Advanced Topics in Deep Learning (Master)
every
(in German: Advanced Topics in Deep Learning (Master) )
Module-ID: FIN-INF-120496 |
|
Abbreviation ATDL |
Credit Points 6 |
Semester every |
Term starting at 1. |
Duration 1 Semester |
Language english |
Level Master |
| Link: | LSF |
| Limited accessability: | |
| Responsibility: | Sebastian Stober |
| Lecturer: | Sebastian Stober |
| Classes: | Advanced Topics in Deep Learning |
| Applicability in curriculum: | - M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. WIF: Informatik - M.Sc. DKE: Learning Methods and Models for Data Science - M.Sc. DKE: Data Processing for Data Science - M.Sc. DKE: Applied Data Science - M.Sc. DE: Methoden der Informatik - M.Sc. VC: Computer Science |
Intended learning outcomes:
Students ...
- can read and analyze current research papers
- apply deep learning to an application domain
- gain in-depth knowledge In the application domain
- learn to work efficiently in a team on scientific problems
- learn to work on a server infrastructure
- present scientific results in a written and oral form
Content:
- Current topics from deep learning research
- Create domain-specific deep learning pipelines
- The application domain will be announced
beforehand
Workload:
- 30h in person
- 150h self-study
| Pre-examination requirements: | Type of examination: | Teaching method / lecture hours per week (SWS): |
|
Vorlesung/Seminar (2 SWS) |
| Prerequisites according to examination regulations: | Recommended prerequisites: |
|
none |
Introduction to Deep Learning |
| Media: | Literature: |
|
|
Comments: