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Knowledge Discovery in Databases II (SS 2018)

News

Two recaps next week: Monday at 14:00 and Tuesday at 14:00.

The 1st exam will be on 13th July, 12-14, B101/HGB.

Organisation

  • Umfang: 3+2 Semesterwochenstunden
  • Required: Lecture "Knowledge Discovery in Databases I" or equivalent
  • ECTS: 6

 

Time and Locations

Component When Where Starts at
Lecture Wed, 9,00 - 12,00 h Room B U101 (Oettingenstr. 67) 11.04.2018
Tutorial 1 Mon, 14,00 - 16,00 h Room M 001 (HGB) 16.04.2018
Tutorial 2 Mon, 16,00 - 18,00 h Room M 001 (HGB) 16.04.2018
Tutorial 3 Tue, 14,00 - 16,00 h Room A 120 (HGB) 17.04.2018
Tutorial 4 Tue, 16,00 - 18,00 h  Room A 120 (HGB) 17.04.2018

Time and Locations

LectureTutorial
DateTopicDateExerciseMaterial
11.04.18 Introduction
18.04.18 High Dimensional Data (Intro, Curse of Dimensionality 23/24.04.18 ex1 optics-data.zip ArffGen.zip sol1.pdf
25.04.18 High Dimensional Data (Feature Selection) ex2 ex2.zip feature_func
02.05.18 High Dimensional Data (Dimensionality Reduction) 07/08.05.18 ex3
09.05.18 Guest Lecture: Dr. Gerhard Rolletschek, Glanos GmbH 14/15.05.18 ex4 kpca.csv
16.05.18 High Dimensional Data (Clustering)
23.05.18 High Dimensional Data (Clustering - cont.)
Achtung: Beginn: 10:15 Uhr
28/29.05.18 ex5
30.05.18 Guest Lecture: Process Mining
06.06.18 Data Streams (Clustering) 11/12.06.18 ex6 ex6.1_solution
13.06.18 Data Streams (Classification) 18/19.06.18 ex7
20.06.18 Sequence Data/Time Series 25/26.06.18 ex8
27.06.18 Time Series/Spatio-temporal Data 02/03.07.18 ex9
04.07.18 Graph Data 09/10.07.18 Recap


Content

In many modern application areas, data scientists face challenges which go beyond the basic techniques being introduced in the basic module Knowledge Discovery in Databases I. The module Knowledge Discovery in Databases II covers advanced techniques to handle large data volumes, volatile data streams, complex object descriptions and linked data. These topics are also known as the three major challenges (Volume, Velocity, Variety) in Big Data Analysis. The module is directed at master students being interested in developing and designing knowledge discovery processes for various types of applications. This includes the development of new data mining and data preprocessing methods as well as the ability to select the best suited established approach for a given practical challenge.