The lecture introduces the basic concepts of deep learning, covering the basic architectures, generative models, attacks and defebses, XAI methods, and structure processing models. Popular applications such as for image recognition will be discussed. On a practical side,mostly PyTorchwill be used.
programming skills, mathematics, basics of machine learning or neural networks
Rhythmus | Tag | Uhrzeit | Format / Ort | Zeitraum |
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Modul | Veranstaltung | Leistungen | |
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39-M-Inf-AI-adv-foc Advanced Artificial Intelligence (focus) | Advanced Artificial Intelligence (focus): Vorlesung | benotete Prüfungsleistung
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Studieninformation |
39-M-Inf-AI-app Applied Artificial Intelligence | Applied Artificial Intelligence: Vorlesung | Studieninformation | |
- | benotete Prüfungsleistung | Studieninformation | |
39-M-Inf-AI-app-foc Applied Artificial Intelligence (focus) | Applied Artificial Intelligence (focus): Vorlesung | Studieninformation | |
- | benotete Prüfungsleistung | Studieninformation | |
39-M-Inf-DL Deep Learning | Deep Learning | benotete Prüfungsleistung
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Studieninformation |
Die verbindlichen Modulbeschreibungen enthalten weitere Informationen, auch zu den "Leistungen" und ihren Anforderungen. Sind mehrere "Leistungsformen" möglich, entscheiden die jeweiligen Lehrenden darüber.
Students are able distinguish different key architectures for deep learning, and they know the learning scenarios where these architectures can be used. The students know how to train a deep architecture, both, as regards the algorithmic pipeline and possible forms of implementation using modern systems.
Zu dieser Veranstaltung existiert ein Lernraum im E-Learning System. Lehrende können dort Materialien zu dieser Lehrveranstaltung bereitstellen: