392180 Maschinelles Lernen im Web (V) (WiSe 2022/2023)

Contents, comment

Eine ständig wachsende Informationsflut im Web legt es nahe, nach Möglichkeiten zu suchen, aus der
Fülle der Daten automatisch sinnvolle Informationen oder Modelle zu extrahieren. Dabei müssen
die Algorithmen in der Lage sein, auch mit großen Datenmengen realistisch umzugehen.
Die Vorlesung behandelt wichtige Techniken des Maschinellen Lernens mit Bedeutung für das Web,
die es erlauben, automatisiert Informationen aus Daten zu extrahieren.
Behandelte Themen im Einzelnen sind:
Graph-clustering,
nichtlineare Dimensionsreduktion,
Prototype-Verfahren (teils inkrementell).

The increasing availability of electronic information on the web requires novel technologies
to deal with these data. In particular, the algorithms have to deal with big and structured data.
In the lecture, a few topics of relevance in this realm will be tackled, including
graph clustering,
nolinear dimensionality reduction,
link analysis,
prototype-based models.

Kommentar/ Comments:

The lecture will be given in English if requested.

Requirements for participation, required level

Grundkenntnisse im Bereich Algorithmen und Datenstrukturen sowie Mathematik werden empfohlen

foundations in computer science and math, programming skills

Teaching staff

Dates ( Calendar view )

Frequency Weekday Time Format / Place Period  
block Block 9-13 CITEC 20.-24.02.2023

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Subject assignments

Module Course Requirements  
39-Inf-AKS Applications of Cognitive Systems Anwendungen Kognitiver Systeme Machine learning on the web or Modern Data Analysis or Soft Computing for Bioinformatics Ungraded examination
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 Klausur oder mündliche Prüfung (3LP)

exercises (2LP) and (oral/written) exam (3LP)

E-Learning Space
E-Learning Space
Registered number: 83
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: 30
Address:
WS2022_392180@ekvv.uni-bielefeld.de
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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_372280709@ekvv.uni-bielefeld.de
Notes:
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Last update basic details/teaching staff:
Friday, September 30, 2022 
Last update times:
Wednesday, February 15, 2023 
Last update rooms:
Wednesday, February 15, 2023 
Type(s) / SWS (hours per week per semester)
lecture (V) / 2
Department
Faculty of Technology
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372280709