392245 Maschinelles Lernen (S) (SoSe 2020)
Contents, comment
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Within the seminar, an overview about important facets of practically relevant Deep Neural Networks (DNNs) will be given in the form of recent original publications from the literature. Topics which will be covered include the following:
- efficient training of DNNs
- automatic hyperparameter optimization
- adversarial examples for DNNs
- generative adversarial networks and their efficient training
- deep reinforcement learning
- deep recurrent and recursive networks
- neural Turing machines
- applications for language translation
- applications for tracking in vision
- applications for privacy preserving data storage
Requirements for participation, required level
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Knowledge of basic math and computer science is required. Some knowledge about machine learning might be benefitial for some of the topics.
Bibliography
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The articles covered in the seminar are available in the Lernraum / Dokumentenablage.
Further reading is available on the internet such as:
- the book: http://www.deeplearningbook.org/
- another very gentle introductory book: http://neuralnetworksanddeeplearning.com/
- collection of material: http://deeplearning.net/
- link to Andrew Ngs courses: https://www.deeplearning.ai/
- short introduction: https://machinelearningmastery.com/what-is-deep-learning/
- ....
Typically, one of the standard frameworks are used in practical applications, such as tensoflow, theano, pytorch, keras (on top of theano/tensorflow) (all in python), caffe (C++), deeplearning4you (java). Python seems the dominant language, currently. These frameworks come with an embedded technology to train the networks on suitable GPU.
Teaching staff
Dates ( Calendar view )
| Frequency | Weekday | Time | Format / Place | Period |
|---|
Subject assignments
| Module | Course | Requirements | |
|---|---|---|---|
| 39-Inf-EGMI Ergänzungsmodul Informatik | vertiefendes Seminar 1 | Ungraded examination
|
Student information |
| vertiefendes Seminar 2 | Ungraded examination
|
Student information | |
| vertiefendes Seminar 3 | Ungraded examination
|
Student information | |
| vertiefendes Seminar 4 | Ungraded examination
|
Student information | |
| 39-Inf-MIKE Modularisierter individueller Kompetenz-Erwerb (MiKE) | - | Ungraded examination | 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 should present one of the topis of the seminar and they should actively take part in the discussions accompanying the other presetations. It is possible to extend the work towards a small project which can be counted e.g. as individual MSc project (5 CP).
Due to the current setting, the seminar will take place online only, likely via zoom, and the number of participants is limited.
- E-Learning Space
- E-Learning Space
- Registered number: 13
- This is the number of students having stored the course in their timetable. In brackets, you see the number of users registered via guest accounts.
- Address:
- SS2020_392245@ekvv.uni-bielefeld.de
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- 7 Students to be reached directly via email
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- Last update basic details/teaching staff:
- Thursday, January 9, 2020
- Last update times:
- Tuesday, February 11, 2020
- Last update rooms:
- Tuesday, February 11, 2020
- Type(s) / SWS (hours per week per semester)
- seminar (S) / 2
- Language
- This lecture is taught in english
- Department
- Faculty of Technology
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