Jekyll2023-01-23T16:28:03+01:00https://mlcdresden.gitlab.io/pages/feed.xmlMLC - Machine Learning Community DresdenWe are a grass-roots movement of Machine Learning practitioners in Dresden, Germany.mlcdresdenMLC Seminar introducing kernel methods and Gaussian processes2023-01-20T07:00:00+01:002023-01-20T07:00:00+01:00https://mlcdresden.gitlab.io/pages/seminars/gaussianprocesses<p>It is our pleasure to announce the first seminar in 2023:</p>
<blockquote>
<p><strong>Introduction to kernel methods and Gaussian processes</strong></p>
</blockquote>
<p>presented by Steve Schmerler (HZDR) to our community on <strong>February 06, 2023, at 1:30pm CET</strong> as an online seminar.</p>
<h2 id="abstract-of-the-talk">Abstract of the talk</h2>
<p>Kernel ridge regression (KRR) is an example of a kernel method. It represents the family of “classical” machine learning techniques (e.g. no neural networks) that is popular in application areas where data is not necessarily abundant and where one can build expressive models by using (physics-informed) engineered features. A closely related method is Gaussian process regression (GPR), whose foundations lie in Bayesian statistics and which can provide powerful uncertainty information. In this introduction, Steve will cover the mathematical foundations of those methods, shed light onto their close relation and discuss when they may be a useful parallel approach to more flexible models such as neural networks.</p>
<h2 id="seminar-details">Seminar Details</h2>
<p>We facilitate this meeting under the <a href="https://dresden-code-of-conduct.org/de/">Dresden Code of Conduct</a>. <strong>Please be mindful of your peers and supportive in your communication!</strong></p>
<p>To make the seminar more interactive for everyone, we set up interactives <a href="https://notes.desy.de/-Y8TPFrDREa6u5J8_radPA?edit">notes</a>. Please use them to connect to others, submit questions, add further material for the talk. The talk notes will appear on this website after the talk.</p>
<h2 id="connection-details">Connection Details</h2>
<p>Are shared in the <a href="https://notes.desy.de/-Y8TPFrDREa6u5J8_radPA?edit">notes</a>.</p>mlcdresdenIntroducing Kernel Methods and Gaussian ProcessesMLC XMAS Event - MLC meets ScaDS.AI Living Lab2022-12-01T09:00:00+01:002022-12-01T09:00:00+01:00https://mlcdresden.gitlab.io/pages/events/xmas-event<p>It is our pleasure to announce the MLC XMAS event in 2022 in cooperation with the ScaDS.AI living lab:</p>
<blockquote>
<p><strong>XMAS Event – MLC meets ScaDS.AI Living Lab</strong></p>
</blockquote>
<p>presented by MLC and ScaDS.AI to our community on <strong>December 15, 2022, at 04pm - 06pm CEST</strong> as an onsite event.</p>
<h2 id="abstract-of-the-event">Abstract of the event</h2>
<p>There will be some machine learning related short pitches, fun (digital)
games, and plenty of Christmas mood.</p>
<p>To better arrange the event, please mark your participation in the poll:
<a href="https://terminplaner.dfn.de/Kub2vllz2gdg2vre">https://terminplaner.dfn.de/Kub2vllz2gdg2vre</a></p>
<p>If you would also like to give a pitch, please send an email with the title to
<a href="mailto:mlc-orga@groups.tu-dresden.de">mlc-orga@groups.tu-dresden.de</a>.
