Every winter semester
5 Credit points
For information on the duration of the modul, refer to the courses of study in which the module is used.
Non-official translation of the module descriptions. Only the German version is legally binding.
Students are able to 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 with regards to the algorithmic pipeline and possible forms of implementation using modern systems. The module consists of lectures and exercise classes and includes an exam at the end of the term.
The module introduces the basic concepts of deep learning, in particular different popular architectures including restricted Boltzmann machines, convolutional neural networks, stacked autoencoders (as concerns feedforward models), and long short term memory models and recursive networks (as concerns recurrent models). Different tricks for training are introduced including optimiziation schemes such as ADAM and training modi such as drop-out and training set enhancement. Popular applications such as for image recognition will be discussed. On a practical side, mostly TensorFlow will be used with Keras as API.
Programming knowledge (Python or a similar language), Foundations of mathematics, foundations of machine learning (e.g., neural networks or introduction to machine learning)
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Module structure: 1 bPr 1
Portfolio consisting of per default weekly exercises or programming tasks and final written exam (per default 60 minutes) or final oral exam (per default 15 minutes). The exercises are based on the content of the lecture and enable students to train and further investigate the topics. It is required that a sufficient percentage of the exercises are successfully completed (per default 50% of the total number of points which can be achieved during a semester). The final oral exam concerns both, the content of the lecture as well as the exercises.
| Degree programme | Profile | Recommended start 3 | Duration | Mandatory option 4 |
|---|---|---|---|---|
| Bioinformatics and Genome Research / Master of Science [FsB vom 30.09.2016 mit Änderungen vom 15.09.2017, 02.05.2018, 04.06.2020 und 31.03.2023] | 1. o. 2. | one semester | Compulsory optional subject | |
| BioMechatronics / Master of Science [Studien- und Prüfungsordnung vom 22.12.2022] | 1. o. 2. o. 3. | one semester | Compulsory optional subject | |
| Data Science / Master of Science [FsB vom 06.04.2018 mit Änderungen vom 01.07.2019, 02.03.2020, 21.03.2023 und 10.12.2024] | Variante 1 | 3. | one semester | Compulsory optional subject |
| Data Science / Master of Science [FsB vom 06.04.2018 mit Änderungen vom 01.07.2019, 02.03.2020, 21.03.2023 und 10.12.2024] | Variante 2 | 3. | one semester | Compulsory optional subject |
| Intelligent Systems / Master of Science [FsB vom 27.07.2018 mit Änderung vom 04.06.2020] | 1. o. 2. o. 3. | one semester | Compulsory optional subject | |
| Informatics for the Natural Sciences / Master of Science [FsB vom 30.09.2016 mit Berichtigung vom 10.01.2017 und Änderungen vom 15.09.2017, 02.05.2018, 04.06.2020 und 31.03.2023] | 1. o. 2. o. 3. | one semester | Compulsory optional subject |
The system can perform an automatic check for completeness for this module.