full potential of Mg–based materials is gaining control over the corrosion rate. Consequently, bespoke additives must be identified for each application for an optimal performance. Corrosion prevention is essential in transport applications to avoid material failure whereas constant dissolution of the material is required to boost the efficiency of Mg-air primary batteries. Furthermore, the search for benign and efficient corrosion inhibitors has become critical due to the imminent ban of highly effective but toxic chromates. The vast number of small molecules with potentially useful dissolution modulating properties (inhibitors or accelerators) renders conventional experimental discovery methods too time- and resource-consuming. Consequently, computer-assisted selection prior to experimental investigations of the most promising candidates is of great benefit in the search for efficient corrosion modulating additives for Mg-based materials. One of the major challenges is the identification of sound molecular descriptors that correlate well with experimentally derived properties as input parameters with low or no relevance to the modelled property will degrade the model. Towards this end, we utilized colour-coded correlation maps to facilitate an intuitive screening for reliable input features. We recently illustrated the potential of complementary quantum chemical density functional theory (DFT) calculations and machine learning methods for the prediction of corrosion modulating properties of small organic molecules for commercially pure Mg (CP Mg). Furthermore, we developed a workflow that facilitates screening of a large commercial database for an unbiased selection of untested additives for the control of the degradation rate of Mg.

  8. Rupert Angerbauer: Anwendung von Methoden des Maschinellen Lernens zur Darstellung komplexer Strömungsfelder.

  9. Pascal Gleske, Konrad Nölle, Hendrik Sieck (HULKs): Distributed Genetic Neural Network Architecture Search at HULKs.
    The increasing application of artificial neural networks (ANN) in various domains of robotics demands highly optimized ANN architectures. The efficient architecture search requires horizontal and vertical scaling of ANN evaluation. In this talk the HULKs present their approach and application of a scalable genetic algorithm based on distributed task execution, to be used in the context of RoboCup soccer competitions.

HULKs@TUHH - RoboCup SPL Team


We are a team of students from the Hamburg University of Technology. Since the foundation of our team in the year 2013, we are participating anually in the Standard Platform League of the RoboCup competition. The RoboCup is an international initiative with the goal of advancing research in humanoid robotics and artificial intelligence. Our team consists mostly of students who work on this project in their free time. Furthermore, we are one of the few teams that developed their entire framework from scratch.


Since 2014, we are anually hosting the Robotic Hamburg Open Workshop (RoHOW).

The RoHOW is an event hosted by the Hamburg University of Technology that serves the purpose of education and networking. Over 100 scientists and students from all over the world who are researching in the field of humanoid robotics participate in this event.

The goal of this event is the exchange of information between RoboCup teams from different universities and research institutes. Participants have the opportunity to visit lectures and workshops, challenge other teams in competitions, and get in direct contact with each other.

Here you find more information on RoHOW.

HULKs@TUHH - RoboCup SPL Team

Weitere Informationen auf der Webseite der Hulks:

Weitere Veranstaltungen (in Planung)

MLE ist eine Initiative zur Bündelung der Kompetenzen im Bereich Machine Learning an der TUHH, die kontinuierlich interne wie externe Veranstaltungen zum Thema durchführt:

  • Öffentliche Ringvorlesung für die interessierte Öffentlichkeit im Laufe des Wintersemesters 2020/21 mit Persönlichkeiten aus Wissenschaft und Industrie. Organisator: Marc-André Pick

  • MLE-Days @ TUHH: Zweitägiges Event zur Darstellung der Kompetenzen, Forschungsfelder und Zukunftskonzepte an der TUHH zu Anfang nächsten Jahres. Angebot insbesondere für Interessierte aus Wirtschaft, Industrie und öffentlichem Dienst. Ansprechpartner: Volker Turau