Your pitch should take a maximum of 5 min.</p>
<p>Hope to see you there!</p>
<h2 id="location-details">Location Details</h2>
<p>Room 1020, Andreas-Pfitzmann-Bau, Nöthnitzer Str. 46
Find more details here: <a href="https://navigator.zih.tu-dresden.de/etplan/apb/01/raum/542101.3250">https://navigator.zih.tu-dresden.de/etplan/apb/01/raum/542101.3250</a>.</p>mlcdresdenXMAS networking eventMLC Seminar on PyRCN2022-09-11T12:00:00+02:002022-09-11T12:00:00+02:00https://mlcdresden.gitlab.io/pages/seminars/reservoirs<p>It is our pleasure to announce another seminar in 2022:</p>
<blockquote>
<p><strong>PyRCN – Reservoir Computing Networks in Python</strong></p>
</blockquote>
<p>presented by Peter Steiner (TU Dresden) to our community on <strong>October 21, 2022, at 10am CEST</strong> as an online seminar.</p>
<h2 id="abstract-of-the-talk">Abstract of the talk</h2>
<p>Reservoir Computing Networks (RCNs) belong to a group of machine learning techniques that are simple and yet effective in solving non-linear problems, such as classification or regression. With a significantly easier training process than state-of-the-art deep neural networks, such as Convolutional Neural Networks (CNNs) and LSTMs, they achieve comparable results in various classification and regression tasks. In this talk, we introduce RCNs together with our publicly available Python toolbox PyRCN (Python Reservoir Computing Networks) for optimizing, training and analyzing RCNs on arbitrarily large datasets. It provides a platform for educational and exploratory analyses of RCNs, as well as a framework to apply RCNs on complex tasks including sequence processing.</p>
<h2 id="seminar-details">Seminar Details</h2>
<p>We facilitate this meeting under the <a href="https://dresden-code-of-conduct.org/de/">Dresden Code of Conduct</a>. <strong>Please be mindful of your peers and supportive in your communication!</strong></p>
<p>To make the seminar more interactive for everyone, we set up interactives <a href="https://notes.desy.de/7Y5W9_oLSNGiPWBRHbDqFg">notes</a>. Please use them to connect to others, submit questions, add further material for the talk. The talk notes will appear on this website after the talk.</p>
<h2 id="connection-details">Connection Details</h2>
<p>Are shared in the <a href="https://notes.desy.de/7Y5W9_oLSNGiPWBRHbDqFg">notes</a>.</p>mlcdresdenReservoir Computing Networks in PythonMLC Seminar on Prediction of designer-recombinases for DNA editing with generative deep learning2022-08-01T12:00:00+02:002022-08-01T12:00:00+02:00https://mlcdresden.gitlab.io/pages/seminars/vaes-for-genomics<p>It is our pleasure to announce another seminar in 2022:</p>
<blockquote>
<p><strong>Prediction of designer-recombinases for DNA editing with generative deep learning</strong></p>
</blockquote>
<p>presented by Lukas T. Schmitt (TU Dresden) to our community on <strong>September 12, 2022, at 11am</strong> as an online seminar.</p>
<h2 id="abstract-of-the-talk">Abstract of the talk</h2>
<p>Site-specific tyrosine-type recombinases are effective tools for genome engineering, with the first engineered variants having demonstrated therapeutic potential. So far, adaptation to new DNA target site selectivity of designer-recombinases has been achieved mostly through iterative cycles of directed molecular evolution. While effective, directed molecular evolution methods are laborious and time consuming. Here we present RecGen (Recombinase Generator), an algorithm for the intelligent generation of designer-recombinases. We gathered the sequence information of over two million Cre-like recombinase sequences evolved for 89 different target sites with which we trained Conditional Variational Autoencoders for recombinase generation. Experimental validation demonstrated that the algorithm can predict recombinase sequences with activity on novel target-sites, indicating that RecGen is useful to accelerate the development of future designer-recombinases.</p>
<h2 id="seminar-infrastructure">Seminar Infrastructure</h2>
<p>We facilitate this meeting under the <a href="https://dresden-code-of-conduct.org/de/">Dresden Code of Conduct</a>. <strong>Please be mindful of your peers and supportive in your communication!</strong></p>
<p>To make the seminar more interactive for everyone, we set up interactives <a href="https://notes.desy.de/7Y5W9_oLSNGiPWBRHbDqFg">notes</a>. Please use them to connect to others, submit questions, add further material for the talk. The talk notes will appear on this website after the talk.</p>
<h2 id="connection-details">Connection Details</h2>
<p>Are shared in the notes.</p>mlcdresdenUnsupervised Generation of Genome SequencesMLC Seminar on new ZIH HPC compendium2022-05-30T12:00:00+02:002022-05-30T12:00:00+02:00https://mlcdresden.gitlab.io/pages/seminars/IntroHPC_compendium<p>It is our pleasure to announce a further seminar in 2022:
<strong>Introduction to new ZIH HPC compendium</strong> presented by Martin Schroschk and Christoph Lehmann (ZIH TU Dresden) to our community on <strong>July 14, 2022, at 10am</strong> as an online seminar.
