392219 Maschinelles Lernen im Web (V) (WiSe 2014/2015)
Contents, comment
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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,
link-Analyse,
Streaming Algorithmen.
In Matlab-Übungen am Rechner direkt exemplarisch ausprobiert.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,
streaming algotithms.
The algorithms will be tested in the exercises based on Matlab code.Kommentar/ Comments:
The lecture will be given in English if requested.
The time slot for the lecture is not necessarily fixed, provided the majority prefers another time slot (such as Monday, 10-12; or, as an alternative, the lecture will be given as a block.)
Requirements for participation, required level
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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 |
|---|
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 | |
|---|---|---|---|---|---|---|---|
| Naturwissenschaftliche Informatik / Diplom | (Enrollment until SoSe 2004) | allgem.HS | HS |
Erfolgreiches Bearbeiten der Übungen (2LP) sowie mündliche Prüfung (3LP)
exercises (2LP) and exam (3LP)
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- Notes:
- Additional notes on the electronic mailing lists
- Last update basic details/teaching staff:
- Friday, December 11, 2015
- Last update times:
- Thursday, October 22, 2015
- Last update rooms:
- Monday, August 25, 2014
- 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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