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Institute of Information Systems

Instituteof Information
Systems

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Artificial Intelligence in Business Administration

Type and Deduction (in German only):

  • main focus Business Analytics in BA MSc.

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 am -2:00 pm, VMP 9, A514

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!).

The course Artificial Intelligence in Business Administration focuses on the practical application of machine learning methods to business management issues. Using various case studies, different machine learning models are applied and the results analyzed.
The course covers the individual tasks involved in solving a machine learning problem, with the lecture focusing on the phases of data preparation and processing, modeling and evaluation of results, and practical application in business administration.
The lecture covers classic models for regression and classification on the one hand, and deep learning models for natural language processing and computer vision on the other.
No special programming skills are required. However, we will be programming with Python during the lecture, for which a brief introduction will be provided.
The following topics will be covered in detail:

  • Business analytics and artificial intelligence
  • Procedural models for solving machine learning problems
  • Introduction to the Python programming language and its use in machine learning
  • Regression and classification
  • Large language models
    • Embedding
    • Transformer models
    • Applications in business administration and evaluation
  • Computer vision
    • Areas of application
    • Convolutional neural networks for classification and object recognition

Amaratunga, T.: Understanding Large Language Models, Apress Berkeley, CA, 2023, https://doi.org/10.1007/979-8-8688-0017-7

Chopra, D., Khurana, R.: Introduction to Machine Learning with Python, 2023, https://doi.org/10.2174/97898151244221230101

Kordon, A. K.: Applying Data Science. How to Create Value with Artificial Intelligence, Springer Nature Switzerland, 2020.

McTear, M.; Ashurkina M.: Transforming Conversational AI, Apress Berkeley, CA, 2024, https://doi.org/10.1007/979-8-8688-0110-5

Qamar, U. , Raza, M.S. : Data Science Concepts and Techniques with Applications, Springer, 2023.

Shanthamallu, U.S., Spanias, A.: Machine and Deep Learning Algorithms and Applications, 2022, Springer.