392246 Modern Data Analysis (V) (WiSe 2026/2027)

Contents, comment

In der Vorlesung werden einige neue Datenanalysetools vorgestellt, die im Gegensatz zu klassischen Verfahren auf wenigen Annahmen über die Daten beruhen. Ein schwerpunkt liegt auf Methoden für komplexe Datenstrukturen. Behandelt werden unter anderem folgende Themen:
- spärliche Repräsentation von Daten
- moderne Regressionsverfahren
- Core Methoden
- Featureextraktion und Metriklernen
- Ausreissererkennung
- moderne Zeitreihenverarbeitung
- Integration funktioneller Abhängigkeiten der Daten
- Anwendungen etwa in der Biomedizin

Requirements for participation, required level

Grundkenntnisse im Bereich Algorithmen und Datenstrukturen sowie Mathematik werden empfohlen

Teaching staff

Dates ( Calendar view )

Frequency Weekday Time Format / Place Period  
weekly Mo 12-14   12.10.2026-05.02.2027

Subject assignments

Module Course Requirements  
39-M-Inf-AI Artificial Intelligence (Basis) Einführende Vorlesung Student information
- Graded examination Student information
39-M-Inf-AI-adv-foc Advanced Artificial Intelligence (focus) Advanced Artificial Intelligence (focus): introductory Lecture Graded examination
Student information
Advanced Artificial Intelligence (focus): in-depth Lecture Student information
- Graded examination Student information
39-M-Inf-AI-adv_a Advanced Artificial Intelligence Advanced Artificial Intelligence: Lecture 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_a Applied Artificial Intelligence (focus) - Graded examination Student information
39-M-Inf-AI-x Artificial Intelligence (Focus) Einführende Veranstaltung Seminar o. 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 oder Klausur (3LP)

No eLearning offering available
Registered number: 8
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.
Limitation of the number of participants:
Limited number of participants: 50
Address:
WS2026_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_749881039@ekvv.uni-bielefeld.de
Notes:
Additional notes on the electronic mailing lists
Last update basic details/teaching staff:
Tuesday, June 30, 2026 
Last update times:
Tuesday, June 30, 2026 
Last update rooms:
Tuesday, June 30, 2026 
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=749881039
Send page to mobile
Click to open QR code
Scan QR code: Enlarge QR code
ID
749881039