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:
Mon. 13:00 bis 15:00 weekly 11.10.2021 to
24.01.2022
G29-307 (Verwaltung durch FIN, Pandemiebestuhlung: 42 Plätze) Spiliopoulou   60
Übung (Ü) - Exercise - Dates/Times/Location: Group 1
Thu.15:00 bis 17:00weekly14.10.2021 to
27.01.2022
G29-336 (30 Pl./12 Pl. CoV19) Spiliopoulou ,
Unnikrishnan
 20
Übung (Ü) - Exercise - Dates/Times/Location: Group 2
Tue.13:00 bis 15:00weekly12.10.2021 to
25.01.2022
G22A-128 (24 Pl.)  1
Tue.13:00 bis 15:00Singular eventat 19.10.2021G23-K12 (24 Pl.)  1

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
 
Description
 
Literature

Scientific papers (to be announced at the course)

Remarks
 
Prerequisites

Data Mining (recommended)

Certificates
 
Target Group

WPF Master DKE

WPF Master Inf

WPF Master WIF

WPF Master CV

WPF Master IngInf

WPF Master Statistik

 

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: 04.10.2021 - Contact Person:

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