Data Mining II - Advanced Topics in Data Mining

News:

Announcement: Course materials for the semester will be distributed over Moodle. Please follow this link to see the content.

 

Timetable

DayTimeFrequencyPeriodRoomLecturerRemarksMax. participants
Vorlesung(V) - Lecture - Dates/Times/Location:
Tue.  bis  weekly   Spiliopoulou  
Übung (Ü) - Exercise - Dates/Times/Location:
Thu. bis weekly  Unnikrishnan  

Overview (from LSF)

Learning Content

In this course, we discuss advanced Data Mining methods for Data Science:

* Dealing with VELOCITY: methods for supervised, semi-supervised and unsupervised learning on data streams

* Dealing with VOLATILITY: learning and adaption on dynamic data

* Dealing with VOLUME: methods for learning on high-dimensional data

* VERACITY: incorporating expert knowledge into the learning process

From the applications' perspective, we focus on web applications and on applications from the domain of medical research.

Comments
New exam form: Elektronische schriftliche Prüfung (unbeaufsichtigt),
i.e. digital unsupervised open-book exam
Description
New exam form: Elektronische schriftliche Prüfung (unbeaufsichtigt),
i.e. digital unsupervised open-book exam
Literature

Scientific papers (to be announced at the course)

Remarks
New exam form: Elektronische schriftliche Prüfung (unbeaufsichtigt),
i.e. digital unsupervised open-book exam
Prerequisites

Data Mining (recommended)

Certificates
New exam form: Elektronische schriftliche Prüfung (unbeaufsichtigt),
i.e. digital unsupervised open-book exam
Target Group

WPF Master DKE

WPF Master Inf

WPF Master WIF

WPF Master CV

WPF Master IngInf

WPF Master Statistik

Description Data Mining II - Advanced Topics in Data Mining

 

 

 

Lecture

  • The lecture and exercises materials are distributed over Moodle. Click here to open the course page on Moodle.

Exercise

  • The lecture and exercises materials are distributed over Moodle. Click here to open the course page on Moodle.

 

 

Last Modification: 27.10.2020 - Contact Person: