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t.BA.WI.STDM.19HS (Statistical Data Mining)
Module: Statistical Data Mining
This information was generated on: 29 March 2024
No.
t.BA.WI.STDM.19HS
Title
Statistical Data Mining
Organised by
T IDP
Credits
4
Description
Version: 2.0 start 01 August 2024
Short description
The module introduces the basic principles of statistical data mining/machine learning. Methods from supervised and unsupervised learning are covered and applied to specific case studies.
Module coordinator
Martin Frey
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, UMAP
A selection of well-known and modern clustering methods, such as k-means clustering, hierarchical clustering, density-based and model-based 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
Lecture notes
Worksheets with solutions
Supplementary literature
James, Witten, Hastie and Tibshirani, An Introduction to Statistical Learning, 2021 (Second Edition)
Prerequisites
ExpD, Wahr and GStat
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.
Note
Additional available versions:
1.0 start 01 February 2020
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.