Data Mining

Type and Deduction (in German only):

• im Schwerpunkt Business Analytics des Master Betriebswirtschaftslehre
  

Requirements

Basic knowledge about the essential data mining methods concerning custering, classification, and regression.

Language 

German

Scope of Deduction

6 LP
3 SWS

Lecturer
    
Dr. Kai Brüssau

Lecture Dates
   
Mo: 11:00-2:00 pm, WiWi 1005

Registration  

In order to participate in this course it is obligatory to register in STiNE during the STiNE registration periods.

Examination

Registration for the exams in STiNE well within the registration periods is mandatory (also for students who repeat the exam!).

This course focuses on the practical application of machine learning models for business management issues. With the help of various case studies, different methods are used and the results are analyzed.

The focus is on the question of how data is prepared and processed in machine learning before a model is created that maps the relationships in the data. The analysis of results is also of great importance, as is the use of these results in business practice. This covers various areas of business analytics.

Contents

• Artificial intelligence and machine learning
• Procedure models for solving machine learning problems
• Introduction to the Python programming language
  • Data storage and data access
  • Use for machine learning
• Data preparation and processing (correlation analyses, outliers, missing values)
• Regression analyses
  • Model building
  • Evaluation
  • Case study
• Classification
  • Modeling
  • Evaluation
  • Case study
• Feature selection
• Large language models
  • Embedding
  • Self-attention
  • Topic modeling
  • Evaluation
  • Case study

• Florin Gorunescu: Data Mining - Concepts, Models and Techniques, Springer, 2011, DOI: 10.1007/978-3-642-19721-5
• Charu C. Aggarwal: Data Mining - The Textbook, Springer, 2015, DOI: 10.1007/978-3-319-14142-8