The Center for Information Services and High Performance Computing (ZIH) at TU Dresden provides HPC resources for researchers. These resources are also available to researchers who are not members of TU Dresden.</p>
<h2 id="abstract-of-the-talk">Abstract of the talk</h2>
<p>This talk will provide an overview of the new ZIH HPC compendium, that is typically a first point of reference for users of the ZIH HPC system.
Thereby, the new HPC compendium is also of interest for users of other HPC systems as it contains articles of a generic use.
An important feature of the new HPC compendium is the possibility of active user contibution by directly opening issues and even writing articles.
For the talk a main focus will lie on aspects/questions of machine learning and data analytics usage of the HPC system.
Please note, that access to the ZIH HPC system is possible as well for people who are not members of the TU Dresden.</p>
<h2 id="seminar-infrastructure">Seminar Infrastructure</h2>
<p>We facilitate this meeting under the <a href="https://dresden-code-of-conduct.org/de/">Dresden Code of Conduct</a>. <strong>Please be mindful of your peers and supportive in your communication!</strong></p>
<p>To make the seminar more interactive for everyone, we set up interactives notes <a href="https://hackmd.io/to/be/added/">here</a>. Please use them to connect to others, submit questions, add further material for the talk. The talk notes will appear on this website after the talk.</p>
<h2 id="connection-details">Connection Details</h2>
<p>This is an online meeting via BigBlueButton.</p>
<p>Participants with ZIH login:
<a href="https://selfservice.zih.tu-dresden.de/l/link.php?m=185544&p=1e5a8705">https://selfservice.zih.tu-dresden.de/l/link.php?m=185544&p=1e5a8705</a></p>
<p>Participants without ZIH login:
<a href="https://selfservice.zih.tu-dresden.de/link.php?m=185544&p=8ef6332a">https://selfservice.zih.tu-dresden.de/link.php?m=185544&p=8ef6332a</a></p>mlcdresdenSeminar ZIH HPC Compendium July 14, 2022MLC Seminar on Interpretable Classifications2022-02-21T07:00:00+01:002022-02-21T07:00:00+01:00https://mlcdresden.gitlab.io/pages/seminars/villmannseminar-on-20220315<p>It is our pleasure to announce the speaker of our first seminar in 2022: <strong>Thomas Villmann (HS Mittweida)</strong>.</p>
<p>Thomas agreed to provide a discussion about</p>
<p><strong>Interpretable Models for Classification Learning - Performance is not enough</strong></p>
<p>to our community on <strong>March 15, 2022, at 10am</strong> as an online seminar (see connection details below).</p>
<h2 id="abstract-of-the-talk">Abstract of the talk</h2>
<blockquote>
<p>Classification learning currently is dominated by deep neural networks, which frequently achieve best performance for a given task. Moreover, due to sophisticated end-to-end learning methods and strategies as well as pre-trained modules for many application tasks, these networks became popular for a broad community in applied AI development. The latter one also reduce the problems of the requirement of huge data bases for adequate training as well as the time consuming training due to vanishing gradients.</p>
<p>Another major drawback of these deep networks is that usually they act as black-box approaches. Many attempts are made to explain their behavior including saliency/heat maps, simplified models imitating the behavior and visual explanations.</p>
<p>Following C. Rudin, a better approach is to require interpretability for a machine learning model by design. One of the most prominent interpretable approaches in unsupervised and supervised learning is based on vector quantization.</p>
<p>In the talk, we will show recent developments for classification learning based on vector quantization models. We give the basic mathematical justifications for several related approaches together with their possible application areas from a methodology point of view. Further, we demonstrate the interpretation possibilities of those networks, which follow from the model design.</p>
</blockquote>
<h2 id="about-our-speaker">About our speaker</h2>
