t.BA.WI.STDM.19HS (Statistical Data Mining) 
Module: Statistical Data Mining
This information was generated on: 27 September 2021
No.
t.BA.WI.STDM.19HS
Title
Statistical Data Mining
Organised by
T IDP
Credits
4

Description

Version: 1.0 start 01 February 2020
 

Short description

The module introduces the basic principles of statistical data mining. Methods from supervised and unsupervised learning are dealt with and applied to specific case studies.

Module coordinator

Helmut Grabner

Learning objectives (competencies)

Objectives Competences Taxonomy levels
You know the basics of the data mining process. F K1, K2
You are familiar with unsupervised learning methods and their most important properties. You can see what problems they are suitable for and use them to solve new problems. F, M K1, K2, K3
You are familiar with supervised learning methods and their most important properties. You can see what problems they are suitable for and use them to solve new problems. F, M K1, K2, K3
You can implement and interpret data mining methods for specific tasks in a programming language (e.g. R or python) with the help of software packages. F, M K4, K5

Module contents

Unsupervised learning

  • Similarity- and distance measurements, outlier detection
  • ​A selection of typical methods for data reduction, such as Principal Component Analysis (PCA), Multidimensional scaling, t-SNE 
  • A selection of typical and modern clustering methods, such as k-means clustering, hierarchical clustering

Supervised learning

  • Basics, model selection, cross-validation
  • Performance evaluation for classification tasks
  • A selection of typical and modern methods such as Bayes Classifier, Nearest Neighbor Classifier (NN), k-NN, Support Vector Machines, Logistic Regression, Decision Trees und Random Forest
  • Ensemble methods (Bagging and Boosting)

Teaching materials

  • Slides
  • Handouts and/ or jupyter notebooks for the exercises

Supplementary literature

  • Duda, Hart, Stork, Pattern Classification, 2001, 2nd Edition
  • ​James, Witten, Hastie and Tibshirani, An Introduction to Statistical Learning, 2013

Prerequisites

WaSt1 - WaSt3

Teaching language

(X) German ( ) English

Part of International Profile

( ) Yes (X) No

Module structure

Type 2a
  For more details please click on this link: T_CL_Modulauspraegungen_SM2025

Exams

Description Type Form Scope Grade Weighting
Graded assignments during teaching semester Multiple homeworks during the term     Grading max. 40 %
End-of-semester exam Final exam written 90 min. Grading min. 60 %

Remarks

The lecturer communicates the exact procedure for the exams in writing at the beginning of the term.

Legal basis

The module description is part of the legal basis in addition to the general academic regulations. It is binding. During the first week of the semester a written and communicated supplement can specify the module description in more detail.
Course: Statistisches Data Mining - Vorlesung
No.
t.BA.WI.STDM.19HS.V
Title
Statistisches Data Mining - Vorlesung

Note

  • No module description is available in the system for the cut-off date of 01 August 2099.