Brown Bag Seminars on Data Science
-
We aim for talks on methods, papers, conference experiences and ideas you want to discuss
Discussions and all kind of questions (also stupid ones) are welcome!!
No slides are required - the presenter can freely decide on the format (blackboard, talk, slides) and language (english or german).
You might bring your lunch with you
Active contributions are required and welcome from everybody - please contact us (sick@zhaw.ch) if you have ideas for a topic or want to join the seminar mailing list.
Talks
| When/Where | Who | Title | Material |
| 29. April 2021 | Beate Sick | Bayes for dummies - Transformation Models for Flexible Posteriors in Variational Bayes | slides |
| 29. April 2021 | Lilach Goren | CNNs for Fault Detection: a Wind Turbine Use Case | slides |
| 19. Nov 2020 | Oliver Dürr | Ordinal Transformation Models (ONTRAM) | slides |
| 18. June 2020 | Lucas Kook | Diluted causality via anchor regeression | slides |
| 8. May 2020 | Beate Sick | Deep transformation models | slides |
| 10. Oct 2019 | Lucas Kook, UZH | Transformation models - an introduction | slides |
| 3. Oct 2019 | Oliver Dürr, HTWG | Go with the Flow | slides | github |
| 21. May 2019, TB610 | Beate Sick | Uncertainties in Neural Networks | slides |
| 23. January 2019 | Bernd Freisleben, Uni Marburg | Deep Learning for Computer Visision | slides |
| 15. November 2018 | Thilo Stadelmann | AlphaZero: Learning Games from Selfplay |
slides
video |
| 19. July 2018 | Beate Sick | Historic anecdotes about (non-) causal thinking in statistics and artificial intelligence | slides |
| 19. June 2018 | Vasily Tolkachev | Deep Image Representations with Explainable Features | slides |
| 29. May 2018 | Gilles Kratzer, UZH | Bayesian Networks Learning in Nutshell | slides |
| 29. November 2017 | Gabriel Eyyi | Introduction to Reinforcement Learning:: Introduction / OpenAI Gym | slides code |
| 29. November 2017 | Melanie Geiger | Introduction to Reinforcement Learning:: Deep-Q Learning | slides nb env |
| 29. November 2017 | Oliver Dürr | Introduction to Reinforcement Learning:: Policy Gradient | slides |
| 23. November 2017 | Domenic Bertschi | Extreme Gradient Boosting | slides code |
| 2. November 2017 | Beate Sick | Some statistical considerations for predictive modelling | slides |
| 19. October 2017 | Olivier Verscheure (EPFL) | From Open Data Science to Collective Intelligence | slides (will come soon) |
| 2. August 2017 | Lukas Tuggener | Impressions of the ICVSS CV Summer School | no slides |
| 5. July 2017 | Ismail Elezi | On Relaxation Labeling | slides |
| 7. June 2017 | Lukas Tuggener | An overview of modern methods for segmantic image segmentation | slides |
| 18. January 2017 | Ana Sima, Jan Stampfli, Kurt Stockinger | Real-Time Alarm Verification with Spark Streaming and Machine Learning | slides |
| 10. January 2017 | Oliver Dürr | Introduction to Recurrent Neural Networks (RNN) | slides github |
| 10. January 2017 | Beate Sick | Dropout and Bayesian Statistics | slides |
| 21. December 2016 | Thilo Stadelmann | Datalab Christmas Lecture: Generative Adverserial Networks (GAN) | slides |
| 23. November 2016 | Oliver Dürr | Deep Learning with TensorFlow (Again in TB 534) | slides github |
| 16. November 2016 | Oliver Dürr | Introduction to TensorFlow | slides R python |
| 22. September 2016 | Beate Sick | individual conditional expectation plots | slides |
| 6. July 2016 | Thoralf Mildenberger | Compositional Data | slides code |
| 15. June 2016 | Manuel Renold | Climate Modelling on High Performance Computer (HPC) | slides |
| 11. May 2016 | Oliver Dürr | Variational Autoencoders | slides github |
| 20. April 2016 | Yang Hu | Extreme Learning Machines and Its applications in Fault Detection | slides |
| 11. April 2016 | Marcello Pelillo | Grouping Games | slides |
| 17. Feburary 2016 | Philipp Ackermann | D3.js (Data-Driven Documents) | slides github |
| 27. January 2016 | Beate Sick | Auto encoders, PCA and semantic hashing | slides |
| 21. October 2015 | Stefan Glüge | Learning Long-term Dependencies in Segmented-Memory Recurrent Neural Networks | slides github |
| 7. October 2016 | Kurt Stockinger | Spark | slides |
| 15. August 2015 | Christoph Heitz | Data Products | slides |
| 1. July 2015 | Thilo Stadelmann, Thierry Musy | Graf Transformer Network | slides |
| 11. June 2015 | Beate Sick | Causal inference with graphical models | slides |
| 20. May 2015 | Christoph (Axa) | Telematik | not available |
| 22. April 2015 | Oliver Dürr | Convolutional Neural Nets II: Hands on | slides github |
| 1. April 2015 | Thierry Musy | IPython for Data Scientistc | slides github |
| 3. March 2015 | Yan Ke, Derek Hoiem, Rahul Sukthankar | Computer Vision for Music Identification | slides |
| 21. January 2015 | Fatih Uzdilli | Deep Learning of Text Representations | slides |
| 17. December 2014 | Oliver Dürr | Convolutional Neural Networks | slides |
| 3. December 2014 | Melanie Imhof | Big Data Query Processing with Mixed Workloads | slides |
| 29. October 2014 | Bernd Wiswedel | Introduction to KNIME | slides |
| 15. October 2014 | Beate Sick | Introduction to Random Forests | slides |
| 17. September 2014 | Oliver Dürr | Introduction to git/github | slides |
| 10. July 2014 | Thoralf Mildenberger | Assoziationmining | slides R |
| 11. June 2014 | Thilo Stadelmann | Multimedia Analysis: Speaker Recognition | slides |
| 21. May 2014 | Thilo Stadelmann | Multimedia Analysis: Audio Type Classification | slides |
| 16. April 2014 | Oliver Dürr | Community Detection / Graph Drawing | slides |
| 26. March 2014 | Melanie Imhof | Multifaceted Feature Set for Information Retrieval | slides |
| 26. February 2014 | Fatih Uzdilli | Sentiment Analysis | slides I slides II |
| 5. February 2014 | Kurt Stockinger | Click Stream Analyse | blackboard |
| 15. January 2014 | Marcel Dettling | Bagging and Boosting | slides |
| 11. December 2013 | Kurt Stockinger | Big Data Analyse mit Hadoop | slides I slides II |
| 20. November 2013 | Thoralf Mildenberger | Prediction with Expert Advice | slides R |