<p><strong>Thomas Villmann</strong> is the director of the Saxon Institute for Computational Intelligence and Machine Learning (SICIM) at the University of Applied Sciences Mittweida. If you would like to explore more about Thomas, allow us to refer to his professional <a href="https://www.cb.hs-mittweida.de/webs/villmann.html">website</a>.</p>
<h2 id="seminar-infrastructure">Seminar Infrastructure</h2>
<p>We facilitate this meeting under the <a href="https://dresden-code-of-conduct.org/de/">Dresden Code of Conduct</a>. <strong>Please be mindful of your peers and supportive in your communication!</strong></p>
<p>To make the seminar more interactive for everyone, we set up interactives notes <a href="https://notes.desy.de/t3nZypI2Sf2Z75JhmH8xug?edit">here</a>. Please use them to connect to others, submit questions, add further material for the talk. The talk notes will appear on this website after the talk.</p>
<h2 id="connection-details">Connection Details</h2>
<blockquote>
<p>Join Zoom Meeting
https://us06web.zoom.us/j/95240535967?pwd=VkY2UlFscjkwM2xidC82QlpYbWY5UT09</p>
<p>Meeting ID: 952 4053 5967
Passcode: 690757
One tap mobile
+496971049922,,95240535967#,,,,<em>690757# Germany
+496938079883,,95240535967#,,,,</em>690757# Germany</p>
<p>Dial by your location
+49 69 7104 9922 Germany
+49 69 3807 9883 Germany
+49 69 3807 9884 Germany
+49 69 5050 0951 Germany
+49 69 5050 0952 Germany
+49 695 050 2596 Germany
+1 720 707 2699 US (Denver)
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Meeting ID: 952 4053 5967
Passcode: 690757
Find your local number: https://us06web.zoom.us/u/kdCCl6grIR</p>
</blockquote>mlcdresdenMachine Learning Seminar on March 15, 2022ML Community Event,2021-06-14T08:00:00+02:002021-06-14T08:00:00+02:00https://mlcdresden.gitlab.io/pages/seminars/communityevent-on-20210622<p>It is our pleasure to announce the speaker of our fourth seminar in 2021: <strong>Dánnell Quesada</strong> will present the current status and challenges of his project:
<strong>Statistical downscaling of precipitation using skip connections based models</strong> <br />
to our community on <strong>22nd June 2021, 10 am CEST</strong> as an online seminar.</p>
<h2 id="abstract-of-the-talk">Abstract of the talk</h2>
<blockquote>
<p>Regional climate change adaptation and mitigation models for different domains (agriculture, ecosystems, energy, flooding) require spatial datasets with a much higher resolution than the ones provided in most climate projections databases. To overcome this scale mismatch, downscaling methods are applied. In the present project, several variables at different atmospheric levels are used as predictors to obtain precipitation on a 1 km pixel size for Saxony. Deep learning algorithms used in computer vision, such as Unet and Unet++ were tested while exploring the hyperparameter space.</p>
</blockquote>
<h2 id="about-our-speaker">About our speaker</h2>
<p>Dánnell Quesada studied Civil Engineering in Costa Rica, where he worked as hydrologist/hydraulic engineer for a renewable energy consultant firm. He did his MSc at the TU Dresden, Hydroscience and Engineering, and is currently working towards his PhD at the chair of Meteorology (TUD) with a focus on machine learning to obtain high spatio-temporal resolution climate projections and analyse their implications on ecosystems.</p>
<h2 id="seminar-infrastructure">Seminar Infrastructure</h2>
<p>We facilitate this meeting under the <a href="https://dresden-code-of-conduct.org/de/">Dresden Code of Conduct</a>. <strong>Please be mindful of your peers and supportive in your communication!</strong></p>
<p>To make the seminar more interactive for everyone, we set up interactives notes <a href="https://hackmd.io/@fkYnCNm8RhqPqv5cMetCdQ/BksWYcEiu/edit">here</a>. Please use them to connect to others, submit questions, add further material for the talk. The talk notes will appear on this website after the talk.</p>
<h2 id="connection-details">Connection Details</h2>
<blockquote>
<p>Topic: Machine Learning Community Dresden</p>
<p>Join Zoom Meeting
https://zoom.us/j/95240535967?pwd=VkY2UlFscjkwM2xidC82QlpYbWY5UT09</p>
<p>Meeting ID: 952 4053 5967
Passcode: 690757
One tap mobile
+496938079883,,95240535967#,,,,<em>690757# Germany
+496950502596,,95240535967#,,,,</em>690757# Germany</p>
<p>Dial by your location
+49 69 3807 9883 Germany
+49 695 050 2596 Germany
+49 69 7104 9922 Germany
+49 30 5679 5800 Germany
+1 669 900 9128 US (San Jose)
+1 253 215 8782 US (Tacoma)
+1 301 715 8592 US (Washington DC)
+1 312 626 6799 US (Chicago)
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Meeting ID: 952 4053 5967
Passcode: 690757
Find your local number: https://zoom.us/u/acAUveM2gI</p>
</blockquote>mlcdresdenMachine Learning Community Event on 22nd June 2021, 10 am CESTMLC Seminar on Surgical Video Analysis2021-04-19T08:00:00+02:002021-04-19T08:00:00+02:00https://mlcdresden.gitlab.io/pages/seminars/funkeseminar-on-20210504<p>It is our pleasure to announce the speaker of our third seminar in 2021: <strong>Isabel Funke (NCT Dresden)</strong>. Isabel agreed to provide a discussion about <br />
<strong>Deep learning for video recognition and applications in surgical video analysis</strong> <br />
to our community on <strong>May 4, 2021, at 10am</strong> as an online seminar.</p>
<h2 id="abstract-of-the-talk">Abstract of the talk</h2>
<blockquote>
<p>This talk will give an overview of deep models for video recognition, including spatiotemporal convolutional neural networks and approaches to long-term temporal modeling (e.g. recurrent networks and temporal convolutional networks). Furthermore, applications in the surgical domain will be presented, where an understanding of the surgical video stream is required to implement meaningful computer-based assistance for the operating room of the future.</p>
</blockquote>
<h2 id="about-our-speaker">About our speaker</h2>
<p><strong>Isabel Funke</strong> studied computer science in Karlsruhe and Gothenburg and interned at Philips Research in Eindhoven. She is currently working towards her PhD at the National Center for Tumor Diseases (NCT) Dresden in the Division of Translational Surgical Oncology. Her research focuses on automatic, video-based analysis of surgical performance.</p>
<h2 id="seminar-infrastructure">Seminar Infrastructure</h2>
<p>We facilitate this meeting under the <a href="https://dresden-code-of-conduct.org/de/">Dresden Code of Conduct</a>. <strong>Please be mindful of your peers and supportive in your communication!</strong></p>
<p>To make the seminar more interactive for everyone, we set up interactives notes <a href="https://hackmd.io/@Z3k-IRVbRJuDU-0M-Yfqzw/S1be0h5Iu/edit">here</a>. Please use them to connect to others, submit questions, add further material for the talk. The talk notes will appear on this website after the talk.</p>
<h2 id="connection-details">Connection Details</h2>
<blockquote>
<p>Time: May 4, 2021 10:00 AM Amsterdam, Berlin, Rome, Stockholm, Vienna</p>
<p>Join Zoom Meeting
https://zoom.us/j/96089493875?pwd=RHlDcDRiRE85VzMxVmdOZmNMNGFMZz09</p>
<p>Meeting ID: 960 8949 3875
Passcode: 073888
One tap mobile
+496938079883,,96089493875#,,,,<em>073888# Germany
+496950502596,,96089493875#,,,,</em>073888# Germany</p>
<p>Dial by your location
+49 69 3807 9883 Germany
+49 695 050 2596 Germany
+49 69 7104 9922 Germany
+49 30 5679 5800 Germany
+1 301 715 8592 US (Washington DC)
+1 312 626 6799 US (Chicago)
+1 346 248 7799 US (Houston)
+1 646 558 8656 US (New York)
+1 669 900 9128 US (San Jose)
+1 253 215 8782 US (Tacoma)
Meeting ID: 960 8949 3875
Passcode: 073888
Find your local number: https://zoom.us/u/abiwXyBIqx</p>
</blockquote>mlcdresdenMachine Learning Seminar on May 4, 2021Recap of our MLC Seminar on Reinforcement Learning2021-04-06T00:00:00+02:002021-04-06T00:00:00+02:00https://mlcdresden.gitlab.io/pages/seminars/postmortem-rlseminar<p>On April 6, 2021, <strong>Fabian Hart (TU Dresden)</strong> presented his <strong>Introduction to Reinforcement Learning</strong> to our community. If you weren’t able to attend, his slide deck is available on <a href="https://figshare.com/s/441d5dd9a7342bb82fde">figshare.com:441d5dd9a7342bb82fde</a>.</p>
<p>In a nutshell, Fabian introduced reinforcement learning, so the optimization of an agent’s actions over time given a specific reward. Fabian chose to start from his application as his focus is to design an automated system to navigate freighters on the Lower Rhine river in Germany. Among the important tasks to approach with this technique are: trajectory planning, overtake maneuver decision making, ship-following mode, safety distances, …</p>
