Machine Learning Interest Group (MLIG)

If you want to present at the next ONLINE MEETUP, please write us a short e-mail (philipp.roesch@unibw.de, fabian.deuser@unibw.de). Thanks!
 

The Machine Learning Interest Group (MLIG) aims to bring together scientific staff with an interest in Machine Learning/Artificial Intelligence. It is currently open for researchers from University of the Bundeswehr Munich and Helmut Schmidt University/University of the Federal Armed Forces Hamburg. At the moment we are over 150 people in the MLIG. 

  • Platform for ML/AI topics at UniBw M and HSU
  • Simplify networking between researchers
  • Encouraging exchange between scientists – across institutes

 

We have regular ONLINE MEETUPs (via BBB) where researchers present their work in the domain of Machine Learning. Usually we have 2-3 presentations which last 15 minutes each. After all presentations we have dedicated breakout rooms, where each topic can be discussed in more depth. 

 

We are organised in an Ilias group. This group has a Forum for questions, problems, ideas. Moreover, we have an "Introduce yourself" section where you can leave some words about you and your interests. If you want to join, please do the following:  

 

We look forward to meeting you!

 

Our former ONLINE MEETUPs:

 

July 2022

7th ONLINE MEETUP (details on our Ilias page)

June 2022

Second Stammtisch at UniCasino (details on our Ilias page)

03.05.2022

 

State-of-the-art ML project presentation from Airbus and DesignAI (German):

ASTARTES – Wie AlphaStar die Art und Weise, wie wir Entscheidungen treffen, verändert

13.04.2022, 18:00 First Stammtisch at UniCasino (details on our Ilias page)
02.02.2022 Fabian Deuser (ETTI): CNN-based Audio Signal Processing and Beyond Moritz Dannehl (CODE): XFL: eXtreme Function Labeling Arthur Müller (SOWI): Post-Training and Fine-Tuning of BERT-Based Models for Political Language in German Tweets
08.07.2021 Martin Denk (LRT): Reduction of Image Rotation Variety using Moment of Areas for CNNs [Paper] Benjamin Haser (LRT): Analysis of Phobos internal Structure & SuperResolution  
28.04.2021 Sarah Rudigkeit (LRT): Artificial intelligence based cell-tracking for the evaluation of radiation effects in eucaryotic cells Simon Gottschalk (LRT): An Optimal Control Framework for Deep Reinforcement Learning  
11.03.2021 Peter Mortimer (LRT): Semantic Segmentation in Unstructured Outdoor Environments n/a Philipp J. Rösch (ETTI): Positional Information in Transformer-based Models for Language and Vision
10.02.2021 Maren Hülsmann (LRT): AI4Space Moritz Dannehl (CODE): Using Machine Learning to Understand
Semantics of Binary Machine Code
Philipp J. Rösch (ETTI): Multimodal Machine Learning: Processing Images and Text at the same time

 

Your orga team