Deep Learning for Weather and Climate
(in German: Deep Learning for Weather and Climate - )
Module-ID: FIN-INF-999965 |
| Link: | LSF |
| Responsibility: | Prof. Dr. Christian Lessig |
| Lecturer: | Prof. Dr. Christian Lessig |
| Classes: | Blockseminar Deep Learning for Weather and Climate |
| Applicability in curriculum: | - M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. WIF: Informatik - M.Sc. DKE: Applied Data Science - M.Sc. DE: Methoden der Informatik - M.Sc. VC: Visual Computing - M.Sc. VC: Computer Science |
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Abbreviation DLWC |
Credit Points 5 |
Semester Summer |
Term ab 1. |
Duration 1 Semester |
Language english |
Level Master |
Intended learning outcomes:
1. kennen und verstehen spezialisierte Fachinhalte, die das Bachelor-Studium erweitern und vertiefen
4. können sich neues Wissen eigenständig aneignen und für sich nutzbar machen
8. können selbstständig wissenschaftlich arbeiten
9. können Wissen vermitteln, Ergebnisse erklären und Argumente verteidigen
Content:
- Fundamentals of Earth system modeling for weather and climate
- Implementation and presentation of simple case studies of how deep learning methods can help to better understand climate change
Workload:
56 contact hours + 124 h self study
| Type of examination: | Teaching method / lecture hours per week (SWS): |
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Presentation |
Zwei Termine:
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| Prerequisites according to examination regulations: | Recommended prerequisites: |
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keine |
Advanced course in deep learning and Fundamentals in scientific computing |
| Media: | Literature: |
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Comments:
Th seminar will take place in two blocks. Attendance in both is mandatory. In between the blocks, students are expected to work on projects and there will be individual online meetings. All relevant course work will done on May 17th.