<p>The agent, a ship in Fabian’s case, is exposed to a bag of input information it has to account for while making decisions. These can be of external nature such as the river geometry ahead, the river flow dynamics, or the flow depth. They can also be more internal to the vessel itself, such as the engine dynamics, hydrodynamic effects reacting to the rudder, <a href="https://en.wikipedia.org/wiki/Squat_effect">ship squat dependent on speed</a> to name a few. From the many actions such a vessel has to perform, Fabian selected the Longitudinal Control as the field he introduced reinforcement learning. Make sure to dive into his <a href="https://figshare.com/s/441d5dd9a7342bb82fde">slides on figshare</a> to get a closer look.</p>
<p>The seminar was well attended reaching 60 participants logged into zoom. It was nice to see colleagues from Leipzig there too. The question and answer session sampled from this crowd rather effectively. The interactive notes on <a href="https://hackmd.io">hackmd.io</a> captured nine questions which in turn led to sub-discussions each. The colloquium that developed was super interesting even for people not familiar with reinforcement learning. Community members also shared interesting papers that e.g. investigate the interpretability of such agents. Feel free to have a look at <a href="/pages/assets/20210406-seminar-fabianhart-rl-hackmd-notes.md">the show notes</a>.</p>mlcdresdenMLC Seminar on Reinforcement Learning2021-03-29T00:00:00+02:002021-03-29T00:00:00+02:00https://mlcdresden.gitlab.io/pages/seminars/rlseminar-on-20210406<p>It is our pleasure to announce the speaker of our second seminar in 2021: <strong>Fabian Hart (TU Dresden)</strong>. Fabian agreed to provide an <br />
<strong>Introduction to Reinforcement Learning</strong> <br />
to our community on <strong>April 6 at 10am</strong> as an online seminar. The abstract for the talk is:</p>
<blockquote>
<p>The scope of this talk is to introduce Reinforcement Learning (RL) with the interesting practical use case. We discuss the motivation for RL and the basic components and principles. Q learning as a fundamental algorithm for RL is introduced and two variants, DQN and DDPG, are briefly explained. Both variants are presented in the context of the current research project, the development of a two-dimensional ship traffic simulator on the Lower Rhine.</p>
</blockquote>
<h2 id="seminar-infrastructure">Seminar Infrastructure</h2>
<p>We facilitate this meeting under the <a href="https://dresden-code-of-conduct.org/de/">Dresden Code of Conduct</a>. <strong>Please be mindful of your peers and supportive in your communication!</strong></p>
<p>To make the seminar more interactive for everyone, we set up interactives notes <a href="https://hackmd.io/@Z3k-IRVbRJuDU-0M-Yfqzw/HJwfyukSu/edit">here</a>. Please use them to connect to others, submit questions, add further material for the talk. The talk notes will appear on this post after the talk.</p>
<h2 id="connection-details">Connection Details</h2>
<blockquote>
<p>Time: Apr 6, 2021 10:00 AM Amsterdam, Berlin, Rome, Stockholm, Vienna</p>
<p>Join Zoom Meeting <a href="https://us02web.zoom.us/j/86727838757?pwd=YXM5eXBiaTFuM0t6V1Jac1JsekxRZz09">through this direct link</a></p>
<p>Meeting ID: <code class="language-plaintext highlighter-rouge">867 2783 8757</code>
Passcode: <code class="language-plaintext highlighter-rouge">195131</code></p>
<p>One tap mobile</p>
<p>+496971049922,,86727838757#,,,,*195131# Germany</p>
<p>+493056795800,,86727838757#,,,,*195131# Germany</p>
<p>Dial by your location
<code class="language-plaintext highlighter-rouge">+49 69 7104 9922</code> Germany<br />
<code class="language-plaintext highlighter-rouge">+49 30 5679 5800</code> Germany<br />
<code class="language-plaintext highlighter-rouge">+49 69 3807 9883</code> Germany<br />
<code class="language-plaintext highlighter-rouge">+49 695 050 2596</code> Germany</p>
<p>Meeting ID: <code class="language-plaintext highlighter-rouge">867 2783 8757</code>
Passcode: <code class="language-plaintext highlighter-rouge">195131</code>
Find your local number: https://us02web.zoom.us/u/k3CUHOGbn</p>
</blockquote>mlcdresdenGet your Bearings on RL on April 6, 2021