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 Lehrpreisträger/-in  
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.

Literature

Scientific papers (to be announced at the course)

Prerequisites

Data Mining (recommended)

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:

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