392002 Übungen zu Grundlagen künstlicher Kognition II (Ü) (SoSe 2016)
Contents, comment
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Es werden verschiedene Probleme des maschinellen Lernens und der Neuroinformatik formalisiert und unterschiedliche Algorithmen eingeführt, die Beispielprobleme in konkreten Situationen algorithmisch lösen können. Themen sind etwa: Klassifikation und Regression durch Lazy learning, lineare Verfahren, Perzeptron/Adatron, Bayes-Klassifikator, zeitliche Aspekte behandelt durch MDPs, unüberwachte Verfahren wie Oja/Sanger, Fuzzy-Clustering, Grundlagen der Lerntheorie.
Teaching staff
Dates ( Calendar view )
| Frequency | Weekday | Time | Format / Place | Period | |
|---|---|---|---|---|---|
| weekly | Mi | 10-12 | C01-230 | 11.04.-22.07.2016
not on: 5/4/16 / 7/13/16 |
einmalig am 13.07.2016 in C01-136 |
| weekly | Do | 10-12 | C01-142 | 11.04.-22.07.2016
not on: 5/5/16 / 5/26/16 |
|
| weekly | Do | 14-16 | V4-112 | 11.04.-22.07.2016
not on: 5/5/16 / 5/26/16 |
|
| weekly | Fr | 12-14 | U2-135 | 11.04.-22.07.2016
not on: 5/6/16 |
|
| one-time | Mi | 10:00-12:00 | C01-136 | 13.07.2016 | einmalig am 13.07.2016 in C01-136 |
Subject assignments
| Module | Course | Requirements | |
|---|---|---|---|
| 39-Inf-13 Grundlagen künstlicher Kognition | Grundlagen künstlicher Kognition II | Ungraded examination
|
Student information |
| 39-Inf-13_a Grundlagen künstlicher Kognition | Grundlagen künstlicher Kognition II | 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.
- Registered number: 70
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- SS2016_392002@ekvv.uni-bielefeld.de
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- 17 Students to be reached directly via email
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- Last update basic details/teaching staff:
- Friday, December 11, 2015
- Last update times:
- Friday, April 22, 2016
- Last update rooms:
- Friday, April 22, 2016
- Type(s) / SWS (hours per week per semester)
- exercise (Ü) / 2
- Department
- Faculty of Technology
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