Deep Learning für Ingenieure
Winter
(engl. Deep Learning for Engineers )
Modulnummer: FIN-INF-110485 |
| Link zum LSF: | LSF |
| Verantwortung: | Sebastian Stober |
| Dozent:in: | Sebastian Stober |
| Lehrveranstaltungen: |
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| Verwendbarkeit: | - B.Sc. INF: Informatik - Wahlpflicht - B.Sc. INF: Studienprofil: Computer Games - B.Sc. INF: Studienprofil: Künstliche Intelligenz - B.Sc. CV: Informatik - Wahlpflicht - B.Sc. CV: Anwendungsfach - Computer Games - B.Sc. INGINF: Informatik - Wahlpflicht - B.Sc. WIF: Gestalten und Anwenden - Wahlpflicht - B.Sc. INF (bilingual): Informatik - Wahlpflicht |
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Kürzel DLFI |
CP 5 |
Semester Winter |
Fachsem. ab 3. |
Dauer 1 Semester |
Sprache deutsch |
Niveau Bachelor |
Angestrebte Lernergebnisse:
The students ...
- can confidently apply modern deep learning methods for different domains
- have the ability to follow current research in this area
- know the process of developing deep neural networks.
Inhalt:
Introduction to the learning process through back propagation. Essential model architectures such as MLP, RNN, CNN, Auto-Encoder and Transformer(Attention) are introduced and applied to various problems. The learning paradigms of supervised and unsupervised learning and their applications are taught. This also includes regularisations, advanced training methods and interpretation of learning curves and results
Arbeitsaufwand:
- 56h attendance time (lecture + exercise)
- 94h independent work (preparation and follow-up of lecture (OER) and exercise, working on exercise and programming tasks)
| Prüfungsvorleistungen: | Studien-/Prüfungsleistungen: | Lehrform / SWS: |
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Written exam 120 minutes
Announcement of the necessary preliminary work in the first week of the course.
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| Voraussetzungen nach Prüfungsordnung: | Empfohlene Voraussetzungen: |
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none
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Maschine Learning
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| Medienformen: | Literatur: |
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