392246 Modern Data Analysis (V) (WiSe 2023/2024)

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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

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Subject assignments

Module Course Requirements  
39-M-Inf-ADA Advanced Data Analysis Modern Data Analysis Student information
- Ungraded examination Graded examination Student information
39-M-Inf-AI-adv-foc_ver1 Advanced Artificial Intelligence (focus) Advanced Artificial Intelligence (focus): Vorlesung Graded examination
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

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.


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

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

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Registered number: 56
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WS2023_392246@ekvv.uni-bielefeld.de
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Last update basic details/teaching staff:
Monday, December 4, 2023 
Last update times:
Friday, January 5, 2024 
Last update rooms:
Friday, January 5, 2024 
Type(s) / SWS (hours per week per semester)
lecture (V) / 2
Language
This lecture is taught in english
Department
Faculty of Technology
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423430469