392132 Theory of Deep Neural Networks (V) (WiSe 2022/2023)
Contents, comment
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This lecture offers a journey through recent approaches towards a deeper theoretical understanding of deep neural networks. The lecture will proceed mainly along a "theory backbone" provided in the recent book "The principles of deep learning theory" by Roberts, Yaida and Hanin (2021), but, whenever apt, meandering into selected papers to deepen additional aspects. A major strategy will be to develop analytical methods for certain limiting cases of neural network architectures (linear, vanishing width/depth ratio) in order to gain insights about properties of real networks that are "close" to such cases. The developed formalisms draw heavily on methods from theoretical physics developed to connect microscopic and macroscopic scale descriptions of multiparticle systems and, thereby, expose interesting analogies between the behavior of deep networks and physical phenomena in multiparticle systems.
Prerequisites are a solid background in linear algebra, multivariate calculus and rudimentary probability theory.
Requirements for participation, required level
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Prerequisites are a solid background in linear algebra, multivariate calculus and rudimentary probability theory.
Teaching staff
Dates ( Calendar view )
| Frequency | Weekday | Time | Format / Place | Period | |
|---|---|---|---|---|---|
| weekly | Do | 14-16 | ON SITE & ONLINE X-E0-205 | 10.10.2022-03.02.2023
not on: 12/8/22 / 12/29/22 / 1/5/23 |
hybrid form |
Subject assignments
| Module | Course | Requirements | |
|---|---|---|---|
| 39-M-Inf-VML Vertiefung Maschinelles Lernen | Vertiefung Maschinelles Lernen | Student information |
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.
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- Last update basic details/teaching staff:
- Monday, June 26, 2023
- Last update times:
- Thursday, October 20, 2022
- Last update rooms:
- Thursday, October 20, 2022
- Type(s) / SWS (hours per week per semester)
- lecture (V) / 2
- Language
- This lecture is taught in english
- Department
- Faculty of Technology
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