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Logo: Institut für Verteilte Systeme - Fachgebiet Wissensbasierte Systeme (KBS)
Logo Leibniz Universität Hannover
Logo: Institut für Verteilte Systeme - Fachgebiet Wissensbasierte Systeme (KBS)
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Data Mining II

Overview

In Data Mining I, basic data mining/ machine learning tasks and techniques are introduced. However, the volume, variety, velocity and veracity of the data generated by modern applications introduces challenges which go beyond those techniques.

The focus of this course is on the challenges introduced due to the modern data characteristics and on methods and techniques for mining large complex datasets. This includes both adaptation of old techniques to deal with modern data challenges but also new methods and techniques that were explicitly introduced for such sort of data.

In this course we will cover advanced data mining/ machine learning techniques to handle large data volumes, volatile data streams and complex object descriptions. These topics cover the Volume, Velocity and Variety aspects of Big Data and comprise major challenges for big data analytics.

ECTS points: 5

 

Course content

  • Big data challenges for data mining/ machine learning
  • Mining over high dimensional data (feature selection, dimensionality reduction, high-dimensional clustering)
  • Mining over large object cardinalities (parallel-, distributed-mining, summarization and sampling)
  • Mining over data streams (stream classification, stream clustering, change detection)
  • Multi-view (ensembles) and Multi-instance learning
  • Semi-supervised learning

Schedule

  • Lecture: every Tuesday 13:15 - 14:45, starting 17.10.2017
  • Tutorials: every Thursday, 14:15-15:45
  • Room: 023 (Building 3703), Appelstraße 4, 30167 Hannover

Teaching team

    • Responsible Professor: Prof. Dr. Eirini Ntoutsi
    • Teaching Assistants: Vasileios Iosifidis, Damianos Melidis

     !!! Please check Stud.IP for announcements, material and up-to-date information on the course.

    !!! For live streaming use this link.

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