280002 Introduction to Physics informed Machine Learning (V+Ü) (WiSe 2021/2022)
Contents, comment
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We will introduce the basic concepts of neural networks and some standard architectures of deep networks. We will discuss some application of deep networks in physics, including data analysis, solving ordinary differential equations and simulating statistical models. We will use python to implement deep networks within hands-on exercises.
Requirements for participation, required level
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Basic knowledge of Linear Algebra and Python are recommended but not necessary.
Teaching staff
Dates ( Calendar view )
| Frequency | Weekday | Time | Format / Place | Period |
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Subject assignments
| Module | Course | Requirements | |
|---|---|---|---|
| 28-M-VP Vertiefung | Vertiefung (B.1) | Graded examination
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Student information |
| Vertiefung (B.2) | Graded examination
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| Vertiefung (B.3) | Graded examination
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Student information | |
| Vertiefung (B.4) | Graded examination
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Student information | |
| Vertiefung (B.5) | Graded examination
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Student information | |
| Übungen zu Vertiefung (B.1) | Study requirement
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Student information | |
| Übungen zu Vertiefung (B.2) | Study requirement
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Student information | |
| Übungen zu Vertiefung (B.3) | Study requirement
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| Übungen zu Vertiefung (B.4) | Study requirement
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| Übungen zu Vertiefung (B.5) | Study requirement
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| 28-MDP_a Methoden der Physik | Angewandte Physik | Study requirement
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The binding module descriptions contain further information, including specifications on the "types of assignments" students need to complete. In cases where a module description mentions more than one kind of assignment, the respective member of the teaching staff will decide which task(s) they assign the students.
- E-Learning Space
- E-Learning Space
This course has a video conference. Details will be displayed to you as a participant of this course. For an event with participant management, you must also be registered as attending by the teaching staff.
- Registered number: 9
- This is the number of students having stored the course in their timetable. In brackets, you see the number of users registered via guest accounts.
- Address:
- WS2021_280002@ekvv.uni-bielefeld.de
- This address can be used by teaching staff, their secretary's offices as well as the individuals in charge of course data maintenance to send emails to the course participants. IMPORTANT: All sent emails must be activated. Wait for the activation email and follow the instructions given there.
- If the reference number is used for several courses in the course of the semester, use the following alternative address to reach the participants of exactly this: VST_305454384@ekvv.uni-bielefeld.de
- Coverage:
- 6 Students to be reached directly via email
- Notes:
- Additional notes on the electronic mailing lists
- Last update basic details/teaching staff:
- Tuesday, December 21, 2021
- Last update times:
- Wednesday, September 8, 2021
- Last update rooms:
- Wednesday, September 8, 2021
- Type(s) / SWS (hours per week per semester)
- lecture (V) + exercise (Ü) / 2+1
- Department
- Faculty of Physics
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- https://ekvv.uni-bielefeld.de/kvv_publ/publ/vd?id=305454384
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- ID
- 305454384