392246 Modern Data Analysis (V) (WiSe 2013/2014)

Contents, comment

deutsch:

In der Vorlesung werden einige neue Datenanalysemethoden vorgestellt, die oft durch eine geschickte Modellierung zu überraschenden Ergebnissen führen. Die Methoden werden zum großen Teil, basierend auf existenten Implementationen im Netz, in den Übungen beispielhaft getestet. Konkrete Themen beinhalten die folgenden:
- Extraktion semantischer Entitäten aus Zeitreihen, ohne die Semantik zu kennen: Slow Feature Analsis
- Adaptive effiziente Kodierung von Daten durch Sparse Coding und Nichtnegative Matrixfaktorisierung
Die ein Pixel Kamera: compressed Sensing
- Wissen, welche Feature wichtig sind: die Support Feature Machine
- Klassifikation und co. in linearer Zeit: Core Methoden
- Ein Klassiker für Zeitreihen, und wie man daraus einen Kern macht: DTW und DTW Kern
- Prior über Funktionen statt Parametrisierung: Gaussian Processes
Die Themen sind weitgehend unabhängig voneinander und beleuchten verschiedene Facetten der Datenverarbeitung.

The lecture can/will be taught in English provided international students are present.

english:
In the lecture, novel data analysis methods will be presented which often yield surprising results by using clever modeling tricks. The methods will be tested in the exercises based on existing implementations on the web. Concrete topics which will be covered include the following:
- extraction of semantically meaningful entities from time series without any knowledge about the semantics: slow feature analysis
- adaptive efficient encoding of data by sparse coding and non negative matrix factorization
- the one pixel camera: compressed sensing
- how to know which features are relevant: support feature machine
- classification and similar in linear time based on core techniques
- a classical approach for time series and its kernel: dynamic time warping
- prior over function instead of parameterization: Gaussian processes

Requirements for participation, required level

Grundkenntnisse im Bereich Algorithmen und Datenstrukturen sowie Mathematik werden empfohlen

Basic knowledge as concerns computer science and mathematics is requested

Bibliography

wird in der Vorlesung bekannt gegeben

will be announced in the lecture

Teaching staff

Dates ( Calendar view )

Frequency Weekday Time Format / Place Period  

Show passed dates >>

Subject assignments

Module Course Requirements  
39-Inf-AKS Anwendungen Kognitiver Systeme Maschinelles Lernen im Web oder Modern Data Analysis oder Softcomputing für die Bioinformatik Graded 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.

Degree programme/academic programme Validity Variant Subdivision Status Semester LP  
Bioinformatik und Genomforschung / Bachelor (Enrollment until SoSe 2011) Anwendungen Kognitiver Wahlpflicht 3. 5. 3 benotet /unbenotet  
Informatik / Bachelor (Enrollment until SoSe 2011) Nebenfach Anwendungen Kognitiver Wahlpflicht 3. 5. 3 benotet /unbenotet  
Intelligente Systeme / Master (Enrollment until SoSe 2012) Anwendungen Kognitiver Wahlpflicht 1. 3. 3 benotet /unbenotet  
Kognitive Informatik / Bachelor (Enrollment until SoSe 2011) Anwendungen Kognitiver Wahlpflicht 3. 5. 3 benotet /unbenotet  
Medieninformatik und Gestaltung / Bachelor (Enrollment until SoSe 2011) Anwendungen Kognitiver Wahlpflicht 3. 3 benotet /unbenotet  
Naturwissenschaftliche Informatik / Bachelor (Enrollment until SoSe 2011) Anwendungen Kognitiver Wahlpflicht 3. 5. 3 benotet /unbenotet  
Naturwissenschaftliche Informatik / Diplom (Enrollment until SoSe 2004) allgem.HS   HS
Naturwissenschaftliche Informatik / Master (Enrollment until SoSe 2012) Anwendungen Kognitiver Wahlpflicht 3. 5. 3 benotet /unbenotet  

Erfolgreiches Bearbeiten der Übungen (2LP) sowie mündliche Prüfung (3LP)

Biweekly homework (2LP) and final oral exam (3LP)

No eLearning offering available
Address:
WS2013_392246@ekvv.uni-bielefeld.de
This address can be used by teaching staff, their secretary's offices as well as the individuals in charge of course data maintenance to send emails to the course participants. IMPORTANT: All sent emails must be activated. Wait for the activation email and follow the instructions given there.
If the reference number is used for several courses in the course of the semester, use the following alternative address to reach the participants of exactly this: VST_38018920@ekvv.uni-bielefeld.de
Notes:
Additional notes on the electronic mailing lists
Last update basic details/teaching staff:
Friday, December 11, 2015 
Last update times:
Thursday, September 19, 2013 
Last update rooms:
Thursday, September 19, 2013 
Type(s) / SWS (hours per week per semester)
lecture (V) / 2
Language
This lecture is taught in english
Department
Faculty of Technology
Questions or corrections?
Questions or correction requests for this course?
Planning support
Clashing dates for this course
Links to this course
If you want to set links to this course page, please use one of the following links. Do not use the link shown in your browser!
The following link includes the course ID and is always unique:
https://ekvv.uni-bielefeld.de/kvv_publ/publ/vd?id=38018920
Send page to mobile
Click to open QR code
Scan QR code: Enlarge QR code
ID
38018920