11881 - Foundations of Data Mining Modulübersicht

Module Number: 11881
Module Title:Foundations of Data Mining
  Grundlagen des Data Mining
Department: Faculty 1 - Mathematics, Computer Science, Physics, Electrical Engineering and Information Technology
Responsible Staff Member:
  • Prof. Dr.-Ing. habil. Schmitt, Ingo
Language of Teaching / Examination:English
Duration:1 semester
Frequency of Offer: Each winter semester even year
Credits: 6
Learning Outcome:Acquaintance with the statistical and learning-theoretical foundations of knowledge extraction from large data sets; knowledge of specific notions and of mathematical background in order to understand current publications and software concerning the field; ability of transfer to concrete problems; knowledge of algorithms and their usage.
  • Foundation of statistics
  • Clustering (partition-based, density-based, hierarchical, ...)
  • Classification (decision trees, support vector machines, deep learning on convolution neural networks, ...)
  • Association rules (frequent itemsets)
  • further data mining approaches
Acquired knowledge will be applied within a project.
Recommended Prerequisites:Knowledge of the content of the modules
  • 11112 Mathematik IT-1 (Diskrete Mathematik)
  • 11113 Mathematik IT-2 (Lineare Algebra)
Mandatory Prerequisites:
  • No successful participation in module 12351 Grundlagen des Data Mining.
Forms of Teaching and Proportion:
  • Lecture / 2 Hours per Week per Semester
  • Exercise / 1 Hours per Week per Semester
  • Practical training / 1 Hours per Week per Semester
  • Self organised studies / 120 Hours
Teaching Materials and Literature:
  • James, Gareth; Witten, Daniela; Hastie, Trevor; Tibshirani, Robert: An Introduction to Statistical Learning with Applications in R. Springer, New York 2013.
  • Aloaydin, Ethem: Machine Learning. The MIT Press, Massachusetts Institute of Technology, 2004.
  • Mitchell, Tom M.: Machine Learning. McGraw-Hill, 1997.
Module Examination:Prerequisite + Final Module Examination (MAP)
Assessment Mode for Module Examination:Prerequisite:
  • Successful completion of project exercises in the course
Final module examination:
  • Written examination, 90 min. OR
  • Oral examination, 30-45 min. (with small number of participants)
In the first lecture it will be annunced, if the examination will offered in written or oral form.
Evaluation of Module Examination:Performance Verification – graded
Limited Number of Participants:None
Part of the Study Programme:
  • Master (research-oriented) / Artificial Intelligence / PO 2022
  • Abschluss im Ausland / Betriebswirtschaftslehre / keine PO
  • Abschluss im Ausland / Cyber Security / keine PO
  • Master (research-oriented) / Cyber Security / PO 2017
  • Master (research-oriented) / eBusiness / PO 2007
  • Abschluss im Ausland / Informatik / keine PO
  • Bachelor (research-oriented) / Informatik / PO 2008
  • Master (research-oriented) / Informations- und Medientechnik / PO 2017
  • Bachelor (research-oriented) / Mathematik / PO 2019
  • Abschluss im Ausland / Power Engineering / keine PO
  • Bachelor (research-oriented) / Wirtschaftsinformatik / PO 2024
  • Study programme Informatik B.Sc.: Compulsory elective module in complex „Grundlagen der Informatik“ (level 300)
  • Study programme eBusiness M.Sc.: Compulsory elective module in main focus „Entwicklung und Aufbau von eBusiness-Systemen"
  • Study programme Artificial Intelligence M.Sc.: Compulsory elective module in complex  „Knowledge Acquisition, Representation, and Processing“
  • Study programme Cyber Security M.Sc.: Compulsory elective module in complex „Computer Science“
  • Study programme Mathematik B.Sc.: Compulsory elective module in complex „Anwendungen“, field  „Informatik“
  • Study programme Wirtschaftsmathematik B.Sc.: Compulsory elective module in complex „Anwendungen“, field  „Informatik“

If there is no need that the module is taught in English, alternatively the german version 12351 „Grundlagen des Data Mining“ may be offered instead.

Module 11881 „Foundations of Data Mining“ and 12351 „Grundlagen des Data Mining“  can not be combined.

Module Components:
  • Lecture Foundations of Data Mining
  • Accompanying exercise with laboratory
  • Related examination
Components to be offered in the Current Semester: