ITWM Machine & Deep Learning Seminar
Machine Learning, Deep Learning and the analysis of huge amounts of data in general are becoming more and more important. Such methods are used in almost every area of research, development or industry. The Machine & Deep Learning Seminar of the Fraunhofer ITWM is intended to give interested persons an insight into this large field of research and a deeper understanding. Everyone who wants to learn more about Deep Learning, Machine Learning or AI in general is invited - no matter if students, PhD students, professors or software developers.
This is a series of seminars within the framework of High Performance Center Simulation and Software Based Innovation.
Speakers
In addition to the employees of the related divisions, interested external speakers can also give a lecture in our seminar series. We also have the opportunity to invite external speakers. We are always open to suggestions, suggestions or requests.
Lectures and Talks
A talk should have a minimum length of 20 minutes and a maximum length of 60 minutes. The remaining time is available for questions, comments and feedback. We plan a maximum of 60 minutes for each seminar.
The topic of a talk should either come directly from or is relevant to Deep Learning, Machine Learning, Data Analysis or AI, no matter whether it is about a paper, an own project on or an interesting topic. The complexity can range from an overview talk to a special topic.
Contact
In addition to our github page and the website at ITWM with current information, we offer a mailing list as well: subscribe here
You can also reach us directly by contacting Dr. Dominik Straßel.
Dates Winter Term 2022/23
The seminar takes place regularly on Thursdays at 10 am via MS Teams (at the moment). Titles and other dates will be added in the course of the year.
Note that the talks of our employees of the division are marked with (Fraunhofer ITWM). These dates can be postponed without any problems if there is interest in a lecture on this date.
| Date of Talk | Speaker | Organization | Contact | Title | Abstract | Comment |
|---|---|---|---|---|---|---|
| 20/10/2022 | t.b.a. | |||||
| 27/10/2022 | t.b.a. | |||||
| 03/11/2022 | t.b.a. | |||||
| 10/11/2022 | t.b.a. | |||||
| 17/11/2022 | t.b.a. | |||||
| 24/11/2022 | t.b.a. | |||||
| 01/12/2022 | t.b.a. | |||||
| 08/12/2022 | t.b.a. | |||||
| 15/12/2022 | t.b.a. | |||||
| 22/12/2022 | t.b.a. | |||||
| CHRISTMAS BREAK | ||||||
| 12/01/2023 | t.b.a. | |||||
| 19/01/2023 | t.b.a. | |||||
| 26/01/2023 | t.b.a. | |||||
| 02/02/2023 | t.b.a. | |||||
| 09/02/2023 | Klaus Dorer | IMLA, Offenburg | klaus.dorer@hs-offenburg.de | Deep Reinforcement Learning for Robot Soccer | ||
| 16/02/2023 | t.b.a. | |||||
| WINTER TERM 2022/23 | ||||||
| WINTER TERM 2022/23 | t.b.a. | |||||
| WINTER TERM 2022/23 | t.b.a. |
