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
Frequency | Weekday | Time | Format / Place | Period |
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Module | Course | Requirements | |
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39-M-Inf-AI-adv-foc Advanced Artificial Intelligence (focus) | Advanced Artificial Intelligence (focus): Vorlesung | Graded examination
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Student information |
39-M-Inf-AI-app Applied Artificial Intelligence | Applied Artificial Intelligence: Vorlesung | Student information | |
- | Graded examination | Student information | |
39-M-Inf-AI-app-foc Applied Artificial Intelligence (focus) | Applied Artificial Intelligence (focus): Vorlesung | Student information | |
- | Graded examination | Student information | |
39-M-Inf-DL Deep Learning | Deep Learning | Graded examination
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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.
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.
A corresponding course offer for this course already exists in the e-learning system. Teaching staff can store materials relating to teaching courses there: