Data Mining II - Advanced Topics in Data Mining

News:

Exercise 8 will be conducted on June 27, 2019

 

Timetable

DayTimeFrequencyPeriodRoomLecturerRemarksMax. participants
Vorlesung(V) - Lecture - Dates/Times/Location:
Tue. 15:00 bis 17:00 weekly G29-K059 (28 Plätze) Spiliopoulou Lehrpreisträger/-in  
Übung (Ü) - Exercise - Dates/Times/Location:
Thu.09:00 bis 11:00weeklyG22A-210 (24 Plätze) Tutor  20
Thu.17:00 bis 19:00weeklyG29-K059 (28 Plätze) 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

Homework option for DM II: Questions are here and here.

Exercise

 

 

Last Modification: 15.07.2019 - Contact Person:

Sie können eine Nachricht versenden an: Dr.-Ing. Tommy Hielscher
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